Adaptive Assembly Approach for E-Axles
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Abstract
Achieving high quality, high variety batch size production can be quite expensive. In this vision article, the methodology of achieving this at low costs and the available technologies in the field of e-mobility production are described. The focus of this research lies in high adaptive and cognitive aspects in the assembly along with qualitative aspects. To match the high flexibility of a Flexible Manufacturing System (FMS) while considering quantitative efforts, a use case of an e-axle assembly is being done. E-axle is chosen due to the ongoing electrification of mobility. Hence, a solution for implementing a set of methodologies for an adaptive manufacturing system with respect to assembly, quality and implementation efforts is shown. A LoPA (Level of Practical Application) matrix is presented of all the possible adaptive technologies that are feasible to implement in the e-assembly line.
Keywords
E-mobility Cognitive production High variety batch production Level of Practical Application (LoPA) Qualitative analysisNotes
Acknowledgments
The authors gratefully acknowledge the support from Pro2Future GmbH. Pro2Future is funded as part of the Austrian COMET Program – Competence Centers for Excellent Technologies – under the auspices of the Austrian Federal Ministry of Transport, Innovation and Technology, the Austrian Federal Ministry for Digital and Economic Affairs, and the Provinces of Upper Austria and Styria. COMET is managed by the Austrian Research Promotion Agency FFG.
References
- 1.Ko, J., Hu, S. J., & Huang, T. (2005). Reusability assessment for manufacturing systems. CIRP Annals. – Manufacturing Technology. https://doi.org/10.1016/S0007-8506(07)60062-6.CrossRefGoogle Scholar
- 2.Abou-El-Hossein, K. A., Theron, N. J., & Ghobashy, S. (2015). Design of machine tool based on reconfigurability principles. Applied Mechanics and Materials. https://doi.org/10.4028/www.scientific.net/amm.789-790.213.CrossRefGoogle Scholar
- 3.Mehrabi, M. G., Ulsoy, A. G., Koren, Y., & Heytler, P. (2002). Trends and perspectives in flexible and reconfigurable manufacturing systems. Journal of Intelligent Manufacturing. https://doi.org/10.1023/A:1014536330551.CrossRefGoogle Scholar
- 4.Sugiarto, I., Axenie, C., & Conradt, J. (2016). From adaptive reasoning to cognitive factory: Bringing cognitive intelligence to manufacturing technology. International Journal of Industrial Research and Applied Engineering. https://doi.org/10.9744/jirae.1.1.1-10.
- 5.Marcel Schwartz, M. S., Dipl.-W irt.-Ing., Dominik Kolz, M. S., & Katharina Heeg, M. A. (2016). Dienstleistungsinnovationen für Elektromobilität – Förderung von Innovation und Nutzerorientierung. Amsterdam.Google Scholar
- 6.Electric Car (Market) Data. https://evobsession.com/electric-car-sales/.
- 7.Wiendahl, H. P., ElMaraghy, H. A., Nyhuis, P., Zäh, M. F., Wiendahl, H. H., Duffie, N., & Brieke, M. (2007). Changeable manufacturing – Classification, design and operation. CIRP Annals. – Manufacturing Technology. https://doi.org/10.1016/j.cirp.2007.10.003.CrossRefGoogle Scholar
- 8.Fasth-Berglund, Å., & Stahre, J. (2013). Cognitive automation strategy for reconfigurable and sustainable assembly systems. Assembly Automation. https://doi.org/10.1108/AA-12-2013-036.CrossRefGoogle Scholar
- 9.Dencker, K., Fasth, Å., Stahre, J., Mårtensson, L., Lundholm, T., & Akillioglu, H. (2009). Designing proactive assembly systems (ProAct) – Criteria and interaction between automation, information, and competence. Annual Reviews in Control, 33(2), 230–237.CrossRefGoogle Scholar
- 10.Lotter, B., & Wiendahl, H.-P. (2008). Changeable and reconfigurable assembly systems. In Changeable and reconfigurable manufacturing systems. London: Springer.Google Scholar
- 11.Meichsner, T. P. (2009). Migration manufacturing – A new concept for automotive body production. In Changeable and reconfigurable manufacturing systems. London: Springer.Google Scholar
- 12.Bussmann, S., & Sieverding, J. (2001, October). Holonic control of an engine assembly plant: An industrial evaluation. In 2001 IEEE international conference on systems, man and cybernetics. E- systems and e-man for cybernetics in cyberspace (cat. No. 01CH37236) (Vol. 1, pp. 169--174). IEEE.Google Scholar
- 13.Gräßler, I., & Pöhler, A. (2017). Implementation of an adapted holonic production architecture. Procedia CIRP, 63, 138–143.CrossRefGoogle Scholar
- 14.Bi, Z. M., Lang, S. Y. T., Shen, W., & Wang, L. (2008). Reconfigurable manufacturing systems: The state of the art. International Journal of Production Research. https://doi.org/10.1080/00207540600905646.CrossRefGoogle Scholar
- 15.Koren, Y., & Shpitalni, M. (2010). Design of reconfigurable manufacturing systems. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2011.01.001.CrossRefGoogle Scholar
- 16.Katz, R. (2007). Design principles of reconfigurable machines. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-006-0615-2. CrossRefGoogle Scholar
- 17.Abele, E., Liebeck, T., & Wörn, A. (2006). Measuring flexibility in investment decisions for manufacturing systems. CIRP Annals. – Manufacturing Technology. https://doi.org/10.1016/S0007-8506(07)60452-1.CrossRefGoogle Scholar
- 18.Koren, Y., Gu, X., & Guo, W. (2018). Reconfigurable manufacturing systems: Principles, design, and future trends. Frontiers of Mechanical Engineering, 13(2), 121–136.CrossRefGoogle Scholar
- 19.Gorecky, D., Worgan, S. F., & Meixner, G. (2011). COGNITO: A cognitive assistance and training system for manual tasks in industry. In Proceedings of the 29th annual European conference on cognitive ergonomics.Google Scholar
- 20.ElMaraghy, H. A. (2008). Changeable and reconfigurable manufacturing systems. New York: Springer.Google Scholar
- 21.Wallhoff, F., AblaBmeier, M., Bannat, A., Buchta, S., Rauschert, A., Rigoll, G., & Wiesbeck, M. (2007, July). Adaptive human-machine interfaces in cognitive production environments. In 2007 IEEE international conference on multimedia and expo (pp. 2246--2249). IEEE.Google Scholar
- 22.Funk, M., & Schmidt, A. (2015). Cognitive assistance in the workplace. IEEE Pervasive Computing. https://doi.org/10.1109/MPRV.2015.53.CrossRefGoogle Scholar
- 23.Böckenkamp, A., Mertens, C., Prasse, C., Stenzel, J., & Weichert, F. (2017). A versatile and scalable production planning and control system for small batch series (In industrial internet of things (pp. 541–559)). Cham: Springer.CrossRefGoogle Scholar
- 24.Pascu, C. I., & Paraschiv, D. (2016). Study about improving the quality process performance for a steel structures components assembly using FMEA method. Applied Mechanics and Materials. https://doi.org/10.4028/www.scientific.net/amm.822.429.CrossRefGoogle Scholar
- 25.Betterton, C. E., & Silver, S. J. (2012). Detecting bottlenecks in serial production lines – A focus on interdeparture time variance. International Journal of Production Research. https://doi.org/10.1080/00207543.2011.596847.CrossRefGoogle Scholar
- 26.Law, A. M. (2009). How to build valid and credible simulation models. In Proceedings – Winter Simulation Conference.Google Scholar
- 27.Kikolski, M. (2016). Identification of production bottlenecks with the use of Plant Simulation software. Engineering Management in Production and Services. https://doi.org/10.1515/emj-2016-0038.CrossRefGoogle Scholar
- 28.Zhuang, C., Liu, J., & Xiong, H. (2018). Digital twin-based smart production management and control framework for the complex product assembly shop-floor. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-018-1617-6.CrossRefGoogle Scholar