Browse > Article
http://dx.doi.org/10.12989/acd.2019.4.1.043

Industry 4.0 - A challenge for variation simulation tools for mechanical assemblies  

Boorla, Srinivasa M. (Technical University of Denmark, Department of Mechanical Engineering)
Bjarklev, Kristian (Technical University of Denmark, Department of Mechanical Engineering)
Eifler, Tobias (Technical University of Denmark, Department of Mechanical Engineering)
Howard, Thomas J. (Technical University of Denmark, Department of Mechanical Engineering)
McMahon, Christopher A. (Technical University of Denmark, Department of Mechanical Engineering)
Publication Information
Advances in Computational Design / v.4, no.1, 2019 , pp. 43-52 More about this Journal
Abstract
Variation Analysis (VA) is used to simulate final product variation, taking into consideration part manufacturing and assembly variations. In VA, all the manufacturing and assembly processes are defined at the product design stage. Process Capability Data Bases (PCDB) provide information about measured variation from previous products and processes and allow the designer to apply this to the new product. A new challenge to this traditional approach is posed by the Industry 4.0 (I4.0) revolution, where Smart Manufacturing (SM) is applied. The manufacturing intelligence and adaptability characteristics of SM make present PCDBs obsolete. Current tolerance analysis methods, which are made for discrete assembly products, are also challenged. This paper discusses the differences expected in future factories relevant to VA, and the approaches required to meet this challenge. Current processes are mapped using I4.0 philosophy and gaps are analysed for potential approaches for tolerance analysis tools. Matching points of simulation capability and I4.0 intents are identified as opportunities. Applying conditional variations, incorporating levels of adjustability, and the un-suitability of present Monte Carlo simulation due to changed mass production characteristics, are considered as major challenges. Opportunities including predicting residual stresses in the final product and linking them to product deterioration, calculating non-dimensional performances and extending simulations for process manufactured products, such as drugs, food products etc. are additional winning aspects for next generation VA tools.
Keywords
industry 4.0; variation analysis; Monte Carlo; conditional variation; selective manufacturing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Arya, S., Sachin, K. and Ciney, J. (2012), "Application of Monte Carlo technique for analysis of tolerance and allocation of reciprocating compressor assembly", J. Res. Mech. Eng. Technol., 2(1), 15-20.
2 Bockenkamp, A., Mertens, C., Prasse, C., Stenzel, J. and Weichert, F. (2017), "A versatile and scalable production planning and control system for small batch series", Industrial Internet Things, Springer, 541-559.
3 Boorla, S.M., Troldtoft, M.E., Eifler, T. and Howard, T.J. (2017), "Quantifying the robustness of process manufacturing concept - a medical product case study", Adv. Product. Eng. Manag., 12(2), 127-138.   DOI
4 Boorla, S.M. and Howard, T.J. (2016), "Production monitoring system for understanding product robustness", Adv. Product. Eng. Manag., 11(3), 159-172.   DOI
5 Bort, C.M.G., Leonesio, M. and Bosetti, P. (2016), "A model-based adaptive controller for chatter mitigation and productivity enhancement in CNC milling machines", Robot. CIM-Int. Manuf., 40, 34-43.   DOI
6 Chen, T. and Tsai, H.R. (2017), "Ubiquitous manufacturing: Current practices, challenges, and opportunities", Robot. CIM-Int. Manuf., 45, 126-132.   DOI
7 Boorla, S.M., "Design for Industry 4.0 - Today's opportunity and tomorrow's need", Linkedin, December. https://www.linkedin.com/pulse/design-industry-40-todays-opportunity-tomorrows-need-boorla, Accessed on 28th April 2017.
8 Dopico, M., Gomez, A., De la Fuente, D., Garcia, N., Rosillo, R. and Puche, J. (2016), "A vision of industry 4.0 from an artificial intelligence point of view", Proceedings on the International Conference on Artificial Intelligence (ICAI), Las Vegas, U.S.A., July.
9 Ferryanto, L. (2007), "Design for six sigma", Jurnal Teknik Industri, 9(1), 1-14.
10 Gonzalo, O., Seara, J.M., Guruceta, E., Izpizua, A., Esparta, M., Zamakona, I. and Thoelen, J. (2017), "A method to minimize the workpiece deformation using a concept of intelligent fixture", Robot. CIM-Int. Manuf., 48, 209-218.   DOI
11 Maiolino, P., Woolley, R., Branson, D., Benardos, P., Popov, A. and Ratchev, S. (2017), "Flexible robot sealant dispensing cell using RGB-D sensor and off-line programming", Robot.CIM-Int. Manuf., 48, 188-195.   DOI
12 Mikael, R., Ann-Christine, F., Lars, L. and Rikard, S. (2016), "Variation analysis considering manual assembly complexity in a CAT Tool", Proceedings of 14th CIRP conference of Computer Aided Tolerancing, Goteborg, Sweden, May.
13 Muller, R., Matthias, V.M., Horauf, L. and Speicher, C. (2016), "Identification of assembly system configuration for cyber-physical assembly system planning", Appl. Mech. Mater., 840, 24-32.   DOI
14 Murthy, S. Boorla, Eifler, T., McMahon, C. and Howard, T.J. (2018), "Product Robustness Philosophy-A Strategy Towards Zero Variation Manufacturing (ZVM)", Manag. Prod. Eng. Rev., 9(2), 3-12.
15 Prisco, U. and Giorleo, G. (2002), "Overview of current CAT systems", Integr. Comput-Aid Eng., 9(4), 373-387.   DOI
16 Suri, K., Cuccuru, A., Cadavid, J., Gerard, S., Gaaloul, W. and Tata, S. (2017), "Model-based development of modular complex systems for accomplishing system integration for industry 4.0", 5th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2017), Porto, Portugal, February.
17 Putnik, G. (2012), "Advanced manufacturing systems and enterprises: Cloud and ubiquitous manufacturing and an architecture", J. Appl. Eng. Sci., 10(3), 127-134.   DOI
18 Rubinstein, R.Y. and Kroese, D.P. (2016), Simulation and the Monte Carlo Method, John Wiley & Sons, New York, U.S.A.
19 Salimans, T., Kingma, D.P. and Welling, M. (2015), "Markov chain Monte Carlo and variational inference: Bridging the gap", proceedings of 32nd International Conference of Mechanical Learning, Lille, France, July.
20 Wan, X.J., Li, Q. and Wang, K. (2017), "Dimensional synthesis of a robotized cell of support fixture", Robot. CIM-Int. Manuf., 48, 80-92.   DOI
21 Wang, S., Wan, J., Li, D. and Zhang, C. (2016), "Implementing smart factory of industrie 4.0: An outlook", J. Distributed Sensor Networks, 12(1), 3159805.   DOI
22 Whitney, D.E., Mantripragada, R., Adams, J.D. and Rhee, S.J. (1999), "Designing assemblies", Res. Eng. Des., 11(4), 229-253.   DOI
23 Yan, H., Wu, X. and Yang, J. (2015), "Application of monte carlo method in tolerance analysis", Proceedings of 13th CIRP conference of Computer Aided Tolerancing, Hangzhou, China, May.