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Methodology for complexity and cost comparison between subtractive and additive manufacturing processes

Author

Listed:
  • S. Touzé

    (Institut de Recherche en Génie Civil et Mécanique (GeM), UMR CNRS 6183, Ecole Centrale de Nantes)

  • M. Rauch

    (Institut de Recherche en Génie Civil et Mécanique (GeM), UMR CNRS 6183, Ecole Centrale de Nantes)

  • J.-Y. Hascoët

    (Institut de Recherche en Génie Civil et Mécanique (GeM), UMR CNRS 6183, Ecole Centrale de Nantes)

Abstract

This works presents a methodology, along with its software implementation called “Design 2 Cost”, for evaluating the manufacturing cost and complexity of a part built by a subtractive (e.g. milling) or additive (e.g. laser metal deposition, Selective laser melting, wire-arc additive manufacturing) process. The overall manufacturing complexity is calculated as a weighted average of morphological and material criteria, which are defined either locally or globally. The local morphological criteria are calculated over the leaf nodes of an octree representation of the part using a raycasting technique. This allows to efficiently probe the local geometry of the part and compare it with manufacturing constraints emanating from the manufacturing process and its associated effector. This algorithm yields a cartography of the local complexity criteria that helps visualizing the problematic regions for the processes under consideration. The software is accompanied by a database that feeds the required material and process properties needed for the calculation of the manufacturing complexity and cost. The proposed methodology therefore permits a technical and economic comparison of manufacturing processes for a given geometry and material, as well as a comparison of various part geometries and materials for a given manufacturing process.

Suggested Citation

  • S. Touzé & M. Rauch & J.-Y. Hascoët, 2024. "Methodology for complexity and cost comparison between subtractive and additive manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 35(2), pages 555-574, February.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:2:d:10.1007_s10845-022-02059-z
    DOI: 10.1007/s10845-022-02059-z
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    References listed on IDEAS

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    1. Sheng Yang & Thomas Page & Ying Zhang & Yaoyao Fiona Zhao, 2020. "Towards an automated decision support system for the identification of additive manufacturing part candidates," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1917-1933, December.
    2. Jingchao Jiang & Yi Xiong & Zhiyuan Zhang & David W. Rosen, 2022. "Machine learning integrated design for additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1073-1086, April.
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