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

Author

Listed:
  • Shuting Wang

    (Huazhong University of Science and Technology)

  • Jie Meng

    (Huazhong University of Science and Technology)

  • Yuanlong Xie

    (Huazhong University of Science and Technology)

  • Liquan Jiang

    (Huazhong University of Science and Technology)

  • Han Ding

    (Huazhong University of Science and Technology)

  • Xinyu Shao

    (Huazhong University of Science and Technology)

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.

Suggested Citation

  • Shuting Wang & Jie Meng & Yuanlong Xie & Liquan Jiang & Han Ding & Xinyu Shao, 2023. "Reference training system for intelligent manufacturing talent education: platform construction and curriculum development," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1125-1164, March.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01838-4
    DOI: 10.1007/s10845-021-01838-4
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    References listed on IDEAS

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    1. Bashir Salah & Mustufa Haider Abidi & Syed Hammad Mian & Mohammed Krid & Hisham Alkhalefah & Ali Abdo, 2019. "Virtual Reality-Based Engineering Education to Enhance Manufacturing Sustainability in Industry 4.0," Sustainability, MDPI, vol. 11(5), pages 1-19, March.
    2. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
    3. Marcus Kurth & Carsten Schleyer & Daniel Feuser, 2017. "Smart factory and education: an integrated automation concept," International Journal of Service and Computing Oriented Manufacturing, Inderscience Enterprises Ltd, vol. 3(1), pages 43-53.
    4. Barba-Sánchez, Virginia & Atienza-Sahuquillo, Carlos, 2018. "Entrepreneurial Intention Among Engineering Students: The Role Of Entrepreneurship Education," European Research on Management and Business Economics (ERMBE), Academia Europea de Dirección y Economía de la Empresa (AEDEM), vol. 24(1), pages 53-61.
    5. Xifan Yao & Jiajun Zhou & Yingzi Lin & Yun Li & Hongnian Yu & Ying Liu, 2019. "Smart manufacturing based on cyber-physical systems and beyond," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2805-2817, December.
    6. Ullah Saif & Zailin Guan & Chuangjian Wang & Cong He & Lei Yue & Jahanzaib Mirza, 2019. "Drum buffer rope-based heuristic for multi-level rolling horizon planning in mixed model production," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3864-3891, June.
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    1. Yuzhe Zhao & Jingmiao Zhou & Zhongxiu Peng & Zongyao Wang & Zunkuo Sheng, 2023. "A Big-Data-Based Experimental Platform for Green Shipping Monitoring and Its Teaching Application," Sustainability, MDPI, vol. 15(11), pages 1-19, May.

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