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Application of optimized convolutional neural network to fixture layout in automotive parts

International journal of advanced manufacturing technology, 2023-05, Vol.126 (1-2), p.339-353 [Peer Reviewed Journal]

The Author(s) 2023 ;The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0268-3768 ;ISSN: 1433-3015 ;EISSN: 1433-3015 ;DOI: 10.1007/s00170-023-10995-0

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  • Title:
    Application of optimized convolutional neural network to fixture layout in automotive parts
  • Author: Villena Toro, Javier ; Wiberg, Anton ; Tarkian, Mehdi
  • Subjects: Artificial neural networks ; Automation ; Automotive parts ; CAE) and Design ; Computer-Aided Engineering (CAD ; Engineering ; Fixtures ; Industrial and Production Engineering ; Industrial applications ; Layouts ; Machine learning ; Mechanical Engineering ; Media Management ; Metal sheets ; Neural networks ; Optimization ; Original Article ; Principles ; Production costs ; Supervised learning ; Topographic maps ; Workpieces
  • Is Part Of: International journal of advanced manufacturing technology, 2023-05, Vol.126 (1-2), p.339-353
  • Description: Fixture layout is a complex task that significantly impacts manufacturing costs and requires the expertise of well-trained engineers. While most research approaches to automating the fixture layout process use optimization or rule-based frameworks, this paper presents a novel approach using supervised learning. The proposed framework replicates the 3-2-1 locating principle to layout fixtures for sheet metal designs. This principle ensures the correct fixing of an object by restricting its degrees of freedom. One main novelty of the proposed framework is the use of topographic maps generated from sheet metal design data as input for a convolutional neural network (CNN). These maps are created by projecting the geometry onto a plane and converting the Z coordinate into gray-scale pixel values. The framework is also novel in its ability to reuse knowledge about fixturing to lay out new workpieces and in its integration with a CAD environment as an add-in. The results of the hyperparameter-tuned CNN for regression show high accuracy and fast convergence, demonstrating the usability of the model for industrial applications. The framework was first tested using automotive b-pillar designs and was found to have high accuracy (≈ 100 % ) in classifying these designs. The proposed framework offers a promising approach for automating the complex task of fixture layout in sheet metal design.
  • Publisher: London: Springer London
  • Language: English
  • Identifier: ISSN: 0268-3768
    ISSN: 1433-3015
    EISSN: 1433-3015
    DOI: 10.1007/s00170-023-10995-0
  • Source: SWEPUB Freely available online
    ProQuest Central
    Springer Nature OA Free Journals
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