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Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling

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  • J. Apolinar Muñoz Rodríguez

    (Centro de Investigaciones en Óptica, A. C., Lomas del Bosque 115, Col. Comas del Campestre, Leon 37000, GTO, Mexico)

Abstract

Industry 4.0 represents high-level methodologies to make intelligent, autonomous, and self-adaptable manufacturing systems. Additionally, the surface modeling technology has become a great tool in industry 4.0 for representing the surface point cloud. Thus, the micro-scale machining technology requires efficient models to represent micro-scale flat and free-form surfaces. Therefore, it is fundamental to perform surface modeling through artificial intelligence for representing small surfaces. This study addressed multi-objective optimization via genetic algorithms and micro laser line projection to accomplish surface models for representing micro-scale flat and free-form surfaces, where an optical microscope system retrieves micro-scale topography via micro laser line coordinates and the multi-objective optimization constructs the flat and free-form surface models through genetic algorithms and micro-scale topography. The multi-objective optimization determines the surface model parameters through exploration and exploitation, and the solution space is deduced via surface data. The surface model generated through the multi-objective optimization fit accurately to the micro-scale target surface. Thus, the proposed technique enhanced the fitting of micro-scale flat and free-form surface models, which were deduced via gray-level images of an optical microscope. This enhancement was validated by a discussion between the multi-objective optimization via genetic algorithms and the micro-scale surface modeling via optical microscope imaging systems.

Suggested Citation

  • J. Apolinar Muñoz Rodríguez, 2022. "Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling," Energies, MDPI, vol. 15(18), pages 1-23, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6571-:d:909973
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    References listed on IDEAS

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    1. Hietam Elhoone & Tianyang Zhang & Mohd Anwar & Salil Desai, 2020. "Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2841-2861, May.
    2. Andres Bustillo & Danil Yu. Pimenov & Mozammel Mia & Wojciech Kapłonek, 2021. "Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 895-912, March.
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    4. Tan, K.C. & Chiam, S.C. & Mamun, A.A. & Goh, C.K., 2009. "Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 197(2), pages 701-713, September.
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