Optimization of micro milling of hardened steel with different grain sizes using multi-objective evolutionary algorithm |
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| Affiliation: | 1. Dept. of Mechanical Engineering, University of Aveiro, Campus Santiago, 3810-193 Aveiro, Portugal;2. Federal University of São João del Rei, Department of Mechanical Engineering, Center for Innovation in Sustainable Manufacturing, São João del Rei, Brazil;3. Federal Institute of Technological Education of Minas Gerais, Rua Bernardo Mascarenhas, 1283 Juiz de Fora, Brazil;1. Physics Department, Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia;2. Physics Department, Faculty of Mathematics and Science, The State University of Malang, Malang, Indonesia;1. Department of Biomedical Engineering, National Cheng Kung University, 1 University Road, Tainan City 701, Taiwan;2. Musculoskeletal Research Center, National Cheng Kung University, 1 University Road, Tainan City 701, Taiwan;3. Department of Occupational Therapy, National Cheng Kung University, 1 University Road, Tainan City 701, Taiwan;4. Department of Orthopedics, National Cheng Kung University Hospital, 138 Sheng Li Road, Tainan City 701, Taiwan;5. Medical Device Innovation Center, National Cheng Kung University, 1 University Road, Tainan City 701, Taiwan;1. Fraunhofer Institute for Production Technology IPT, Germany;2. Laboratory for Machine Tools and Production Engineering (WZL) at RWTH Aachen University, Germany;1. Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China;2. Institute of Advanced Manufacturing Technology, Hefei Institutes of Physical Science, Chinese Academy of Science, Huihong Building, Changwu Middle Road 801, Changzhou, 213164 Jiangsu, China;3. School of Logistics Engineering, Wuhan University of Technology, Heping Road 1178#, Wuhan, 430063 Hubei, China;1. Centre of Advanced Manufacturing and Material Processing (AMMP), Department of Mechanical Engineering, Engineering Faculty, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. Department of Mechanical Engineering, Faculty of Engineering, Assiut University, Assiut 71516, Egypt |
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| Abstract: | Modern manufacturing processes need high production rates, low costs, and high product quality. Generally, surface roughness is a good reference to determine the performance in machined products. The use of optimization systems can determine the optimum machining parameters in the machining process, especially in milling operations. The present study integrates the least square model based on feed rate, cutting speed, and grain size with a genetic optimization algorithm to provide the optimal process parameter. The NSGA II algorithm was applied due to its coverage and easily to optimize the micro milling of hardened steel. The responses were Fy Force and Mz Torque. The results show that the feed rate was the most significant factor for minimizing Fy force and Mz Torque. |
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| Keywords: | Genetic algorithm Micro milling Least-square method Grain size |
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