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An autonomous intelligent framework for optimal orientation detection in 3D printing

Rezaei, M. R ; Sharif University of Technology | 2023

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  1. Type of Document: Article
  2. DOI: 10.1080/0951192X.2022.2162587
  3. Publisher: Taylor and Francis Ltd , 2023
  4. Abstract:
  5. By intense growth in the field of 3D printing, new opportunities for manufacturing firms and individuals have been created to benefit from the flexibility in production and personalized products. However, the need for human agents for additive process planning and control results in more operating costs and less agility in 3D printing. So, it is necessary to have autonomous mechanisms for fulfilling the additive process planning. This paper has benefited from the recent advancements of artificial intelligence to create a basis for an autonomous system which decides upon the build orientation of the shape, based on the previously encountered occurrences of parts which were similar to its parts. This creates a flexible structure that is able to conduct the autonomous process planning for new shapes by using information from simpler shapes and decide the orientation by the consolidation of simpler shape scenarios. The proposed solution decomposes the shape, recognizes the types of parts using previous data, determines the orientation of the parts and using knowledge from previous cases for fulfillment of fabrication objectives, an orientation for printing is suggested. The mechanism and the structure of the proposed framework has been analyzed and discussed through illustrative cases. © 2023 Informa UK Limited, trading as Taylor & Francis Group
  6. Keywords:
  7. 3D printing ; Artificial intelligence in 3D printing ; Convolutional neural networks ; Fabrication orientation ; Fabrication orientation determination in 3D printing
  8. Source: International Journal of Computer Integrated Manufacturing ; Volume 36, Issue 6 , 2023 , Pages 908-946 ; 0951192X (ISSN)
  9. URL: https://www.tandfonline.com/doi/abs/10.1080/0951192X.2022.2162587