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This paper describes the energy saving approaches along the complete path from the CAD model to the final post process step of a multi-material component manufactured with DED, using a thermally optimized injection tool as an example. The study focuses in particular on the possible optimization approaches for necessary iterations caused by instabilities during the production of complex components. These are different from approaches used in conventional manufacturing processes, as the special characteristics of additive manufacturing using DED must be considered. Improvements are achieved through geometry-specific process parameter optimization, NC-toolpath optimization strategies and a process monitoring through melt pool analysis. For this purpose, an existing CCD camera is used to detect deviations by means of AI-based image segmentation and classification. As a result of the optimizations carried out, the production time and the number of iterations for a comparable component were significantly reduced and considerable energy savings of 53.4% were achieved.

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Identification of Key Challenges and Optimization Options Along the Process Chain for the Manufacturing of a Thermally Optimized Injection Molding Tool Using the DED Process

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Thore Gericke,
  • Moritz Jens,
  • Nik Schwichtenberg,
  • Alexander Mattes

摘要

This paper describes the energy saving approaches along the complete path from the CAD model to the final post process step of a multi-material component manufactured with DED, using a thermally optimized injection tool as an example. The study focuses in particular on the possible optimization approaches for necessary iterations caused by instabilities during the production of complex components. These are different from approaches used in conventional manufacturing processes, as the special characteristics of additive manufacturing using DED must be considered. Improvements are achieved through geometry-specific process parameter optimization, NC-toolpath optimization strategies and a process monitoring through melt pool analysis. For this purpose, an existing CCD camera is used to detect deviations by means of AI-based image segmentation and classification. As a result of the optimizations carried out, the production time and the number of iterations for a comparable component were significantly reduced and considerable energy savings of 53.4% were achieved.