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Journal of Automobile Engineering and Applications

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Journal of Automobile Engineering and Applications: 2455-3360(e) is a peer-reviewed hybrid open-access journal launched in 2014 focused on the rapid publication of fundamental research papers on all areas of Automobile Engineering and Applications. View Full Focus and Scope…

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4		journal	2.714 Q1	222	1792	3711	51847	28499	3699	6.71	28.93	27.39
5		journal	2.580 Q1	201	1484	3053	59257	29560	3035	9.00	39.93	26.20
6		journal	2.470 Q1	38	78	134	5507	1990	130	15.03	70.60	29.66
7		journal	2.469 Q1	57	513	204	19021	2703	202	14.68	37.08	25.35
8		journal	1.935 Q1	20	69	90	3153	577	90	5.19	45.70	20.62
9		journal	1.775 Q1	53	91	208	6033	1640	206	7.18	66.30	29.38
10		journal	1.553 Q1	152	322	659	14385	4197	657	6.19	44.67	20.67
11		journal	1.511 Q1	14	20	43	743	306	43	7.55	37.15	22.22
12		journal	1.493 Q1	28	74	74	3180	418	70	4.67	42.97	24.24
13		journal	1.484 Q1	45	229	597	13919	4401	593	4.87	60.78	36.43
14		journal	1.268 Q1	112	219	371	8359	2051	368	5.05	38.17	19.09
15		journal	1.262 Q1	118	223	690	13275	3434	687	4.22	59.53	40.05
16		journal	1.222 Q1	59	98	223	6417	1185	223	4.70	65.48	32.50
17		journal	1.119 Q1	12	69	123	3830	558	120	4.63	55.51	18.85
18		journal	1.106 Q1	43	46	176	2522	983	170	5.76	54.83	35.51
19		journal	1.091 Q1	58	124	271	4313	1174	236	4.40	34.78	24.57
20		journal	1.076 Q1	59	83	162	3689	729	159	4.02	44.45	31.83
21		journal	1.071 Q1	24	48	111	2083	649	103	5.51	43.40	19.39
22		journal	1.031 Q1	102	757	2281	35026	11239	2280	4.86	46.27	25.20
23		journal	0.951 Q1	31	79	88	3198	396	86	4.39	40.48	26.18
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25		journal	0.873 Q1	18	62	144	3110	779	143	5.37	50.16	19.21
26		journal	0.854 Q1	17	28	91	1694	425	89	5.03	60.50	21.88
27		journal	0.778 Q1	52	521	352	23759	2354	351	6.48	45.60	20.61
28		journal	0.763 Q1	41	41	101	2225	582	101	5.75	54.27	16.56
29		journal	0.700 Q1	66	317	497	13601	1818	492	3.40	42.91	13.60
30		journal	0.692 Q2	86	739	694	25624	2677	691	3.82	34.67	23.04
31		journal	0.660 Q2	16	20	70	742	338	70	5.18	37.10	28.24
32		journal	0.602 Q2	77	40	197	1473	325	191	1.38	36.83	21.70
33		journal	0.592 Q2	11	19	35	437	162	35	5.24	23.00	26.53
34		journal	0.582 Q2	62	226	1341	9791	3598	1338	2.36	43.32	34.42
35		journal	0.567 Q2	32	352	578	14065	2077	575	3.37	39.96	27.82
36		journal	0.541 Q2	24	60	177	1139	601	177	3.38	18.98	29.39
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42		journal	0.419 Q2	53	137	449	3939	874	449	1.81	28.75	22.03
43		journal	0.413 Q2	56	653	1798	27561	4150	1794	2.06	42.21	18.07
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46		journal	0.389 Q2	28	22	44	1015	113	41	1.59	46.14	17.72
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49		journal	0.347 Q2	13	20	60	605	96	59	1.39	30.25	27.12
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1 Motivating the Study on Change Management

2 state of the art of change management, 3 case study, 4 analysis of differences, 5 conclusions, conflict of interest, data availability statement, part change management: a case study on automotive engineering and production; domestic and international perspectives.

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Knackstedt, S., Sutton, M., and Summers, J. D.Part Change Management: A Case Study on Automotive Engineering and Production; Domestic and International Perspectives ASME Open J. Engineering ASME. January 2023 2 021012 doi: https://doi.org/10.1115/1.4056813

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Due to interest in aspects such as process, strategies, and tools of engineering changes expressed in a literature review, a case study was done on a major automotive original equipment manufacturer (OEM) to assess the perceived quality of its part engineering change management process and supporting system through its employees’ eyes. A combination of 12 interviews lasting 12 h and 46 written surveys was used to capture the views of participants from all major functions found at the research and development (R&D) headquarters of the OEM: Purchasing, Production, Development, and one group consisting of all other functions (“Other”). Statistical analysis was performed to identify statistically significant differences between employee perceptions of an engineering change management system among different departments, amount of use, and years of use. It was found that statistically significant differences exist in terms of understanding the usability of the system between different departments and also between different years of experience.

Changes to parts in the automotive industry come from two sources [ 1 ]: (1) creating innovative features that are implemented in existing vehicles and (2) managing existing features, where innovation is defined as “the multi-stage process whereby organizations transform ideas into new/improved products, service or processes, to advance, compete and differentiate themselves successfully in their marketplace” [ 2 ]. This definition is based on an analysis of word clustering among 60 definitions from 1934 to 2010 [ 2 ]. Managing these changes is increasingly important, not only as the rate of innovation increases [ 1 ] but also as companies continue to seek to improve quality and sustainability while meeting new regulations, often resulting in changes to existing parts [ 3 ]. A consumer survey conducted in 2008 showed that 86% of respondents list quality as the most important factor in buying a new vehicle [ 4 ]. The results of this survey show the importance of quality to the customer, explaining why many companies list quality concerns as a main source for part engineering changes [ 3 , 5 , 6 ]. Furthermore, the implementation of some engineering changes can lead to cost reductions for the company, which can have a significant impact on the company’s financial standing [ 7 ].

1.1 What is Part Engineering Change?.

To understand what is meant by P-EC (part engineering change), it is necessary to understand what is meant by parts in the context of this paper, the kind of changes part engineering change entails, and the sources of these changes. For the purpose of this paper, parts refer to any item that is identified on the Bill of Materials at the original equipment manufacturer (OEM). Additionally, different definitions of the term part engineering change management (P-ECM) exist due to the breadth of work that has been performed on this topic. Here, change is defined as “engineering change orders (ECOs)—changes to parts, drawings or software that have already been released” [ 8 ]. This definition is used in several other research efforts [ 5 , 9 ].

The reasons for engineering part changes can come from a variety of sources [ 3 , 10 ]. At the company that was the focus of this paper, data analysis shows the common sources include quality improvements, cost savings, safety concerns, legal considerations, and ergonomic changes benefitting the employee assembling the vehicle. Examples of each of the aforementioned engineering part change sources can be seen in Table 1 . The exact frequency is withheld here for proprietary reasons.

Examples of engineering part changes

P-EC Source	Example	Approximate percentage of all changes
Quality/product improvements	Smoothing the rough edge of a door handle	50
Cost savings	Removing the option of a painted component and replacing it with a mandatory black component	25
Safety concerns	Testing reveals long-term corrosion of battery leading to loss of power	10
Ergonomic changes	Preassembly of wiring harness to reduce the number of connections during vehicle assembly	10
Legal considerations	Addition of a sticker to meet export/import regulations	5

Examination of engineering change data at the automotive OEM showed that in a 20-month period in the middle of a product’s lifecycle, 1211 changes of all kinds were made. Quality and/or product improvement is the most frequent reason for a part engineering change and accounted for over half of the changes. Many changes are also due to cost savings. According to one interviewee, removing a painted component and replacing it with a black component can reduce vehicle costs by almost €1/vehicle. In an industry where some automobile manufacturers produce over 10 million vehicles annually, these savings make a significant difference to the company. These types of changes accounted for approximately a quarter of total changes. Another driver of change is safety concerns. A component will generally be changed as soon as possible if there is a safety concern [ 5 ]. For example, battery connections that can become corroded due to water and/or salt must be redesigned as these corrosions could lead to the loss of electrical power to safety-critical components such as the brakes. These types of changes are rare, but it was not possible to filter the available data for these types of changes. Less common are legal considerations and ergonomic changes. Legal changes can include changes to laws in countries around the world. For example, some Gulf States require a sticker on the vehicle that describes the fuel economy of the vehicle. While this is not strictly a part change because neither the windshield that bears the sticker nor the fuel system itself is affected, it is a change that needs to be made to the final vehicle before it is shipped. These types of changes are rare and account for less than 5% of the changes. To make the assembly of the vehicle more employee-friendly, ergonomic changes can also be made. One example of this is a preassembly of a wiring harness inside the vehicle. By preassembling this harness, only one connection has to be made once the harness has been installed in the vehicle. As these connections are frequently made in tight spaces, reducing the number of occurrences to one helps the associates. These types of changes are rare, but it was not possible to filter the available data for these types of changes

Other industries are also affected by similar sources, albeit not limited to those listed in Table 1 . In the defense industry, the most common sources as determined by an industry roundtable discussion are a change in requirements or regulations, quality/cost/durability, sustainability, product integration, and project management [ 3 ]. Ergonomic changes have been studied to increase employee output, especially in the furniture industry where carpal tunnel syndrome and tendinitis are more prevalent than in other industries [ 11 ]. Furthermore, it has been shown that legal considerations can account for roughly 20% of engineering changes in the civil engineering design community [ 7 , 12 ]. Regardless of industry, the reasoning behind making an engineering change is frequently the same. All changes require some combination of efforts such as time, cost, and risk. By making a change, the company hopes that the positive aspects of the change, such as improved cost or quality outweigh these negative aspects.

Engineering changes and their management (process and tools) in the industry have been reviewed from several different angles, such as time management and financial management [ 12 – 17 ], as well as P-ECM tool use. The process can be highly complex depending on the type of product and industry involved. To help with this complexity, companies have developed numerous in-house tools that track financial and temporal information [ 3 , 16 , 18 , 19 ]. Furthermore, these can be broken down into research regarding strategies, change propagation, process responsibilities, change drivers, other tools and findings as well as process descriptions.

1.2 Research Questions.

How do employees engage with the engineering change management process and tool?

If so, how do these issues align with differences between departments?

To understand the need for ECs and ECM processes, a review of the related literature was conducted. This included definitions of ECs, an explanation of change propagation and the potential impacts of the phenomenon, and trends in ECM research. Additionally, suggestions for improvements in ECM methods were explored. Lastly, a comparison of the different process models used in ECM studies was conducted.

2.1 Engineering Change.

The definition of engineering change differs slightly depending on the perspective of the research being conducted. Raffaeli et al. defined ECs as a series of changes that impact the components of a product after production has begun for that product [ 20 ]. Another study defined EC as an “alteration made to parts, from embodiment design stage through production stage, in its form or fit or function, drawings or software that has already been released” [ 10 , 21 ]. Knackstedt and Summers gave a similar definition in their study of part change management, which said that ECs were changes to parts, drawings, or software that had already been released [ 22 , 23 ]. Additional context for that definition was given, which stated that ECs are typically made for reasons of either quality and product improvement or to increase cost savings. The literature review conducted by Wright defined EC as a modification to a component of a product after the product is in production [ 24 ]. A commonly used explanation of ECs defined the concept as the changes or modifications in the forms, fits, materials, dimensions, or functions of a component after the product design is released [ 19 , 25 , 26 ]. Huang et al. further elaborated on the EC phenomenon, explaining that ECs typically induce a series of downstream changes that flow across companies and require multi-disciplinary teams to address the effects [ 25 ].

Engineering changes can be broken down into two categories: initiated changes and emergent changes [ 27 – 29 ]. Initiated changes stem from a response to altered customer requirements or from internal innovations in design. Some of the driving factors of initiated changes from the outset of a product design include customer requirements, from general capability statistics to specific equipment; certification requirements, including government codes and environmental standards; innovations, in materials or in processes; and problems identified from past designs. Other factors that can introduce initiated changes even later into the product design process involve new customer requirements, such as changing operation environments or marketplaces; recent innovations, which are more common for products with a long design period; and retrofits, common for products with a long life cycle [ 27 ]. Emergent changes are caused in response to problems occurring during the design process, or from weaknesses identified in the product. The aforementioned problems could occur during the initial part design, as some changes cannot be predicted by computer-aided design (CAD) systems, or during testing, prototyping, and manufacturing phases. Additionally, problems occurring during the use of the product could cause emergent changes as issues are addressed [ 27 ].

Traditionally, engineering changes have been considered a hindrance in the design and manufacturing process. In the literature review conducted by Wright, opinions on different aspects of the manufacturing process are presented [ 24 ]. The manufacturing perspective thinks of ECs as an evil that has the potential to disrupt production operations. Production control views ECs as a source of confusion that has the potential to upset a precarious balance of manufacturing resources, while inventory control sees ECs in terms of the cost of the scrap and rework that would potentially be produced. This sentiment is echoed by Huang et al. in their study of the ECM methods employed in Hong Kong manufacturers, as the companies involved found the management of ECs to be costly and time-consuming [ 25 ]. ECs were also found to have a noticeable negative effect on the delivery time of products, as well as less prominent but still adverse effects on product quality, day-to-day jobs, and workmanship. The article written by Jarratt et al. similarly found that ECs were considered to be a major source of problems in the part development process [ 29 ].

Despite these negative perceptions, ECs are increasingly seen as a way to keep up with the market and implement continuous improvement. Raffaeli et al. stated that ECs represent a key aspect of innovation, especially with regards to product families of standard parts or solutions, as ECs allow for more variants to be created in order to satisfy niche market demands [ 30 ]. Huang and Mak found support for that statement, in that companies gain significant market competitiveness if ECs are handled properly [ 19 ]. Similarly, Wright’s review of ECM literature showed that the incremental improvements made to products using ECs lead to vast leaps in innovation, as well as the fact that EC is a key process that allows companies to stay competitive in the market and effective in their product design [ 24 ]. Kidd and Thompson echoed this sentiment and summed up the new viewpoint toward ECs, saying that ECM has a vital role in all successful industrial organizations [ 31 ].

The new view on ECs is timely, as, across manufacturing industries, there has been a widespread shift in the approach to manufacturing, from mass production to mass customization [ 32 ]. This new approach has been embraced by manufacturers because of the need to make continuous improvements in order to create competitive products [ 31 ]. Mass customization is seen as the best way to satisfy individual needs through products while still turning a profit [ 33 ]. However, this approach introduces new issues to the traditional consideration of EC and change propagation. The greater connectivity that is created between systems using a mass customization, modular system architecture, or product family approach leads to a greater probability that changes will spread to other systems [ 27 ]. Eckert et al. also found that complex products with parts or systems that are tightly linked are more susceptible to change propagation [ 27 ]. Building on this, Raffaeli et al. found a need to extend EC methods in a way that allows for the evaluation of the impact that a change to a single product has on the overall product family [ 30 ].

Engineering change as it relates to manufacturing has been studied through mapping to general process models [ 34 ]. For instance, one model views three basic steps of a manufacturing change management process: before approval, during approval, and after approval [ 35 ]. This work was limited to looking at general phases and did not deeply study the roles of individuals, the timing, or the criticality of the proposed changes. An expansion of this included a cyclic model that required the manufacturing engineer to submit a proposal to the engineering team (the owner of the product, often the development team, but not always) that requires approval before implementation. A challenge identified with this approach is that the production is ongoing and that the change is either implemented immediately pending retraction or manufacturing waits for approval. This resulted in iterative revisions to proposals approved [ 36 ]. Another model focuses on identifying the need, selecting the solution, approving the change intent, approving and releasing updated documents, and communicating modifications. Between selecting the solution and approving the change, this process argues for a pause in production (a decrease in work), but additional steps are required between approving the change and releasing approved and updated documents when compared to the traditional process [ 37 ].

The sources for change in manufacturing can be from the production line, from quality control offices, supplier changes, design changes, or logistics modifications [ 10 , 34 ]. An example of design changes may take the form of introduction of new options or packages in a vehicle model. Option change management intersects both manufacturing and design [ 32 ]. While there are some differences between design engineering changes and manufacturing engineering changes, the processes for implementing these are similar. The main distinction tends to be with respect to the authority to implement the change and the timeliness associated with manufacturing changes during production.

2.2 Change Propagation.

The trickle-down nature of change is commonly referred to as change propagation; however, the definition of this term varies between researchers. Shankar et al. considered change propagation as the effects of changes that spread to other design sections [ 21 ]. Another study by Shankar et al. defined change propagation as the phenomenon by which one change sparks a series of other changes, which is typically studied by considering the linking parameters as the root cause of the spread [ 10 ]. Rouibah and Caskey found that stronger couplings between components increase the possibility that changes in one component will cause a change in another part [ 26 ]. An oft-cited explanation of change propagation defines it as the process by which a change to one part of an existing design or system configuration results in additional changes to the system, when those changes would not otherwise have been required [ 27 – 29 ]. Further breakdown of the phenomenon includes the types of behavior exhibited by components in response to a change, as well as the characterization of propagation types. When a change is introduced to a system or environment, components can act as constants, absorbers, multipliers, resistors, or reflectors. Propagation itself can be characterized as change ripples or change blossoms, if the changes finish on time differentiated by the size of the initial change effort, or as change avalanches, if the changes do not finish on time [ 27 – 29 ].

A study by Shankar et al. looked extensively into the reasons for change propagation [ 10 ]. In the study, 77% of all changes were internal changes, with the remaining 23% considered external changes. Internal changes were found to be caused by document error corrections, cost reduction exercises, manufacturing issues, design corrections, and inventory issues. External changes largely stemmed from changes to customer requirements. Further study broke down changes into propagated and initiated changes. The propagated changes were discovered to come from inventory issues, design error rectification, and manufacturing difficulties [ 10 ].

Further studies have explored methods of measuring and capturing trends in change propagation. Raffaeli et al. considered a method to measure the extent of change propagation and the linkages between changes and found that most of the existing work looks at linkages in the physical domain or in the functional domain [ 30 ]. However, few studies look at the connections between the physical and functional domains. With regards to capturing trends, Eckert et al. used a design structure matrix to show the connection network between trends and to look at the relationship between affecting and affected parameters [ 27 ]. Giffin et al. studied a data set of over 41,500 change requests and used graph theory to find change networks [ 28 ]. From this analysis, they found that the large change networks tend to coalesce based on nucleation in different parts of the system. Additionally, Giffin et al. constructed 1-, 2-, and 3-change motifs, which were used to categorize and characterize the trends in the change requests data. This data can then be used to determine a way to manage the extent of the engineering change propagation.

2.3 Engineering Change Management.

Engineering change management is defined as an activity in which the design change life cycle is monitored [ 31 ]. Rouibah and Caskey and Bouikni et al. go further in their definitions, stating that ECM is the process of making ECs in a planned, systematic manner that typically consists of engineering change requests and engineering change orders [ 26 , 38 ]. Many studies discussing ECs and ECM describe the steps for a successful ECM system. Kidd and Thompson mentioned a multi-stage cycle of change proposal, work order, and specific instructions, change implementation, and release approval for the ECM systems they reviewed [ 31 ]. Another procedure, consisting of identification, evaluation, implementation, and audits of ECs, was discussed in the study of ECM in UK manufacturing industries conducted by Huang and Mak [ 19 ]. Rouibah and Caskey posed an ECM system with four key steps: tracking change impact on product structure elements, identifying the people to be informed of the change, determining a sequence for informing the previously identified people, and executing approval and release of workflow with the participation of all involved parties [ 26 ]. A fourth set of steps was posited by Phelan et al. which consisted of the EC request, development of possible solutions, evaluation of change impacts, EC approval, implementation, and then review of the EC [ 32 ].

Although ECM systems are specialized to work with individual companies, there are some general trends. When first introduced, most ECM systems were paper-based and used the highly inefficient strategy of sequential review [ 19 , 25 , 29 , 31 ]. ECM systems then moved to a more computer-aided approach, in which the systems provided electronic documents that functioned in the same manner as traditional paper documents [ 19 , 25 , 26 ]. Once product data management (PDM) systems became available, many companies began integrating the ECM and PDM systems [ 26 , 31 ]. Huang and Mak determined from their research into the practice of ECM that most ECM systems are formally defined, with clear procedures, with most decisions and meetings involving both the design office and the industrial and production department [ 19 ]. Also identified were the barriers to using ECM efficiently: poor communication and late problem discovery, which leads to panicking and easy acceptance of quick fixes.

Across the literature reviewed, a common theme was the need for improvement to the ECM system and process. Thus, many of the reviewed articles discussed systems or tools. Shankar et al. proposed a seven-step verification validation and testing (VV&T) planning and computational tool, designed to guide a change engineer through the process and reduce human error by carrying over information between computational steps [ 21 ]. Rouibah and Caskey suggested a five-step parameter-based model for ECM, distinct from existing process- or document-dependent models, that would support communication in distributed engineering environments across company borders [ 26 ].

Kidd and Thompson discuss the in-house custom-designed software system used at British Aerospace (Military Aircraft Division) (BAe(MAD) and its three key advantages [ 31 ]. The BAe(MAD) system allows customers as well as the design and manufacturing departments to initiate design changes, notifies all affected departments or individuals of the change proposals, and requires those affected to give advice on the design change. The system also locks CAD files and only releases drawings when changes affect them, thus preventing documentation errors [ 31 ].

The study conducted by Huang and Mak on the ECM methods used in manufacturing industries in the UK found both tools and systems in use [ 19 ]. Commonly used systems include computer-aided design (CAD) and manufacturing (CAM) as well as material requirements planning (MRP) software. Many companies were also found to use design tools such as quality function deployment (QFD), failure modes and effects analysis (FMEA), design for manufacturing and assembly (DFM/DFA) principles, and value analysis in order to control ECs.

Bouikni et al. proposed a product feature evolution validation (PFEV) model that would support formal modification processes used for ECM after the initial product release [ 38 ]. The model would create a dynamic workflow that would offer the flexibility to efficiently handle the informal pre-release EC process, in addition to the formal post-release process. This would be done by incorporating the concepts of shared product features between disciplines, introducing an estimator of change, and generation of discipline- and task-specific information sets.

Other proposed models include the meta-product model that addresses both functional and modular part structures and product architectures [ 30 ]. Eckert et al. suggested modeling ECs using a problem-solution-evaluation loop, representing both a forward partial redesign process and a backward debugging process [ 27 ]. Shankar et al. created a model that would translate change interactions to a domain mapping matrix [ 10 ]. Similarly, Jarrett et al. proposed a change prediction method that would combine design structure matrices with risk management techniques [ 29 ].

Given the research questions proposed in the previous section, a case study was used to answer these questions. This section will give details on case studies, and data collection.

3.1 The Case Study Methodology.

A case study is an objective, in-depth look into the uncontrolled environment offered by companies in industry or universities in an academic setting [ 39 , 40 ]. It can be used for different reasons; providing a description, generating a theory, or testing a theory is among the most common [ 41 ].

A case study can focus on just one company or examine multiple companies to compare and contrast a situation. Within one case study, it is also possible to study processes specific to one company or processes that have become common throughout the entire industry [ 42 ]. Case Studies can use multiple research methods, such as interviews, surveys, and experiential analysis to add credibility to the research while helping to ensure the results are valid and accepted [ 39 , 40 , 43 ]. The case study described here used interviewing and a survey to generate data. An interview and survey approach to understanding the change management process for line-sustaining engineering was selected to provide a broader comparative understanding of organizational elements within the company. The views and perceptions of participants within the process are critical for understanding the rationale associated with any process deviations. This approach has been used for understanding other aspects of engineering design, such as the research culture [ 44 , 45 ], change management in automation and manufacturing [ 34 , 46 ], and capstone design effectiveness [ 47 , 48 ]. Alternative approaches in case study research in engineering design are more generally focused on document analysis, focusing on the objectivity of analyzing existing information rather than seeking to extract potential explanations or perceptions. Document analysis in the case study has been used to understand information loss [ 49 ], design tool use [ 50 , 51 ], or problem-solution detailing [ 52 – 54 ].

For this case study, an automotive OEM was identified that presented a potential to study both the difference between engineering and manufacturing groups in the organization as well as studying the general cultural differences between the groups. The manufacturing facility is located in the Southeast US, while the engineering and management offices are located in Europe. The development headquarters and engineering teams are responsible for design and development of three brands. The manufacturing facility is responsible for the assembly of four vehicle models for one of these brands. The development headquarters employs nearly 20,000 employees across all major functions, while the production plan employs approximately 9000 employees across manufacturing and production activities. Both organizations employ a company-specific permanent engineering change management system, while the production plant also employs internal systems for temporary engineering change management.

Further, this case study was developed to be conducted while the first author was employed at the organization during a graduate internship that spanned roughly nine months. Further, the first author is fluent in both languages that are commonly used within the organization. This allowed the research team to not only literally translate interview and survey information collected, but to also add contextual understanding as appropriate. Finally, the research team had extensive contacts within this organization that spanned multiple decades. This was crucial in that trust had been established through multiple mechanisms that helped encourage multiple interviewees and survey respondents to participate without hesitation or reservation.

While specific details of the engineering change examples are not provided here for proprietary concerns, an illustrative example is presented. The seatbelt receiver in the passenger front seat might be found to rub on the center console, creating minor scratches that could be viewed negatively by the end-user. Therefore, a solution is needed to quickly respond to this quality defect. Locally, the manufacturer could decide to use a soft, felt pad on a sticker to attach to the seatbelt receiver. This could be immediately implemented to address the problem (temporary change), but it would still need to be submitted as an engineering change notification to the engineering group. This change would follow the defined engineering change management process as it is a product deviation. Any changes to the steps for assembly or the tools used are not considered changes to the product and are therefore not submitted as part of the change management process unless the assembly steps or fixtures are explicitly defined as required by the engineering department.

3.2 Phase I: Interviews.

The research procedure for this case study consisted of interviews and surveys. The interview process will be outlined in the ensuing subsections and is followed by the results. The interviews were used primarily to narrow down employee responses to a list of issues and topics that could be used for conducting a survey. A sample of interview questions can be seen in Table 2 .

Sample interview questions

#	Question type	Question
1	P	Do you ever deviate from the process?
2	W	How much time do you lose on the inefficiencies we have discussed?
3	P	Are you notified when changes are made to an engineering change request that you have already submitted feedback on?

Note: P: Process and W: Workload.

The questions in Table 2 were selected because they point toward the targeted nature of questions that were asked during the interview. It was important to find out whether or not the official P-EC process is actually used or if there are deviations. This allows for a starting point in investigating problems. Question 2 was included for a similar reason- if users think about how much time they spend with the problems created by the process and software system, they may be more inclined to actively participate in the interview. The last question is an example of a question that was added to later revisions of the interview list based on previous interviews during which it was mentioned that there is a problem with the P-EC notification system. The final set of interview questions consisted of 26 questions.

3.2.1 Finding Interview Participants.

To identify the P-EC process and obtain details about the software system in use, a focal point was to ask individuals with varying degrees of system interaction and responsibilities in the process. Furthermore, weaknesses in the process and systems were discovered through this approach. A tracking system used by the company was searched to identify interview candidates. This system tracks participants, roles, and timing for all engineering changes for specific models over a period of time that can be set by the user. Other companies track similar information [ 55 ].

The search was narrowed to a period of January 1, 2014, to December 31, 2015, and models that are produced at one particular manufacturing plant in the United States. The time was narrowed to this period because it gave a large enough sample size to be able to segment participants into user frequency groups (UFG), while minimizing the number of people who have moved on to other functions or other employment opportunities. Additionally, interview representation from all major departments at the OEM was sought. These departments are development, purchasing, production, and a group called “other,” describing those employees who do not fall into the other three departments.

The users, defined as those employees who have access to the system and are requested to give feedback, were then divided into user frequency groups, each user being a high-, medium-, or low-volume user. For example, a user who has given feedback 40 times over the course of a year would be in user frequency group “High.” An overview of this categorization can be seen in Table 3 .

Definition of UFG

User frequency group	Use of system ( is the number of uses during a year)	Number of interviews
Low	< 10	3
Medium	10 ≤ < 30	3
High	≥ 30	6

3.2.2 Interview Execution.

The interviews were conducted in the native language of the interviewee. Each interview lasted between 30 and 60 min and was audio recorded after consent was obtained. The interviews were conducted in neutral locations, such as conference rooms or empty breakrooms. The interviewees were generally pleased that this study was being undertaken with comments such as “It’s about time someone looks at this system [translated]” and “throughput times that are pretty much completely pointless… with all the shadow lists that are being created off-line [translated]”. Further, the “low frequency” users were generally surprised to be included in the interview process.

3.2.3 Interview Results.

Results regarding the employees and the software system were gathered. All interviewees stated that their work requires face-to-face meeting time as well as phone conversations to complement the information that is in the P-ECM software system. None of the 15 who were interviewed had familiarity with other engineering change processes, as they had not worked at any other companies.

One difference among the interviewed associates is how much time it takes for them to work on a request, including data entry as well as meetings. This ranged between a few minutes and several hours, regardless of the department.

Additional differences came in the form of the final question, when associates were asked to give feedback on any other struggles they have with the process or the system. The process efficiency (“ The ineffable throughput times that are pretty much completely pointless… with all the shadow lists that are being created off-line, it’s really not efficient ” [translated]) and the quality of information (“ The quality of the information we receive isn’t always good ” [translated]), as well as the timing that is automatically generated for change requests, were the most frequently mentioned criticisms. Furthermore, the purchasing department had specific criticisms of the system and the workflow. Another interviewee was not pleased with getting requests that have no impact on him, saying, “ If somebody would just use their brain for once and think, ‘yeah that part number is affected but there is no change’ that would really help out ” [translated].

3.3 Phase II: Surveys.

To obtain additional data, a survey was used to receive more feedback. The following subsections detail the steps in creating the survey, finding survey participants, and distributing the survey.

3.3.1 Creating the Survey.

Based on the information gathered in the interviews, a survey consisting of 29 questions was created that was submitted to additional persons identified by the company tracking system. The survey was used to triangulate information obtained in the interviews. Prior to giving out the survey, it was tested on three individuals who had also completed interviews to ensure the questions reflected the current issues of the process and system and could be answered without needing clarification [ 56 ]. While the participants in the survey remained anonymous, the first section of the survey consisted of questions that made it possible to group the participant into different groups. For example, the participant was asked whether or not he still uses the engineering change system and how many years he has used the system.

The next section of the survey gave the participant the chance to indicate to which extent they agree with a statement that was made on the survey by marking it on the line. An example of this can be seen in Table 4 .

Excerpt from survey

I receive engineering change requests for topics that do not affect me

Disagree compl.

Agree compl.

Lastly, the users had three free response questions asking them if there are other issues with the system, other ways to improve the system, and how much time they could save with an improved system.

3.3.2 Finding Survey Participants.

The participants for the survey were found using the same approach as finding the participants for the interviews. It was based on the data management system that the OEM uses and was then filtered for those participants with the highest use of the engineering change management system. Fifty participants were sent the survey. The list was continually expanded, following the same selection protocol, until 46 individuals completed the survey.

3.3.3 Distributing the Survey.

Upon receiving the participant’s written consent, the survey was distributed in person, making it possible to personally thank the participant for his time. The survey distribution was completed for three weeks to account for the availability of all participants. The participants were asked to complete the survey in one week. The surveys were then collected as the participants finished them, also for three weeks.

3.4 Survey Findings.

In total, 46 surveys were handed out and completed. The answers to two of the introductory questions (department and the number of uses per week) can be seen in Table 5 .

Demographic data

Department	Number of participants	Number of uses/week	Number of participants
Development	17	0–2	8
Procurement	13	3–5	13
Production	10	6–10	16
Other	5	>10	7
N/A	1	N/A	2

In addition to the data shown in Table 5 , the participants reported an average of 6 years of use in the software system that guides the P-EC process. Furthermore, participants required on average approximately one hour to complete their task regarding an engineering change request. This number includes meeting times, working in the system, and ensuring that all information is correct. However, the range of values given for this question is between 5 min and 7 h. Employees working in development took longer than their counterparts in other departments did.

To analyze the data, a measurement of the mark that was made on the survey for each question was taken. The further to the right the mark is, the more strongly a participant agreed with the statement (see Table 4 ). The data were recorded in a spreadsheet and were grouped by department, use, and experience level of the participant. Initially, every question was grouped by department, and the average was taken. Additionally, a total average for every question was taken. The total average is first used as a baseline for which topics the participants felt most strongly about based on either high or low values on the scale from 0 to 8.0. The raw data can be seen graphically in Fig. 1 .

Plots of average survey responses

A review of Fig. 1 reveals that all questions have a standard deviation between 1.7 and 2.2 with the exception of the last question, which has a standard deviation of 1.3. The next step in the analysis is to look at differences between the departments, company experience, and uses per week. This is the topic of the next section.

Analysis of differences is completed on several different groups: departments, number of uses per week, and number of years of experience. To examine whether or not statistical differences between responses exist, a Mann–Whitney U-test was used. This test is explained in further detail in the next subsection.

4.1 Mann–Whitney U-Test.

A Mann–Whitney U-test is used because of its ability to handle non-normal, discrete, or continuous data [ 57 ]. To use this type of test, the data must exhibit characteristics and/or be exposed to the assumptions displayed in Table 6 .

Assumptions for Mann–Whitney U-test

Assumption	Assumption met?
At least one dependent, continuous variable	The survey responses are dependent on the survey participant
At least one independent variable	The participants are independent
Independence within the samples and mutual independence	No participants are in multiple departments and no participant is asked the same question repeatedly
The distribution for each data set has a different shape	The data sets do not exhibit the same shape

The nomenclature for these equations can be seen in Table 7 .

Nomenclature for Mann–Whitney U-test

Variable	Meaning
	Test value for data set 1
	Test value for data set 2
	Number of samples in data set 1
	Number of samples in data set 2
	Sum of ranks of data set 1
	Sum of ranks of data set 2

Two sets of data are compared using Eqs. (1) and (2) . The comparison follows on the basis of the number of samples in each data set as well as the data rank. For each comparison that is conducted, a value for U 1 and a value for U 2 are generated. The lower of the two U values is then compared to a table of values for this test, which gives a critical value based on the number of samples in each data set and the significance level, α . For the comparisons in this thesis, α = 0.05 was used because it is frequently used in studies where a particular result is expected [ 59 ].

The null hypothesis is the following: H 0 = there is no significant difference between the data sets. This hypothesis is rejected if the calculated value for either U 1 or U 2 is lower than the critical value obtained from the table (i.e., U 1 < U α =0.05 or U 2 < U α =0.05 ) [ 57 ]. The test was run using IBM SPSS Statistics 23.

4.2 Differences Between Departments.

Using the Mann–Whitney U-test, all of the statements presented can be analyzed for the significance of the differences in the data. The focus of the data analysis is on differences between the development department and the production department for the following reason: the development department most frequently generates an engineering change and therefore may exhibit bias toward any issues with the perceived quality of engineering changes, whereas the production department is generally the end-user/customer of the engineering change, and must implement changes. Conducting the test on these two departments reveals the following information, summarized in Table 8 . It is important to note that the significant results are influenced by the number of participants in each group. Since not all participants answered all questions, a different sample size occurred for some questions. The change in sample size leads to a change in the U critical .

Summary of significance in statement means for departments

7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25
ns	ns	ns	ns	ns	ns	ns	ns	s	ns	ns	s	ns	ns	ns	ns	s	s	ns

Note: s: significant, ns: not significant.

Statements 15, 18, 23, and 24 indicate significant differences between the development and the production department. To understand what can explain these differences, the statements are reproduced in Table 9 .

Restatement of statistically significant survey statements (department)

Statement number	Statement	Development response	Production response
15	Completing a feedback for an EC “ ” is difficult because information is missing	4.6	6.8
18	Other colleagues in my department get a feedback request for the same topics as me	4.0	6.3
23	Additional text entry fields in the EC interface would make it easier to immediately get relevant information	2.7	4.9
24	It would be helpful to receive a message when relevant information, in an EC that I have already submitted a feedback for, is changed	5.8	7.0

Statement 15 shows a difference because the production department may rely very heavily on the information that is in the engineering change and may be particularly weary of giving feedback since the members of the production department must ultimately implement changes.

The difference in statement 18 may stem from the fact that a creator of an engineering change needs the feedback of multiple production department employees while not needing as many feedbacks from his own department because he is solely responsible for a part.

The difference in answers to statement 23 may be explained by the fact that the development department, as the creator of the engineering change, does not want to fill out more information, while it would help the production department receive more information to provide informed feedback.

Statement 24 may also be attributed to these differences, though it should be noted that all departments felt strongly about this statement.

4.3 Differences Between the Number of Uses/Week.

The distinction of how many times the software is used per week is another comparison that can be made. For this comparison, those who give feedback on 10 or more changes per week are compared to those who give zero to two feedbacks per week. The results of this comparison can be seen in Table 10 .

Summary of significance in statement means for use

7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25
ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns

Table 10 summarizes that none of the differences between those participants who use the system zero to two times and more than 10 times are significant. While it may seem initially that the averages of those two groups, as seen in Table 10 , are very far apart on some questions, the small sample size of this comparison resulted in comparison to very low U crit values, which resulted in accepting the null hypothesis for every question and therefore accepting that there is no significant difference between the two data samples.

4.4 Differences Between the Years of Use.

Finally, the differences in terms of experience levels were analyzed. In this case, those participants who had used the system for at least 10 years were compared with those who were novices, having less than 2.5 years of experience. The results from this comparison can be seen in Table 11 .

Summary of significance in statement means for experience

7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25
ns	s	s	ns	ns	ns	s	ns	ns	ns	ns	s	ns	ns	ns	ns	ns	ns	ns

A review of Table 11 shows that Statements 8, 9, 13, and 18 indicate significant differences in responses based on experience level. To understand what can explain these differences, the questions are reproduced in Table 12 .

Restatement of statistically significant survey statements (experience)

Statement number	Statement	0–2.5 Year response	10 + Year response
8	The transparency of the EC process needs to be improved	6.1	3.7
9	The usability of the software system needs to be improved	6.1	4.6
13	I help colleagues who have little experience with the EC process and system	6.6	4.0
18	Other colleagues in my department get a feedback request for the same topics as me	4.8	3.3

The statements in Table 12 indicate that, when employees start using the engineering change software system, they feel that the transparency of the process and the usability of the software needs to be improved. It is therefore likely that the employee perception of usability and transparency increases the more they use it. Additionally, there is an expectedly large difference for statement 13 because new users, who are unfamiliar with the process and software, will most likely not help others in a similar position. The response to Statement 18 can be explained in a similar fashion. If the user is a novice user, it is more likely that others in the department will receive a feedback request on the same topic, to ensure that a more knowledgeable employee also knows of a potential change.

4.5 Shared Differences.

The data presented here indicate very few shared differences between the different groups that were compared. The only statement that created statistically significant differences for various groups was Statement 18, which appeared in the department comparison and the years of experience comparison.

Do the employees have any issues with the engineering change management process or tool?

Based on the information provided in Sec. 3 of this paper, the employees do have issues with the engineering change management process or tool. Examples include lacking quality in the engineering change feedback requests and lack of notifications when information is altered in the request. However, it must also be noted that overall, the question “does this software make your work harder?” was generally answered on the “disagree” end of the spectrum.

If so, can these issues be broken down into differences between departments?

These issues and many other statements can be broken down into statistically significant differences between departments and years of experience. Grouping by the number of uses per week did not reveal any statistically significant differences. A shared difference between departments and years of experience exists regarding other employees receiving feedback requests on the same topic. This may be an opportunity to cut costs for the OEM.

5.1 Generalizability.

While this is a case study that explores a single organization and its implemented engineering change management process for supporting line-sustaining engineering, the findings can be generalized and applied beyond this specific organization. First, automotive OEMs might have different approaches to product development, but they are constantly sharing best practices through exchange programs and research literature. These organizations are generally multinational with engineering in one country and products distributed internationally. Thus, the design-manufacturing division is often reinforced by multiple national cultures. The findings within this case study can have potential generalization to other automotive organizations of similar structure and configuration.

In addition to direct transferability to other automotive or other complex systems design and manufacturing organizations, there is the potential to generalize the findings that the process that is implemented to support a specific activity and a defined key performance indicator can be modified and corrupted without malicious intent through the introduction of shadow systems. This is something that should be incorporated into company process development as a consideration. While it is commonly suggested that key performance indicators are often used incorrectly, this case study presents evidence of this. At the same time, it presents an approach to help uncover these deficiencies through systematic analysis through interactions with employees.

Ultimately, the goal of this research is not to present a universal truth, but to add to the understanding of how engineering change management operates in industrial practice. This can help inform the future development of processes and supporting tools.

5.2 Study Limitations.

A limitation of this case study is that the data in the form of interviews and survey results were only analyzed on one product line at one automotive OEM. The perceived issues are only valid for the associates of the product line that were interviewed and could be different when interviewing associates who work on the engineering change request for a different product line within the same company.

5.3 Future Work.

Building off this research and its limitations, many other avenues can be pursued. Similar analysis involving more product lines at this OEM and/or other OEMs throughout the world. The differences between companies could give insights into best practices regarding software use and process flow. These best practices could then lead to cost savings across the industry. Furthermore, an increase in the number of survey participants would help strengthen the case made in this paper as well. Another opportunity to further research engineering change management deals with a detailed analysis of the impact of past engineering changes. Many engineering changes are presented as cost savings; investigating whether or not these cost savings were realized or not can be beneficial to an organization in handling engineering changes. This leads to the following research question: “What percentage of all part engineering changes have a positive long-term value?”

This work can be used as a framework for other industry case studies on manufacturing change management. For instance, this structured interview and survey approach has already been used in a study on “de-automating” aircraft assembly workstations [ 46 ]. Having a defined structure for how to conduct this research is critically important for supporting future studies.

There are no conflicts of interest. All procedures performed for studies involving human participants were in accordance with the ethical standards stated in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Informed consent was obtained from all participants. Documentation is provided upon request. Informed consent is not applicable. This article does not include any research in which animal participants were involved.

The data sets generated and supporting the findings of this article are obtainable from the corresponding author upon reasonable request.

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Reverse Engineering Technologies for Remanufacturing of Automotive Systems Communicating via CAN Bus

• Open access
• Published: 07 December 2011
• Volume 1 , article number  6 , ( 2011 )

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• Stefan Freiberger 1 ,
• Matthias Albrecht 1 &
• Josef Käufl 1  

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Nowadays, as mechatronic and electronic systems have found their way into vehicles, the technological knowledgebase of traditional remanufacturing companies erodes rapidly and even the industrial principle of remanufacturing is at risk. Due to the fact that modern cars incorporate up to 80 of these mechatronic and electronic systems that are communicating with each other e.g. via the vehicle controller area network (CAN), remanufacturing of these automotive systems requires innovative reverse engineering knowhow, methodological innovations and new technologies, especially focusing on the tasks testing and diagnostics of systems and their subassemblies. The European research project "CAN REMAN", conducted by Bayreuth University in cooperation with two other universities and eight industrial partners, focuses on these needs in order to enable companies to remanufacture modern automotive mechatronics and electronics with innovative reverse engineering skills as well as to develop appropriate and affordable testing and diagnostics technologies.

In order to operate and test the mechatronic device with CAN interface outside the vehicle environment, an appropriate simulation of the vehicle network and all connected sensors of the device under test (DUT) is essential. This implies an electrical analysis of the connectors of the DUT, a content-related analysis of the CAN-bus, a sensor hardware simulation and a CAN-bus simulation.

All electrical measurements and results were taken using conventional multimeters or oscilloscopes. The CAN-bus analysis and simulations were conducted using the Vector Informatics software tool "CANoe" (Version 7.1) and a suitable CAN-bus hardware, e.g. the CANcardXL and the IOcab8444opto. All hardware simulations were executed with a conventional wave form generator or a microcontroller evaluation board (Olimex AVR-CAN) and an appropriate electric setup.

In order to initially readout the failure memory and to investigate the diagnostic communication of the DUT, garage testers such as "Bosch KTS 650" or "Rosstech VAG-COM" were used.

The results of the project are application-orientated methods, test benches and skills for remanufacturing companies to find out the working principles of the CAN-bus communication between automotive mechatronic and electronic systems within vehicles.

The knowhow presented in this article enables remanufacturing companies to remanufacture modern automotive mechatronic and electronic systems which are communicating via the CAN-bus and similar communication types.

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1. Introduction

Raising requirements on occupant safety and comfort on the one hand and the introduction of new emission regulations on the other hand, forces the automotive manufacturers to enhance their products continuously. In order to achieve these improvements, electronic systems, based on microcontrollers, have found their way into modern cars and they contributed considerably to many new advantages in terms of safety and comfort such as Electronic Stability Program (ESP), Anti-lock Brake System (ABS), Parking Assist System (PAS), Electro Hydraulic Power Steering (EHPS) or Electro Assisted Steering (EAS). Nevertheless, the new trend of modernization has an immense impact on the remanufacturing business. It can be seen that new branches in electronic remanufacturing arise. In contrast to that, the knowhow of traditional remanufacturing companies has eroded rapidly and even the industrial principle of remanufacturing is at risk [ 1 ]. Due to the fact that modern cars incorporate up to 80 of these mechatronic and electronic systems that are communicating with each other e.g. via the CAN-bus, remanufacturing of these automotive systems requires innovative reverse engineering knowhow, methodological innovations and new technologies especially focussing on the tasks testing and diagnostics of systems and their subassemblies. Since, traditional remanufacturing companies do not have much capacity to build up the appropriate knowhow, the Chair of Manufacturing and Remanufacturing Technologies at Bayreuth University assists these companies in reverse engineering, as well as finding new methodologies and technologies for remanufacturing [ 2 , 3 ].

In the following chapters, reverse engineering methodologies, technologies and results for automotive components will be presented on the example of an EHPS pump. The results have been obtained within the European research project "CAN REMAN" which is conducted by Bayreuth University, Linköping University (Sweden), the University of Applied Sciences Coburg, Fraunhofer Project Group Process Innovation and eight industrial partners. The target of this project is to enable independent aftermarket (IAM) companies to remanufacture modern automotive mechatronics and electronics with innovative reverse engineering skills as well as to develop appropriate and affordable testing and diagnostics technologies [ 4 ]. The described, close to industry results, will contribute to the remanufacturing research theory by the upcoming PhD-thesis of engineers of the Chair of Manufacturing and Remanufacturing Technology.

2. Automotive Mechatronics Change Today's Remanufacturing

The term "mechatronics" was formulated in 1969 in Japan and it is an artifice that describes a system which combines mechanics, electronics and information technologies. A typical mechatronic system gathers data, processes the information and outputs signals that are for instance converted into forces or movements [ 5 ].

2.1. Technological Change of Vehicles

Automotive parts should not longer be seen as isolated standalone applications with few mechanical and electrical inputs and outputs. Now, they have the capability to communicate to each other and to share the same information. Subsequently, the communication of the different automotive subsystems helps the original equipment manufacturers (OEMs) to reduce weight and cost by sharing the same sensors and reducing cable doubling (cable length) in modern vehicles. For the driver the network and communication within the car remains invisible and he feels the car behaving like ten years ago despite of some additional comfort functions. Figure 1 demonstrates the radical shift in the automotive technological development.

Development in automotive maintenance [ 3 ].

But if we take a closer look, nowadays modern vehicles resemble more or less a distributed system. Several embedded computers - often referred to electronic control units (ECUs) - communicate, share information and verify each other over the vehicle network. One of the commonly used communication networks in vehicles is the CAN-bus. Within this network structure, each control unit has at least one unique identifier (ID) on which it broadcasts messages that again incorporate different signals and information [ 6 ]. Easily speaking, in case of a missing or faulty participant in the network, all other controllers will notice the participant as they have a lack of information. The lack of information or errors on the CAN-bus can force other systems to operate in a "safe mode" or cause that these systems never start their operation. In reverse, a controller not connected to the specific vehicle network will not start its regular operation patterns.

2.2. Difficulties for Remanufacturers

As stated before, the introduction of electronic networks into modern cars entails enormous problems for remanufacturers. Modern electronic and mechatronic vehicle components cannot be tested as easily as traditional electrical and mechanical ones [ 7 – 9 ]. While it was usually sufficient to link electrical systems to the power supply (battery), modern mechatronic and electronic systems gather a lot of information from the vehicle environment and driving conditions using plenty of sensors and the CAN-bus network of the vehicle. As a consequence, connecting all sensors and the power plug to the DUT is insufficient unless the device is connected to the network of a real car or an adequate simulation of the communication in the vehicle.

Following these statements, the key for successful remanufacturing and testing of a certain automotive system lies in the simulation of the complete network communication in the vehicle. In each case, the car matrix (CAN database) of the specific vehicle model is required to build a simulation of the CAN communication in a vehicle. However, the OEMs will not release any information on the communication parameters to non-OEs and therefore they will not support the independent remanufacturing business. As a consequence, the independent remanufactures - onto which this paper focuses - have to do a lot of reverse engineering themselves or in cooperation with others in order to design their remanufacturing process chain and to come up with test solutions to ensure the quality of their products [ 10 – 12 ]. These reverse engineering activities focus on the system, its components, the system behavior in the vehicle and the vehicle CAN-bus communication.

2.3. The Remanufacturing Process Chain for Automotive Mechatronics

Following the previous aspects, the state-of-the-art process chain for remanufacturing, needs to be reconsidered when it comes to mechatronics, as shown in Figure 2 . Regarding the process steps, disassembly, cleaning and reassembly, great progress has been made on mechanic systems, as it can be found in the literature [ 13 – 16 ]. This progress on the mechanical systems can also be transferred to the mechanic components inside of mechatronic systems. However, the diagnostics and testing differs to a certain extent from the traditional (final) testing of mechanics, as it has already been discussed before. In addition to this, it was found that a lot of failures of parts and its subassemblies can only be detected or isolated with a test of the completely assembled mechatronic system [ 2 , 17 ], e.g. by utilization of the onboard-diagnostics and readout of the fault memory inside a mechatronic system. This means that the process chain for remanufacturing of mechatronic systems should be extended by an additional first step as it is shown in Figure 2 .

Adopted remanufacturing process chain for mechatronics [ 20 ].

In the initial entrance diagnostics of the system to be remanufactured all communication patterns have to be reverse engineered in order to simulate the vehicle network and get access to the fault memory of the system. An appropriate vehicle network simulation will also prevent new fault memory records to be stored in the DUT.

3. Reverse Engineering an Automotive Mechatronic System

The term " reverse engineering " has its origin in the mechanical engineering and describes in its original meaning the analysis of hardware by somebody else than the developer of a certain product and without the benefit of the original documentation or drawings. However, reverse engineering was usually applied to enhance your own products or to analyze the competitor's products [ 18 ]. According to Cifuentes and Fitzgerald (2000), an analog term is " reengineering " (of software) which does not refer to the process of analyzing software only, but which also intends to translate software into a new form, either at the same or a higher level of abstraction. In addition to this, the two authors summarize the different types of software reverse engineering. It can be differentiated between black and white box reverse engineering. While black box reverse engineering only looks at the behavior of a program and its documentation (if it's available) without examination of the internals of the program, white box reverse engineering involves looking at the internals of a program so that its working can be understood [ 19 ].

Chikovsky and Cross (1990) describe reverse engineering in the context with software development and the software life cycle as an analysis process of a system, in order to identify the system (sub-) components, to investigate their interaction and to represent the system at a higher level of abstraction [ 18 ]. In this context, they also clarifie the terms "redocumentation" and "design recovery".

"Redocumentation" is the generation or revision of a semantically equivalent description at the same abstraction level. This means, that the results are an alternative representation form for an existing system description. However, redocumentation is often used in the context of recovering "lost" information [ 18 ].

The term "design recovery" defines a subset of reverse engineering that includes domain knowledge, external information (of third parties) and conclusions additionally to the original observations and analyses in order derive meaningful abstractions of the system at a higher level [ 18 ].

Overall, reverse engineering of software in the field of software development focuses on the following six targets [ 18 – 20 ]:

Coping with the system complexity

Generation of alternative views

Recovery of lost information

Detection of side effects

Synthesis of higher abstractions

Facilitation of reuse

These targets, that have originally been defined for software reverse engineering, can also be transferred to a certain extent to the reverse engineering of automotive mechatronic systems and hence to the remanufacturing of these systems.

First, remanufacturers will have to cope with the complexity of mechatronic systems as stated before. "Cope" means in this context, that it must be possible to operate an automotive mechatronic system independently from its original environment (the vehicle).

Second, universal taxonomies have to be detected in order to transfer the gained knowledge to similar mechatronic systems or to other variants of the system. Especially the high degree of variation of similarly looking mechatronic systems and control units makes it difficult for the remanufacturers to manage the complexity of automotive components that usually differ by a slight detail [ 21 ].

Third, recovery of missing, rather than lost, information will be one of the most important aspects for the remanufacturing.

The following chapter demonstrates how a reverse engineering analysis can be conducted for an automotive mechatronic system.

4. Analyzing an Automotive System in Five Steps

After a reference system (a reference system in this case is a commonly used automotive subsystem; for example an electro-hydraulic power steering pump) for the analysis has been chosen it is necessary to procure at least one, ideally brand-new, system to grant correct functionality, for all following investigations. In order to analyze the system in its normal working environment, the original vehicle, in which the reference system commonly is built in, should be procured as well.

This investment might be unavoidable, because a mechatronic system communicating via CAN, detached from all other vehicle communication will not work anyway, because essential input information, transmitted via CAN, is missing otherwise (refer to chapter 2). In this case it is very difficult to understand the ECU communication and put up the system into operation isolated from the vehicle.

A cheaper way to investigate the communication between vehicle and reference system is to create a CAN recording using a software tool such as "CANoe" from Vector Informatics. This tool allows easily recording of the complete vehicle communication for instance while doing a test drive with a vehicle that may be available only once. But this procedure requires careful planning prior the test drive is carried out, in order to record every driving condition which is needed for further analyses without having the vehicle available.

Whatever strategy is chosen, it is essential to figure out which input information (CAN data) is necessary to start, operate and control the system.

The following subsections will describe the five most important steps of the analysis process more in detail.

4.1. Electrical Wiring

After having obtained a reference system, it is essential to know the pinout of all connectors of the system. Therefore, the very first step is to find out which pin belongs to which wire and signal.

First of all, the power connector (ground and positive terminal), including ignition, must be identified. One opportunity to obtain this information is the utilization of wiring diagrams or similar credentials. If such documents are not available, for example a visual inspection of the connectors and wire harness in the vehicle or continuity measurements can be beneficial.

Afterwards, it is indispensible to identify the CAN connection pins. These can easily be recognized by inspecting the cable harness (in most cases two twisted wires, but single wire CAN connection is possible, too) or by measuring a terminating impedance of 60 Ω between to cables.

Finally, all connectors for sensors and actuators (auxiliary power and sensor/actuator signal) must be known as well to go further in the analysis process.

4.2. Vehicle Network Topology

The investigation of the structure of all bus systems in the vehicle is placed in front of the proper CAN-bus analysis step. It is necessary to determine how many (CAN-bus) networks are established and in which network the reference system is located. Additionally, the network speed, the presence of a separate diagnosis network (e.g. K-Line), and all ECUs of the specific networks must be found out, especially those ECUs that provide essential input as mentioned before. Furthermore, possible gateway ECUs, which are linking different networks, should be identified.

A feasible solution to gain this information can be for example a web inquiry, documents from the manufacturer of the vehicle or the system, third party documents or technical journals (e.g. ATZ, MTZ ...).

4.3. CAN Bus Communication

In order to understand the vehicle communication more in detail, all ECUs and its associated CAN message IDs must be determined. For this purpose CANoe can be used. First of all, a physical connection to access the CAN-bus using CANoe has to be installed in the vehicle, ideally nearby the reference system ECU. With the "trace functionality" of CANoe the bus communication and all CAN messages of all ECUs can be displayed easily (Figure 3 ). Beside of the CAN IDs, the cycle time and the length of each message can be analyzed. This information is relevant later on for a rest bus simulation of all participating ECUs to ensure correct functionality of the reference system.

CANoe trace with all CAN IDs (messages) that are sent by the different ECUs within the VW Polo (the first column shows the current time stamp in seconds, the second column shows the cycle time in seconds, the third one displays the IDs and the last column contains the data bytes of each message) .

The assignment of CAN ID and the associated ECU is more difficult. In the following, two options are described in detail.

One possibility to gather this information is to record the CAN communication initially with all ECUs connected to the bus using CANoe. Afterwards, each ECU is disconnected from the bus one after another and a CAN trace is stored again. Next, all recordings are compared to each other. Those IDs that are missing in the recording can be assigned to the disconnected ECU.

Another appropriate and more sophisticated way is to locate all ECUs which provide relevant data on the CAN bus and to separate the CAN wires out of the cable harness. Each end of the CAN wires in the vehicle must be connected to a computer via CAN hardware. Afterwards, a kind of software gateway (Figure 4 ) is installed in between the DUT and the other ECUs using CANoe and a simple CAPL (CAN Access Programming Language) program.

CANoe as software-gateway .

By this means, it is now possible to detect the messages on the bus as well as the transmit direction - receive or transmit. This step is repeated for each ECU which provides relevant input data for the reference system. Obviously, the time exposure for this kind of CAN-bus analysis is much higher due to fact that the gateway has to be placed in between every ECU which is connected to the CAN network. The higher the complexity level of the reference system (more inputs), the more time is needed to identify all ECU messages which transmit relevant data via CAN.

The second way is more satisfying, although it may be more time-consuming than the first one. The first option offers a good overview of all CAN messages and its original ECU, but it may be fault-prone and incomplete. No matter which way is chosen, the result is a complete CAN message structure. Both ways are targeting.

But not all identified messages are relevant for the DUT. Some are not recognized by the DUT and can be disregarded for further investigations. By adding filters for single messages in the gateway CAPL program or simply disconnecting whole ECUs from the network, an empty fault memory of the DUT will reveal unnecessary messages/ECUs and hence reduce data complexity. Hereby, an external garage tester can be used in most cases in order to readout the fault memory and in order to determine whether a failure was caused by removing certain data information.

After having identified the relevant CAN messages, it is inevitable to examine the message data bytes in detail to determine the physical signals. This can be achieved by generating physical inputs manually (e.g. open the throttle, drive, break ...) and observe the particular CAN messages as well as its bytes in parallel. After that, a correlation between a CAN message, its CAN data and a physical input value can be established.

Having performed the steps above, it is possible to setup the desired restbus simulation for the reference system.

4.4. Sensors

Besides the CAN data, analog inputs of sensors and analog outputs of actuators are important in order to ensure correct functionality of the reference system. Therefore, each sensor and nearly each actuator has to be analyzed and simulated, too.

The sensors can be analyzed using an oscilloscope and a multimeter in order to characterize current consumption, supply voltage and signal transmission. Typically, sensor output signals are analog to:

Current/voltage, amplitude

Frequency/cycle time

Pulse width/duty cycle

Or they are discrete in the following forms:

Multi-staged (different scaled)

Multi-staged (equidistant) → digital

For the simulation, the measured values must be interpreted and emulated. For example, the internal resistance of a sensor (load) can be calculated from the sensor current consumption. Afterwards, the presence of the sensor can be simulated using a (simple) resistor.

The simulation of the sensor signal can be realized using a waveform generator, an analog circuit, a microcontroller or a combination of them, depending on the signal characteristics.

4.5. Diagnostics

Finally, to test the reference system completely detached from the vehicle, it is necessary to know how the diagnosis communication works in order to check the fault memory and to read internal sensor information of the ECU (e.g. for temperature).

First, the applied protocols for transport and application layer must be identified. Often, standardized communication protocols for ECU diagnostics are used (e.g. ISO TP, KWP2000 or UDS). In some cases OEMs use proprietary self-developed keyword protocols (e.g. KWP1281). Thus, it is more difficult to establish a diagnosis connection to the reference system because the protocol specification is unknown to the remanufacturer. Hence, a detailed analysis of the CAN or K-Line communication during a diagnosis session is essential. Sophisticated reverse engineering capabilities are necessary in order to analyze, understand and recreate such a diagnosis communication. The message IDs, used for the communication, must be investigated independently by observing the diagnosis communication with CANoe. If the CAN IDs and protocols are known, the diagnose communication can be reproduced for example in CANoe using the CAPL environment.

After a remanufacturing company has accomplished all mentioned steps for the reference system, it is able to operate this system detached from all analog (sensor signals) or digital (CAN) inputs.

Finally, a test bench can be developed for entrance and final testing in series production scale.

5. Example: Remanufacturing of an Electro Hydraulic Power Steering (EHPS) Pump

An electro hydraulic power steering pump is a rotating oil pump driven by an electro motor. The pump converts electric power to hydraulic power. The hydraulic power is used to reduce the force the driver needs to steer the car. Most steering assistance is needed at low driving speeds, maybe for parking, which makes it necessary that the EHPS pump has information about the actual driving speed. That information is communicated via CAN-Bus.

The following six steps describe the reverse engineering process on the basis of an EHPS pump that is used in a VW Polo which is seen in Figure 5 .

Examination of the CAN Reman test vehicle .

5.1. Physical Analysis and Electrical Wiring of the EHPS

At the beginning, the EHPS has to be perceived as a black box with inputs and outputs. Because of the mechanical design and the general function of a hydraulic power steering, the output can be determined as the flow rate of the fluid [ 20 ]. The inputs are composed of an information about the internal combustion engine state (running or not running) and direct or indirect information about the necessary oil flow rate.

To get a first overview about the electrical connections of the device, a reference system (in this case the EHPS of the VW Polo - see Figure 6 )) was completely disassembled. Large connector pins were good indicators for the general power supply by reason that the power consumption of the electric motor is supposed to be high. The ground pin of this connector was found by searching for a direct linkage between those pins and the ground plate of the circuit board. The other cable on the connector is the positive power supply. At this point, the connection of the steering angle sensor, which is directly mounted on the steering shaft, was disregarded. The third connector contained three cables. Two of them were twisted in the following cable harness. That was a perfect indication for CAN cables. The CAN-high cable rises from 2.5 V to 3.5 V and the CAN-low cable falls from 2.5 V to 1.5 V during active communication. When operating the vehicle, the last cable was on 12 V level and therefore it was assumed to be the signal for "ignition on". At this point the electrical analysis of the device was completed.

Pins of the EHPS of the VW Polo .

5.2. Vehicle Network Topology

On the example of the VW Polo EHPS, all relevant ECUs for operating the DUT have to be in the same CAN-bus network (Figure 7 ). Unfortunately, the CAN bus is not linked to the on-board diagnosis (OBD) connector of the test vehicle, whereas usually selected CAN bus data is also accessible through this connection. Therefore, the CAN wires in between of the EHPS and the rest of the vehicle were separated in order to get access to this network for further investigations.

CAN bus topology of the test vehicle .

Assigning single messages/IDs to ECUs has simply been done by disconnecting single ECUs and locating missing messages/IDs. By a parallel readout of the internal fault memory of the DUT, relevant ECUs or single messages have been found.

5.3. CAN Bus Communication Investigations

This step can always be split into two parts. The first is the analysis of the communication in order to filter out and understand the relevant messages for the EHPS sent by other ECUs. The second is the simulation of the necessary CAN communication, which is called "restbus" in the following.

First, the start signal, transmitted to the EHPS via CAN bus, must be discovered as described in step 2. Therefore, a recording of the in-car CAN communication was made at a stationary test with well defined and reproducible conditions. After that, the recording was replayed to the test device outside the car and it started its operation. Next, CAN messages were successively filtered out until the motor of the test device stopped. Hence, the last filtered message contained some kind of a start signal. Having performed in depth analyses, this signal was identified to be the RPM signal of the internal combustion engine. In order to eliminate or to find other input parameters, the same study was carried out using a recording of a real-road test. It was found that the vehicle speed is another input parameter for the EHPS.

Second, required input parameters were simulated with CANoe. Using a third party diagnosis garage tester (Bosch KTS 650), it was discovered that the fault memory of the external EHPS can only be erased when at least the presence of the missing messages of the in-car communication is simulated, too. This simulation of messages with and without data content is called restbus.

At this point the EHPS can completely be operated outside the car, but with a real steering angle sensor.

5.4. Simulation of Sensors

In order to operate the EHPS in a completely simulated environment, the angular velocity sensor had to be simulated.

Analog to step one, VCC and GND were identified on the sensor terminal using a multimeter. The third cable transfers the information about the angular velocity of the steering wheel. This signal was analyzed using an oscilloscope (Figure 8 ) and identified as a pulse width modulated signal. This signal was simulated by a waveform generator. Furthermore, the sensor presence had to be emulated by a simple 600 Ω resistor matching the power consumption of the original sensor.

Measuring the sensor signal .

5.5. Diagnostic Functions of the Device

Most devices, including the present EHPS, can be diagnosed over CAN-bus with an external diagnosis garage tester. This tester can, as mentioned above, directly communicate with ECUs using a transport and a keyword protocol. The protocols are only partially defined and the communication differs from brand to brand tremendously. Therefore, the most efficient way to understand how e.g. the fault memory can be erased is to erase the fault memory with one of those testers and to try projecting the sequence onto known standards. In the present case, it were the standards KWP1281 and TP1.6. Even though the understanding of the diagnosis communication was very time-consuming, it was possible to erase and read the fault memory, to read the internal sensor data or duty cycles, to parameterize the device for different car models or even to completely reprogram the software.

Finally, all functions were implemented in CANoe using CAPL which can be controlled by a graphical user interface (GUI).

5.6. Operation Range

At last, the correlations between input and output values were determined in detail. For this reason, the input parameters angular velocity, vehicle speed, RPM and the outputted oil flow rate were recorded simultaneously.

In this case the RPM signal only started the EHPS and was disregarded for the measurement. The vehicle speed was found in a particular message on the CAN bus as figured out in step 3. The angular velocity value is part of the sensor data provided by the EHPS in a diagnosis communication session as mentioned in step 5. The resulting oil flow rate was measured by installing an oil flowmeter to the low pressure side of the EHPS in the test vehicle. This flowmeter generates a frequency modulated signal which was converted to a CAN message by a microcontroller and broadcasted to the local in-car CAN network in a separate CAN message. Finally, all necessary input and output values were recorded from the CAN network time simultaneously using CANoe. Figure 9 depicts the flow rate of the steering oil as a result of vehicle speed and angular velocity, measured in a real-road test.

Flow rate as a function of vehicle speed and angular velocity .

5.7. Practicability of the results

For further mechatronic systems an analysis time of 2 days to 2 month is required, depending on the system complexity. To give some examples for the time required: 2 days for example for another EHPS pump in another VW (each model needs new analyses), 5 days for another EHPS pump in different brand, 1 month for an absolutely new mechatronic system with medium complexity and 2 month or longer for a very complex mechatronic system like an automatic gear box. The costs for the analyses are splitted in the fix costs for the hard- and software of about 40.000 Euro and the costs for the employees for the days they work on. The reliability and safety of a remanufactured mechatronic system is in the same level compared with a new system. A mandatory regulation about standardization of the signals would decrease the costs significantly.

6. Conclusion

A still increasing number of mechatronic and electronic systems is built into today's vehicles. In the future, even more of these systems will be introduced to the cars as a result of increasing demand for comfort, safety and reduced fuel consumption. Remanufacturing of failing mechatronic systems offers a great opportunity for all, the OEMs and OEs which can safe resources and provide spare parts over a long period of time without the demand of long time warehousing; the remanufacturing companies as they can make a growing new business with these systems; and the customers that are benefitting from cheaper, but as good as new, spare parts.

Progress is not possible without its challenges, but it is achievable. The increasing complexity and variety of mechatronic end electronic devices cannot be handled with traditional methodologies. Therefore, remanufacturing companies have to build up new reverse engineering knowhow, find methodological innovations and they need to develop new technologies, especially focusing on the tasks testing and diagnostics of automotive systems and their subassemblies. After having met these challenges, new remanufacturing steps, such as the initial test, can be established and increase the productivity of the remanufacturing businesses e.g. in terms of an automated identification of systems or automated electronic test.

The paper outlines challenges, possible solutions and technological progress for the reverse engineering process of mechatronic automotive systems that are communicating via CAN-bus. In addition to this, the reverse engineering process is demonstrated on the example of an EHPS which is used in a VW Polo. Obviously, it was possible to completely understand the steering system of the VW Polo by reverse engineering. Now, it is possible to run and test the mechatronic system outside the car as well as to adopt the results for remanufacturing the system in series production scale. The same principle can also be applied to further automotive systems, so that everyone wins, regardless of perspective.

Authors' information

Dr.-Ing. Stefan Freiberger

Managing Engineer at:

Bayreuth University

Chair Manufacturing and Remanufacturing Technology and Fraunhofer Project Group Process Innovation

Chairman of the Mechatronics and Electronics Division of APRA (Automotive Parts Remanufacturers Association)

Consultant in the fields of: Remanufacturing, Material- and Energy Efficiency, Process Innovation in Production, Lean Management and Six Sigma

PhD thesis about: "Prüf- und Diagnosetechnologien zur Refabrikation von mechatronischen Systemen aus Fahrzeugen"; „Test and Diagnosis Technologies for Remanufacturing Automotive Mechatronic Systems".

M.Sc., Dipl.-Ing. (FH) Matthias Albrecht

Engineer and research assistant at

Field of Activity: Research for remanufacturing of mechanical, electronic and mechatronic components.

Development of test and diagnosis methods for coupled mechatronic and electronic systems with CAN-bus. Transfer of developed technologies and test equipment to different systems and implementation of these systems in remanufacturing companies. Development of industrial test equipment for mechatronic and electronic automotive components that is application-orientated and easy-to-use.

Dipl.-Ing. (FH) Josef Käufl

Research Engineer at:

Field of activity: Technologies for remanufacturing of mechanical, electronic and mechatronic components. Development of test and diagnosis methods for coupled mechatronic and electronic systems with CAN-bus. Transfer of developed technologies and test equipment to different systems and implementation of these systems in remanufacturing companies.

Abbreviations

Anti-lock Breaking System

Automobiltechnische Zeitschrift (title of an automotive and technical journal)

Controller Area Network

CAN Access Programming Language

Current Replacement Value

Device Under Test

Electro Assisted Steering

Electronic Control Unit

Electro Hydraulic Power Steering

Electronic Stability Program

Ground Connection

Graphical User Interface

Parking Assist System

Independent Aftermarket

(CAN-bus) Identifier

Motortechnische Zeitschrift (title of an engine related journal)

Original Equipment Manufacturer

OEM Product-Service Institute

Positive Power Supply

Volkswagen.

Freiberger S: Finding profitable products for Remanufacturing. APRA Global Connection, Ausgabe Nr,6 Chantilly 2010.

Google Scholar  

Steinhilper R, Rosemann B, Freiberger S: Product and Process Assessment for Remanufacturing of Computer Controlled Automotive Concepts. 13th CIRP International Conference on Life Cycle Engineering, Leuven, Belgium, May 31st - June 2nd 2006.

Steinhilper R: Automotive Service Engineering and Remanufacturing: New Technologies and Opportunities. 15th CIRP International Conference on Life Cycle Engineering, Sidney, Australia, March 17th - 19th 2008.

Freiberger S: European Research Project "Major European reman project given the green light" Starts Now. ReMaTecNews 2/2009, RAI Langfords B.V./RAI Publishing House, Amsterdam 2009.

Roddeck W: Einführung in die Mechatronik. 3rd edition. B.G. Teubner Verlag/GWV Fachverlage GmbH, Wiesbaden, Germany; 2006.

Zimmermann W, Schmidgall R: Bussysteme in der Fahrzeugtechnik. 3rd edition. Vieweg + Teubner/GWV Fachverlage GmbH, Wiesbaden, Germany; 2008.

Freiberger S, Steinhilper R, Heinrich A, Brüggemann D: Failure Detection and Isolation through Infrared Thermal Imaging. In ReMaTecNews - Automotive Remanufacturing International . Volume 6 . RAI Publishing House, Amsterdam, Dezember; 2006.

Freiberger S, Steinhilper R, Stöber R, Fischerauer G: How to remanufacture partially documented mechatronic systems. APRA Global Connection, Ausgabe 16, Chantilly 2006.

Freiberger S: Remanufacturing of Mechatronics and Electronics. APRA Mechatronics and Electronics Division, Harrisburg 2006. [ http://www.apra-europe.org ]

Freiberger S, Landenberger D, Wrobel S: FMEA in der Refabrikationsindustrie - Erfassen, bewerten, vermeiden. In Quality Engineering, Ausgabe 04/2006 . Konradin Verlag, Leinfelden-Echterdingen; 2006.

Freiberger S, Rosemann B, Steinhilper R: Design for Recycling and Remanufacturing of Fuel Cells. Proceedings Eco Design 2005: 4th International Symposium on Environmentally Conscious Design and Inverse Manufacturing, Tokyo 12. bis 14 2005.

Freiberger S, Rosemann B: State of the Art Application and End-of-Life of Fuel Cell Systems. Proceedings 9th International Congress for Battery Recycling, Como, 2. bis 4 2004.

Johnson MR, Wang MH: Economical evaluation of disassembly operations for recycling, remanufacturing and reuse. International Journal of Production Research 1998, 36 (12):3227–3252. 10.1080/002075498192049

Article   Google Scholar  

Seliger G, Hentschel C, Wagner M: Disassembly Factories for Recovery of Resources in Product and Material Cycles, pp 56 - 67. In Life-Cycle Modeling for innovative Products and Processes, Proceedings on life-cycle modeling for innovative products and processes, Berlin, Germany, November/December 1995 . Edited by: Jansen H, Krause F-L. Chapman & Hall; 1995.

Seliger G, Grudzien W, Zaidi H: New Methods of Product Data Provision for a simplified Disassembly. Proceedings of the Life Cycle Design 99, Kingston, Kanada 1999.

Westkämper E, Alting Arndt: Life Cycle Management and Assessment: Approaches and Visions Towards Sustainable Manufacturing. CIRP Annals - Manufacturing Technology 2000, 49 (2):501–526. 10.1016/S0007-8506(07)63453-2

Freiberger S: Selected and Applied Test and Diagnosis Methods for Remanufacturing Automotive Mechatronics and Electronics. In Remanufacturing Automotive Mechatronics and Electronics . Edited by: Fernand J. Weiland, Germany; 2008.

Chikofsky EJ, Cross JH II: Reverse Engineering and Design Recovery: A Taxonomy. IEEE Software, IEEE Computer Society 1990, 13–17.

Cifuentes C, Fitzgerald A: The legal status of reverse engineering of computer software. In Anals of Software Engineering . Volume 9 . Springer Netherlands; 2000:337–351. 10.1023/A:1018993212326

Freiberger S: Prüf- und Diagnosetechnologien zur Refabrikation von mechatronischen Systemen aus Fahrzeugen. Dissertation, Reihe: Fortschritt in Konstruktion und Produktion, Band 6, Shaker Verlag, Aachen, März 2007.

Haumann M, Köhler DCF: Coping with complexity in remanufacturing. Rematec News 9 (3):32–33.

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Acknowledgements

The research project "CAN REMAN" and the activities described in this paper have been financed by the German Federal Government Department for Education and Research (support code 16INE014). Nobody, beyond the mentioned author's, contributed materials essential for the study.

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AB carried out the molecular genetic studies, participated in the sequence alignment and drafted the manuscript. JY carried out the immunoassays. MT participated in the sequence alignment. ES participated in the design of the study and performed the statistical analysis. FG conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.

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Freiberger, S., Albrecht, M. & Käufl, J. Reverse Engineering Technologies for Remanufacturing of Automotive Systems Communicating via CAN Bus. Jnl Remanufactur 1 , 6 (2011). https://doi.org/10.1186/2210-4690-1-6

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Received : 30 November 2010

Accepted : 07 December 2011

Published : 07 December 2011

DOI : https://doi.org/10.1186/2210-4690-1-6

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