Estimation of the minimum machining performance in the abrasive waterjet machining using integrated ANN-SA |
| |
| Authors: | Azlan Mohd Zain Habibollah Haron Safian Sharif |
| |
| Affiliation: | 1. Faculty of Computer Science & Information System, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia;2. Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia;1. Industrial Research Institute Swinburne, Swinburne University of Technology, P.O. Box 218, Hawthorn, Melbourne, Victoria 3122, Australia;2. School of Engineering and Science, Swinburne University of Technology, P.O. Box 218, Hawthorn, Melbourne, Victoria 3122, Australia;1. Department of Mechanical Engineering, University of Washington, Seattle, WA, United States;2. Flow International, Kent, WA, United States;1. G H Raisoni College of Engineering, Mechanical Engineering, Nagpur, Maharashtra, India;2. Indian Institute of Technology Kanpur, 4i-Laboratory, Kanpur, Uttar Pradesh, India;3. Visvesvaraya National Institute of Technology, Mechanical Engineering, Nagpur, Maharashtra, India;1. Department of Mechanical and Industrial Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, Canada M5B 2K3;2. Department of Mechanical and Industrial Engineering, University of Toronto, 5 King''s College Road, Toronto, ON, Canada M5S 3G8 |
| |
| Abstract: | In this study, Artificial Neural Network (ANN) and Simulated Annealing (SA) techniques were integrated labeled as integrated ANN-SA to estimate optimal process parameters in abrasive waterjet (AWJ) machining operation. The considered process parameters include traverse speed, waterjet pressure, standoff distance, abrasive grit size and abrasive flow rate. The quality of the cutting of machined-material is assessed by looking to the roughness average value (Ra). The optimal values of the process parameters are targeted for giving a minimum value of Ra. It was evidence that integrated ANN-SA is capable of giving much lower value of Ra at the recommended optimal process parameters compared to the result of experimental and ANN single-based modeling. The number of iterations for the optimal solutions is also decreased compared to the result of SA single-based optimization. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
