Multi-Objective Approach to Automated Fixture Synthesis Incorporating Deep Neural Network for Deformation Evaluation |
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| Affiliation: | 1. School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China;2. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China;3. Mechanical Engineering Department, Sana''a University, Sana''a 31220, Yemen;1. Advanced Remanufacturing and Technology Centre (ARTC), A*STAR, 3 Cleantech Loop, 637143, Singapore;2. School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore;3. Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou, 510070, China;4. Singapore Institute of Manufacturing Technology (SIMTech), A*STAR, 5 Cleantech Loop, 636732, Singapore;1. College of Engineering and Physical Sciences, Aston University, Birmingham, B47ET, UK;2. Department of Mechanical Engineering, The University of Auckland, Auckland, 1010, New Zealand;3. Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH 44106, USA;4. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden;5. Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW), University of Stuttgart, Stuttgart, 70174, Germany;1. School of Mechanical Engineering, Tongji University, Shanghai 201804, China;2. Siemens Technology, Siemens Ltd., China;1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China;2. State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, China;1. School of Mechanical Engineering, Shandong University, Jinan 250061, PR China;2. Key Laboratory of High Efficiency and Clean Mechanical Manufacture at Shandong University, Ministry of Education, Jinan 250061, PR China;1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China;2. Department of Mechanical and Mechatronics Engineering, The University of Auckland, Auckland, 1010, New Zealand;3. Department of Aerospace Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada;4. Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai, 200072, China |
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| Abstract: | In machining, the synthesis of a fixturing schema significantly impacts the accuracy of the final product. Moreover, A robust and automatic configuration of fixture elements can reduce production costs and eliminate the need for expert labor to perform the task. Given the multi-modal problem of fixture synthesis, this article presents a multi-objective approach to fixture synthesis in the discrete domain. The performance criteria are localization accuracy, detachment of locators, workpiece deformation, severity and dispersion of reaction loads, and the spacing between contact points. Optimization is performed via an improved Declining Neighborhood Simulated Annealing algorithm (DNSA). To achieve consistent performance over different inputs, the number of iterations follows a Shanon entropy index reflecting the recurrence of folds/corners. Except for deformation, all other objectives are derived from the kinematic analysis of the workpiece-fixtures system. In contrast, deformation is estimated via a Constitutive Deep Neural Network (CDNN). Both models incorporate the machining loads as quasi-static intervals. A new strategy is adopted for the trade-off based on the Z-score quantification of objectives through a pre-calibration run of DNSA. Numerical examples demonstrate the implementation flow of our generalized CAD-based tool developed for the purpose. The approach is verified and proved efficient in automating the robust selection of a fixture layout for a prismatic workpiece. |
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