Multi-attribute decision making for green electrical discharge machining |
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| Authors: | SP Sivapirakasam Jose Mathew M Surianarayanan |
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| Affiliation: | 1. Department of Mechanical Engineering, National Institute of Technology, Tiruchirappalli, India;2. CISRA, Central Leather Research Institute, Chennai, India;1. GNA University, Phagwara, Punjab, India;2. Electrical Department, Synergy Institute of Engineering & Technology, Dhenkanal, Odisha;1. Department of Mechanical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat 395007, India;2. Department of Mechanical Engineering, Indian Institute of Technology, Tirupati, Andhra Pradesh 517506, India;1. Department of Mechanical Engineering, Sethu Institute of Technology, Pulloor 626 115, Kariapatti, Virudhunagar District, Tamil Nadu, India;2. Department of Mechanical Engineering, Anna University, Regional Campus Madurai, Madurai 625 019, Tamil Nadu, India;3. Centre for Materials Research, Department of Mechanical Engineering, Sethu Institute of Technology, Pulloor 626 115, Kariapatti, Virudhunagar District, Tamil Nadu, India;1. College of Mechanical and Electronic Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, China;2. Centre for Micro-Photonics, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne 3122, Australia |
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| Abstract: | This paper aims to develop a combination of Taguchi and fuzzy TOPSIS methods to solve multi-response parameter optimization problems in green manufacturing. Electrical Discharge Machining (EDM), a commonly used non-traditional manufacturing process was considered in this study. A decision making model for the selection of process parameters in order to achieve green EDM was developed. An experimental investigation was carried out based on Taguchi L9 orthogonal array to analyze the sensitivity of green manufacturing attributes to the variations in process parameters such as peak current, pulse duration, dielectric level and flushing pressure. Weighing factors for the output responses were determined using triangular fuzzy numbers and the most desirable factor level combinations were selected based on TOPSIS technique. The model developed in this study can be used as a systematic framework for parameter optimization in environmentally conscious manufacturing processes. |
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