Skip to main content

Reference training system for intelligent manufacturing talent education: platform construction and curriculum development

Abstract

Intelligent manufacturing (IM) talent education is becoming considerably relevant owing to the increasing versatility and multi-level compound requirements of academic and applied personnel. Serving as the core promoter of IM talent education, the actions and orientation of higher education are essential for IM society. However, there is no coherent framework in current engineering teaching programs, which can determine the key advancements of IM to facilitate theoretical learning and professional skills. Hence, this study presents a systematic reference training system in a highly student-centered interpretation for IM formation by establishing (1) innovation education platform, to bridge theoretical learning and hands-on practice and (2) enhanced curriculum development, which advocates broad training outcomes from multi-perspectives. First, the IM system architecture and the required attributes of IM talents are analyzed. Second, an innovation platform is constructed by incorporating interdisciplinary IM frontiers. Third, combining multimedia and interactive instruction, the IM training course clusters are reformed by covering overall cognition, module practice, and comprehensive application. In particular, a typical IM production line is depicted to increase students’ interests in participation, which can simultaneously develop disciplinary knowledge and practical skills. From the perspectives of students and instructors, evaluations and assessments demonstrate that this presented reference training system is beneficial for supporting modern IM talent education.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig.5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27

References

  1. Aithal, P. S., & Aithal, S. (2016). Impact of on-line education on higher education system. International Journal of Engineering Research and Modern Education, 1(1), 225–235.

    Google Scholar 

  2. Arvin, F., Espinosa, J., Bird, B., West, A., Watson, S., & Lennox, B. (2019). Mona: An affordable open-source mobile robot for education and research. Journal of Intelligent and Robotic Systems, 94(3–4), 761–775.

    Article  Google Scholar 

  3. Baladrón, C., Jiménez, M. I., Aguiar, J. M., Carro, B., Sánchez-Esguevillas, J., & A. . (2013). Improving teaching in engineering education: Adjunct enterprise professors programme. Journal of Intelligent Manufacturing, 24, 495–499.

    Article  Google Scholar 

  4. Barba-Sánchez, V., & Atienza-Sahuquillo, C. (2018). Entrepreneurial intention among engineering students: The role of entrepreneurship education. European Research on Management and Business Economics, 24(1), 53–61.

    Article  Google Scholar 

  5. Baumfield, V. M., Conroy, J. C., Davis, R. A., & Lundie, D. C. (2012). The Delphi method: Gathering expert opinion in religious education. British Journal of Religious Education, 34(1), 5–19.

    Article  Google Scholar 

  6. Benešová, A., & Tupa, J. (2017). Requirements for education and qualification of people in Industry 4.0. Procedia Manufacturing, 11, 2195–2202.

    Article  Google Scholar 

  7. Boer, R. H., & Campos, C. P. (2019). A retrospective overview of International Collegiate programming contest data. Data Brief, 25, 104382.

    Article  Google Scholar 

  8. Chen, L., & Xia, X. (2019). Industrial IoT high-skilled talents training under the background of intelligent manufacturing. International Journal of Social Science and Education Research, 2(6), 46–50.

    Google Scholar 

  9. Cheng, Y. W., Sun, P. C., & Chen, N. S. (2018). The essential applications of educational robot: Requirement analysis from the perspectives of experts, researchers and instructors. Computers and Education, 126, 399–416.

    Article  Google Scholar 

  10. Cruz-Martín, A., Fernández-Madrigal, J. A., Galindo, C., González-Jiménez, J., Stockmans-Daou, C., & Blanco-Claraco, J. L. (2012). A LEGO Mindstorms NXT approach for teaching at data acquisition, control systems engineering and real-time systems undergraduate courses. Computers and Education, 59(3), 974–988.

    Article  Google Scholar 

  11. Drath, R., & Horch, A. (2014). Industrie 4.0: Hit or hype? IEEE Industrial Electronics Magazine, 8(2), 56–58.

    Article  Google Scholar 

  12. Egger, J., & Masood, T. (2020). Augmented reality in support of intelligent manufacturing—A systematic literature review. Computers and Industrial Engineering, 140, 106195.

    Article  Google Scholar 

  13. Ferguson, G. R., Bacila, I. A., & Swamy, M. (2016). Does current provision of undergraduate education prepare UK medical students in ENT? A systematic literature review. BMJ Open, 6(4), e010054.

    Article  Google Scholar 

  14. Ferrario, A., Confalonieri, M., Barni, A., Izzo, G., Landolfi, G., & Pedrazzoli, P. (2019). A multipurpose small-scale smart factory for educational and research activities. Procedia Manufacturing, 38, 663–670.

    Article  Google Scholar 

  15. Flynn, E. P., & Bach, C. (2019). Integrating advanced CAD modeling simulation, 3D printing, and manufacturing into higher education STEM courses. In Proceedings of the 2019 IEEE technology and engineering management conference (TEMSCON) (pp. 1–5).

  16. Gräßler, I., Pöhler, A., & Pottebaum, J. (2016). Creation of a learning factory for cyber physical production systems. Procedia CIRP, 54, 107–112.

    Article  Google Scholar 

  17. Greetham, M., & Ippolito, K. (2018). Instilling collaborative and reflective practice in engineers: Using a team-based learning strategy to prepare students for working in project teams. Higher Education Pedagogies, 3(1), 510–521.

    Article  Google Scholar 

  18. Hidayat, H. (2017). How to implement technology science for entrepreneurship by using product-based learning approach and participatory action learning system in higher education? Advanced Science Letters, 23(11), 10918–10921.

    Article  Google Scholar 

  19. Holmegaard, H. T., Madsen, L. M., & Ulriksen, L. (2016). Where is the engineering I applied for? A longitudinal study of students’ transition into higher education engineering, and their considerations of staying or leaving. European Journal of Engineering Education, 41(2), 154–171.

    Article  Google Scholar 

  20. Hughes, J., Shimizu, M., & Visser, A. (2019). A review of robot rescue simulation platforms for robotics education. In Robot world cup (pp. 86–98). Springer.

  21. Karabulut-Ilgu, A., Jaramillo, C. N., & Jahren, C. T. (2018). A systematic review of research on the flipped learning method in engineering education. British Journal of Educational Technology, 49(3), 398–411.

    Article  Google Scholar 

  22. Kurth, M., Schleyer, C., & Feuser, D. (2017). Smart factory and education: An integrated automation concept. International Journal of Service and Computing Oriented Manufacturing, 3(1), 43–53.

    Article  Google Scholar 

  23. Kwon, H., Berisha, V., Atti, V., & Spanias, A. (2009). Experiments with sensor motes and Java-DSP. IEEE Transactions on Education, 52(2), 257–262.

    Article  Google Scholar 

  24. Lai, Z. H., Tao, W., Leu, M. C., & Yin, Z. (2020). Smart augmented reality instructional system for mechanical assembly towards worker-centered intelligent manufacturing. Journal of Manufacturing Systems, 55, 69–81.

    Article  Google Scholar 

  25. Lenz, J., Macdonald, E., Harik, R., & Wuest, T. (2020). Optimizing smart manufacturing systems by extending the smart products paradigm to the beginning of life. Journal of Manufacturing Systems, 57, 274–286.

    Article  Google Scholar 

  26. Li, J., Yao, Y., & Wu, J. (2011). CNC partner: A novel training system for NC machining. Computer Applications in Engineering Education, 19(3), 466–474.

    Article  Google Scholar 

  27. Li, Q. (2021). The use of artificial intelligence combined with cloud computing in the design of education information management platform. International Journal of Emerging Technologies in Learning, 15(5), 32–44.

    Article  Google Scholar 

  28. Lou, S. J., Dzan, W. Y., Lee, C. Y., & Chung, C. C. (2014). Learning effectiveness of applying TRIZ-integrated BOPPPS*. International Journal of Engineering Education, 30(5), 1303–1312.

    Google Scholar 

  29. Mears, L., Omar, M., & Kurfess, T. R. (2011). Automotive engineering curriculum development: Case study for Clemson University. Journal of Intelligent Manufacturing, 22, 693–708.

    Article  Google Scholar 

  30. Meng, J., Wang, S., Li, G., Jiang, L., Zhang, X., Liu, C., & Xie, Y. (2021). Iterative-learning error compensation for autonomous parking of mobile manipulator in harsh industrial environment. Robotics and Computer-Integrated Manufacturing, 68, 102077.

    Article  Google Scholar 

  31. Oztemel, E., & Gursev, S. (2020). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31, 127–182.

    Article  Google Scholar 

  32. Pacaux-Lemoine, M. P., Trentesaux, D., Rey, G. Z., & Millot, P. (2017). Designing intelligent manufacturing systems through human–machine cooperation principles: A human-centered approach. Computers and Industrial Engineering, 111, 581–595.

    Article  Google Scholar 

  33. Pan, M., Wang, J., & Luo, Z. (2018). Modelling study on learning affects for classroom teaching/learning auto-evaluation. Science, 6(3), 81–86.

    Google Scholar 

  34. Pavlin, S. (2016). Considering university-business cooperation modes from the perspective of enterprises. European Journal of Education, 51(1), 25–39.

    Article  Google Scholar 

  35. Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers and Education, 147, 103778.

    Article  Google Scholar 

  36. Rehman, M. H., Yaqoob, I., Salah, K., Imran, M., Jayaraman, P. P., & Perera, C. (2019). The role of big data analytics in industrial Internet of Things. Future Generation Computer Systems, 99, 247–259.

    Article  Google Scholar 

  37. Saif, U., Guan, Z., Wang, C., He, C., Yue, L., & Mirza, J. (2019). Drum buffer rope-based heuristic for multi-level rolling horizon planning in mixed model production. International Journal of Production Research, 57(12), 3864–3891.

    Article  Google Scholar 

  38. Salah, B., Abidi, M. H., Mian, S. H., Krid, M., Alkhalefah, H., & Abdo, A. (2019). Virtual reality-based engineering education to enhance manufacturing sustainability in industry 4.0. Sustainability, 11(5), 1477.

    Article  Google Scholar 

  39. Sodhro, A. H., Pirbhulal, S., & Albuquerque, V. H. C. (2019). Artificial intelligence-driven mechanism for edge computing-based industrial applications. IEEE Transactions on Industrial Informatics, 15(7), 4235–4243.

    Article  Google Scholar 

  40. State Council of the People’s Republic of China. (2015). “Made in China 2025” plan unveiled. Retrieved May 5, 2020, from http://www.gov.cn/zhengce/content/2015-05/19/content_9784.htm. (in Chinese)

  41. Sun, W. T., & Wang, L. P. (2017). On the current mode of applied talents cultivation in the higher education institutions-oriented toward the industry 4.0 and China’s intelligent manufacturing. Frontiers, 2017(22), 20.

    Google Scholar 

  42. Tejedor, G., Segalàs, J., & Rosas-Casals, M. (2018). Transdisciplinarity in higher education for sustainability: How discourses are approached in engineering education. Journal of Cleaner Production, 175, 29–37.

    Article  Google Scholar 

  43. Toivonen, V., Lanz, M., Nylund, H., & Nieminen, H. (2018). The FMS Training Center-a versatile learning environment for engineering education. Procedia Manufacturing, 23, 135–140.

    Article  Google Scholar 

  44. Troussas, C., Krouska, A., & Sgouropoulou, C. (2020). Collaboration and fuzzy-modeled personalization for mobile game-based learning in higher education. Computers and Education, 144, 103698.

    Article  Google Scholar 

  45. Wang, B. C., Tao, F., Fang, X. D., Liu, C., Liu, Y. F., & Freiheit, T. (2020) Smart manufacturing and intelligent manufacturing: A comparative review. Engineering https://doi.org/10.1016/j.eng.2020.07.017

  46. Wang, S., Jiang, L., Meng, J., Xie, Y., & Ding, H. (2021). Training for smart manufacturing using a mobile robot-based production line. Frontiers of Mechanical Engineering, 66, 1–22.

    Google Scholar 

  47. White House Office of the Press. (2011). President Obama launches Advanced Manufacturing Partnership. Retrieved May 5, 2020, from. https://obamawhitehouse.archives.gov/the-press-office/2011/06/24/presidentobama-launches-advanced-manufacturing-partnership

  48. Yang, L., & McCall, B. (2014). World education finance policies and higher education access: A statistical analysis of World Development Indicators for 86 countries. International Journal of Educational Development, 35, 25–36.

    Article  Google Scholar 

  49. Yang, Y., & Sun, J. (2013). Study on pedestrian red-time crossing behavior: Integrated field observation and questionnaire data. Transportation Research Record, 2393(1), 117–124.

    Article  Google Scholar 

  50. Yao, D., Zhou, Z., & Zhu, Y. (2018). Metalworking practice of the teaching reform. Education Research Frontier, 8(1), 16–18.

    Google Scholar 

  51. Yao, X., Zhou, J., Lin, Y., Li, Y., Yu, H., & Liu, Y. (2019). Smart manufacturing based on cyber-physical systems and beyond. Journal of Intelligent Manufacturing, 30(8), 2805–2817.

    Article  Google Scholar 

  52. Yen, J. N., Chen, H. H., Chen, L. H., Hsu, H. C., & Lee, Y. C. (2018). Intelligent manufacturing impact of vocational high school education through industrial-academic cooperation plan. International Journal of Electrical Engineering Education. https://doi.org/10.1177/0020720918791424

    Article  Google Scholar 

  53. Yip, J., Wong, S. H., Yick, K. L., Chan, K., & Wong, K. H. (2019). Improving quality of teaching and learning in classes by using augmented reality video. Computers and Education, 128, 88–101.

    Article  Google Scholar 

  54. Zhang, X., Ming, X., Liu, Z., Yin, D., & Chen, Z. (2019). A reference system of smart manufacturing talent education (SMTE) in China. The International Journal of Advanced Manufacturing Technology, 100(9–12), 2701–2714.

    Article  Google Scholar 

  55. Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: A review. Engineering, 3(5), 616–630.

    Article  Google Scholar 

  56. Zhou, J., Li, P., Zhou, Y., Wang, B., Zang, J., & Meng, L. (2018). Toward new-generation intelligent manufacturing. Engineering, 4(1), 11–20.

    Article  Google Scholar 

  57. Zhou, J., Zhou, Y., Wang, B., & Zang, J. (2019). Human–cyber–physical systems (HCPSs) in the context of new-generation intelligent manufacturing. Engineering, 5(4), 624–636.

    Article  Google Scholar 

Download references

Funding

The work was supported in part by the “New Engineering” Research and Practice Project, China (Grant No. E-ZNZZ20201214) and Provincial Teaching Research Project of Higher Education in Hubei Province (Grant No. 2020090).

Author information

Affiliations

Authors

Contributions

Shuting Wang: Conceptualization, Methodology, Original draft preparation; Jie Meng: Methodology, Original draft preparation; Yuanlong Xie: Reviewing and Editing, Funding acquisition; Liquan Jiang: Investigation, Visualization; Han Ding: Supervision, Resources; Xinyu Shao: Reviewing and Editing, Funding acquisition.

Corresponding author

Correspondence to Yuanlong Xie.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1

See Fig. 28.

Fig. 28
figure28

The developed courses for IM engineering curricula

Appendix 2

See Table 5.

Table 5 Teaching scheme of engineering training practice course

Appendix 3

See Table 6.

Table 6 Scores of three representative schools

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, S., Meng, J., Xie, Y. et al. Reference training system for intelligent manufacturing talent education: platform construction and curriculum development. J Intell Manuf (2021). https://doi.org/10.1007/s10845-021-01838-4

Download citation

Keywords

  • Intelligent manufacturing talent education
  • Education platform
  • Curriculum development
  • Hands-on practice
  • Higher engineering education