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Particle swarm optimization of a neural network model in a machining process

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Abstract

This paper presents a particle swarm optimization (PSO) technique to train an artificial neural network (ANN) for prediction of flank wear in drilling, and compares the network performance with that of the back propagation neural network (BPNN). This analysis is carried out following a series of experiments employing high speed steel (HSS) drills for drilling on mild steel workpieces, under different sets of cutting conditions and noting the root mean square (RMS) value of spindle motor current as well as the average flank wear in each case. The results show that the PSO trained ANN not only gives better prediction results and reduced computational times compared to the BPNN, it is also a more robust model, being free of getting trapped in local optimum solutions unlike the latter. Besides, it offers the advantages of a straight-forward logic, simple realization and underlying intelligence.

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of California, Berkeley, 94720, California, USA

    SAURABH GARG

  2. Department of Mechanical Engineering, Indian Institute of Technology Patna, Patna, 800 013, India

    KARALI PATRA

  3. Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721 302, India

    SURJYA K PAL

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  1. SAURABH GARG
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  2. KARALI PATRA
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Correspondence to KARALI PATRA.

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GARG, S., PATRA, K. & PAL, S.K. Particle swarm optimization of a neural network model in a machining process. Sadhana 39, 533–548 (2014). https://doi.org/10.1007/s12046-014-0244-7

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  • DOI: https://doi.org/10.1007/s12046-014-0244-7

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