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Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm
Authors:Wen-Hsien Ho  Jinn-Tsong Tsai  Bor-Tsuen Lin  Jyh-Horng Chou
Affiliation:1. Department of Medical Information Management, Kaohsiung Medical University, 100 Shin-Chuan 1st Road, Kaohsiung 807, Taiwan, ROC;2. Department of Computer Science, National Pingtung University of Education, 4-18 Ming Shen Road, Pingtung 900, Taiwan, ROC;3. Institute of Engineering Science and Technology, National Kaohsiung First University of Science and Technology, 1 University Road, Yenchao, Kaohsiung 824, Taiwan, ROC;1. Institute of Research and Development, Duy Tan University, 03 Quang Trung, Da Nang 550000, Viet Nam;2. Faculty of Mechanical Engineering, Le Quy Don Technical University, 236 Hoang Quoc Viet, Ha Noi 100000, Viet Nam;1. Soft Computing Research Group, Faculty of Computing, Universiti Teknologi Malaysia, UTM, 81310 Skudai, Johor, Malaysia;2. Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, UTM, 81310 Skudai, Johor, Malaysia;1. Opole University of Technology, 76 Proszkowska St., 45-758 Opole, Poland;2. Faculty of Mechanical Engineering, University of Zielona Gora, 4 Prof. Z. Szafrana Street, 65-516 Zielona Gora, Poland;3. Faculty of Mechanical Engineering and Management, Poznan University of Technology, 3 Piotrowo St., 60-965 Poznan, Poland;1. School of Integrated Design Engineering, Faculty of Science and Technology, Keio University, Yokohama, Japan;2. Department of Mechanical and Industrial Engineering, Faculty of Engineering, Gadjah Mada University, Yogyakarta, Indonesia;3. Ho Chi Minh City University of Food Industry, Ho Chi Minh City, Viet Nam;4. Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia;1. Department of Mechanical Engineering, National University of Singapore, Singapore;2. Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117576, Singapore;3. Department of Mechanical Engineering, National University of Singapore, Singapore;4. Department of Mechanical Engineering, National University of Singapore, Singapore
Abstract:In this paper, an adaptive network-based fuzzy inference system (ANFIS) with the genetic learning algorithm is used to predict the workpiece surface roughness for the end milling process. The hybrid Taguchi-genetic learning algorithm (HTGLA) is applied in the ANFIS to determine the most suitable membership functions and to simultaneously find the optimal premise and consequent parameters by directly minimizing the root-mean-squared-error performance criterion. Experimental results show that the HTGLA-based ANFIS approach outperforms the ANFIS methods given in the Matlab toolbox and reported recently in the literature in terms of prediction accuracy.
Keywords:
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