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Machining performance optimization during plasma arc cutting of AISI D2 steel: application of FIS, nonlinear regression and JAYA optimization algorithm

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Abstract

The work focuses on assessing the optimal machining conditions which could simultaneously satisfy multiple process performance indices during machining of AISI D2 steel. The main characteristic indices that have been considered here for evaluating plasma arc machining are surface roughness and material removal rate; the corresponding machining parameters are cutting speed, gas pressure and torch height. The study proposes an integrated optimization module combining fuzzy inference system, nonlinear regression and JAYA algorithm towards optimizing correlated multi-response features during machining of AISI D2 steel. Optimum value of machining parameters found as cutting speed of 4000 m/min, gas pressure of 95 psi and torch height of 0.5 mm using aforementioned methodology. Application potential of the aforesaid integrated optimization route has been compared to that of teaching–learning based optimization (TLBO) algorithm and genetic algorithm. It has been concluded that JAYA algorithm possesses less convergence time and hence execution is faster as compared to TLBO and genetic algorithm.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, Gujarat, 380026, India

    Parthkumar Patel, Bhavdeep Nakum & Kumar Abhishek

  2. Department of Mechanical Engineering, National Institute of Technology, Rourkela, 769008, India

    V. Rakesh Kumar

Authors
  1. Parthkumar Patel
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  2. Bhavdeep Nakum
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  3. Kumar Abhishek
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  4. V. Rakesh Kumar
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Correspondence to Kumar Abhishek.

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Technical Editor: Márcio Bacci da Silva.

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Patel, P., Nakum, B., Abhishek, K. et al. Machining performance optimization during plasma arc cutting of AISI D2 steel: application of FIS, nonlinear regression and JAYA optimization algorithm. J Braz. Soc. Mech. Sci. Eng. 40, 240 (2018). https://doi.org/10.1007/s40430-018-1087-7

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  • DOI: https://doi.org/10.1007/s40430-018-1087-7

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