Interdisciplinary Innovative Talent Training Method and Practice on Modeling and Simulation for Intelligent Manufacturing

  • Yuanjun Laili,
  • Lin Zhang ,
  • Lei Ren ,
  • Lei Wang,
  • Yang Li
  • a,b,c,d,e  School of Automation Science and Electrical Engineering, Beihang University
Cite as
Laili Y., Zhang L., Ren L., Wang L., Li Y. (2021). Interdisciplinary Innovative Talent Training Method and Practice on Modeling and Simulation for Intelligent Manufacturing. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 131-139. DOI: https://doi.org/10.46354/i3m.2021.emss.018

Abstract

Modeling and simulation are the key to implement digital and intelligent manufacturing. It is of great necessity to train high-level innovative talents on the interdisciplinary area of modeling and simulation and intelligent manufacturing. This paper proposes a talent training method by bridging three gaps, i.e., the gap between multiple disciplines, the gap between countries, and the gap between students of different grades, and connecting national major requirements and international state-of-the-art technologies. According to tens of years of exploration and trying, an advanced interdisciplinary training system is established. It includes a training chain with multiple angles, an open study and research platform with international organizations, and a collaborative mode with important national projects. It attempts to lead young talents to solve complex industrial problem innovatively, independently, and collaboratively. Practice examples have shown that the proposed method is able to provide full resources for young talents to train their innovation ability, international communication ability, and teamwork ability, and thus raised many talents on the area of modeling and simulation for intelligent manufacturing.

References

  1. Foresight (2013). The future of manufacturing: A new era of opportunity and challenge for the UK. London: The Government Office for Science.
  2. Kagermann, H., Wahlster, W., and Helbig, J. (2013). Recommendations for implementing the strategic initiative Industrie 4.0: Final report of the Industrie 4.0 Working Group. R. Munich: National Academy of Science and Engineering (acatech).
  3. Close Executive Office of the President, National Science and Technology Council (2012). A national strategic plan for advanced manufacturing. R. Washington, DC: Office of Science and Technology Policy.
  4. Taki, H. (2017). Towards Technological Innovation of Society5.0. Journal-Institute of Electrical Engineers of Japan, 137(5): 275-275.
  5. State Council of the People’s Republic of China (2017). “Made in China 2025” plan unveiled [Internet]. Beijing: State Council of the People’s Republic of China. Available from: http://www.gov.cn/zhengce/content/2015-05/19/content_9784.htm. Chinese.
  6. Kou, Y., Zhang, L., and Zhou, L. (2020). Construction of Digital Factory Platform Based on Intelligent Manufacturing. In International Conference on Application of Intelligent Systems in Multi-modal Information Analytics, 205-211.
  7. Zhang, F., Zhou, F., Wang, Z., and He, Y. (2020). Construction and Exploration of Intelligent Manufacturing and Virtual Simulation Laboratory Based on Integration of Production and Education. In International Workshop of Advanced Manufacturing and Automation. Springer, Singapore, 280-285. 
  8. Möller, D. P., Jehle, I. A., and Hou, W. (2020). Engineering Education in Intelligent Manufacturing. In 2020 IEEE International Conference on Electro Information Technology (EIT), 007-012.
  9. Zhang, L., Zhou, L., Ren, L., and Laili, Y. J. (2019). Modeling and simulation in intelligent manufacturing. Computers in Industry, 112, 103123.
  10. Jardim-Goncalves, R., Romero, D., and Grilo, A. (2017). Factories of the future: challenges and leading innovations in intelligent manufacturing. International Journal of Computer Integrated Manufacturing, 30(1), 4-14.
  11. Ren, L., Meng, Z. H., Wang, X. H., Zhang, L. and Yang, L. (2021). A Data-driven Approach of Product Quality Prediction for Complex Production Systems. IEEE Transactions on Industrial Informatics, 17(9): 6457-6465.
  12. Ren, L., Meng, Z. H., Wang, X. H., Lu, R. Q. and Yang, L. (2020). A Wide-Deep-Sequence Model based Quality Prediction Method in Industrial Process Analysis. IEEE Transactions on Neural Networks and Learning Systems, 31(9): 3721-3731.
  13. Ren, L., Liu, Y. X., Wang, X. H., Lu, J. H., Jamal Deen, M. (2020). Cloud-Edge based Lightweight Temporal Convolutional Networks for Remaining Useful Life Prediction in IIoT. IEEE Internet of Things Journal, DOI: 10.1109/JIOT.2020.3008170.
  14. Ren, L., Laili, Y. J., Li, X., Wang, X.K. (2020). Coding-based large-scale task assignment for industrial edge intelligence. IEEE Transactions on Network Science and Engineering, 7(4): 2286-2297.
  15. Laili, Y. J., Zhang, L., Li, Y. (2019). Parallel transfer evolution algorithm. Applied Soft Computing, 75: 686-701.
  16. Laili, Y. J., Li, Y. L., Fang, Y. L., Duc, T. P., Zhang. L. (2020). Model review and algorithm comparison on multi-objective disassembly line balancing. Journal of Manufacturing Systems, 56: 484-500.
  17. Laili, Y. J., Lin, S. S., Tang, D. Y. (2020). Multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment. Robotics and Computer-Integrated Manufacturing, 61: 101850.