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New requirements of the increasingly digitalized world of work have an impact on modern engineering training. In view of these considerations, the aim of the present work is to integrate artificial intelligence (AI) for error detection into existing practical exercises for product development, taking sustainability aspects into account. The basis of the concept is the use of the smart factory to convey technical content in its real function, control, maintenance and repair, as well as 3D printing with the introduction of AI to detect defects in the manufacturing process (using cameras). This article examines the following research question: What methodologies to foster students’ digital competences are needed to enable it to form the basis of interdisciplinary team project activities of engineering students. At the centre of this investigation is the synergy between traditional teaching approaches and modern technologies to create an interactive and practice-oriented learning environment. The application scenario is the use of AI to design work process descriptions with the recognition of their limits: the optical quality inspection. The aim is to develop and test the teaching/learning modules with a focus on Industry 4.0 and AI-supported generative manufacturing with sustainability-oriented quality assurance. COMET-based competence measurement is offered as an additional instrument for recording the competence of teachers. The expectation of the concept advertised here is to test the implementation of the measure in the modules for the teaching degree program in industrial-technical subjects (GTF) and to gather experience for implementation on a larger scale. The preliminary studies to date promise a successful approach to the research question of designing digital skills development. Further work, in particular trialing interdisciplinary group work and the integration of real-time learning settings, is required to improve the results to date. The integration of further AI-based learning tools can also be realized in future work.

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Development of a Learning Unit for AI-Supported Additive Manufacturing with Sustainability-Orientated Quality Assurance

  • 会议论文

  • 发表日期：

    2025年

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  • Natali Becker,
  • Andrea Dederichs-Koch,
  • Ralph Dreher

摘要

New requirements of the increasingly digitalized world of work have an impact on modern engineering training. In view of these considerations, the aim of the present work is to integrate artificial intelligence (AI) for error detection into existing practical exercises for product development, taking sustainability aspects into account. The basis of the concept is the use of the smart factory to convey technical content in its real function, control, maintenance and repair, as well as 3D printing with the introduction of AI to detect defects in the manufacturing process (using cameras). This article examines the following research question: What methodologies to foster students’ digital competences are needed to enable it to form the basis of interdisciplinary team project activities of engineering students. At the centre of this investigation is the synergy between traditional teaching approaches and modern technologies to create an interactive and practice-oriented learning environment. The application scenario is the use of AI to design work process descriptions with the recognition of their limits: the optical quality inspection. The aim is to develop and test the teaching/learning modules with a focus on Industry 4.0 and AI-supported generative manufacturing with sustainability-oriented quality assurance. COMET-based competence measurement is offered as an additional instrument for recording the competence of teachers. The expectation of the concept advertised here is to test the implementation of the measure in the modules for the teaching degree program in industrial-technical subjects (GTF) and to gather experience for implementation on a larger scale. The preliminary studies to date promise a successful approach to the research question of designing digital skills development. Further work, in particular trialing interdisciplinary group work and the integration of real-time learning settings, is required to improve the results to date. The integration of further AI-based learning tools can also be realized in future work.