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Modelling and Optimization of End Milling Process Using TLBO and TOPSIS Algorithm: Modelling and Optimization of End Milling Process
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Author(s): Atul Tiwari (Maulana Azad National Institute of Technology, Bhopal, India)and Mohan Kumar Pradhan (Maulana Azad National Institute of Technology, Bhopal, India)
Copyright: 2017
Pages: 28
Source title:
Handbook of Research on Manufacturing Process Modeling and Optimization Strategies
Source Author(s)/Editor(s): Raja Das (VIT University, India)and Mohan Pradhan (Maulana Azad National Institute of Technology, Bhopal, India)
DOI: 10.4018/978-1-5225-2440-3.ch004
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Abstract
To assure desire quality of machined products at minimum machining costs and maximum material removal rate, it is very important to select optimum parameters when metal cutting machine tool are used. Minimum Surface Roughness (Ra) is commonly desirable for the component; however Material Removal Rate (MRR) should be maximized. This chapter presents an approach for determination of the best cutting parameters precede to minimum Ra and maximum MRR simultaneously by integrating Response Surface Methodology with Multi-Objective Technique for Order Preference by Similarity to Ideal Solution and Teaching and learning based optimization algorithm in face milling of Al-6061 alloy. 30 experiments have been conducted based on RSM with 4 parameters, namely Speed, Feed, Depth of Cut and Coolant Speed and three levels each. ANOVA is performed to find the most influential input parameters for both MRR and Ra. Later the multi-objective attribution selection method TOPSIS and multi objective optimization method TLBO is used to optimize the responses.
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