Title: An application of artificial neural network and particle swarm optimisation technique for modelling and optimisation of centreless grinding process

Authors: Prosun Mandal; Subhas Chandra Mondal

Addresses: Department of Mechanical Engineering, Indian Institute of Engineering Science and Technology, Shibpur, Howrah-711103, West-Bengal, India ' Department of Mechanical Engineering, Indian Institute of Engineering Science and Technology, Shibpur, Howrah-711103, West-Bengal, India

Abstract: Centreless grinding operation is widely used in manufacturing industry for its high level of accuracy and micro-finishing of shaft, pin material. This paper presents an application of artificial neural network and particle swarm optimisation techniques for modelling and optimisation of centreless grinding operation for machining of C40 steel crane-hook-pin. Full factorial design is used taking three factors at three levels each and a total 3³ or 27 number of experiments are done in all possible combination of factors. The performance of this particular trained neural network has been tested with the experimental data and found satisfactory. Thus the proposed ANN model is efficiently used for predicting surface roughness in centreless grinding operation. Particle swarm optimisation technique combining with response surface modelling are used to find optimal parameter settings. The optimal parameter corresponds to 12 rpm regulating wheel speed, 1/3rd opening of coolant valve opening and 20.002 µm depth of cut.

Keywords: centreless grinding; crane-hook-pin; surface roughness; surface quality; artificial neural networks; ANNs; particle swarm optimisation; PSO; metaheuristics; swarm intelligence; modelling; C40 steel; full factorial design; response surface methodology; RSM; grinding wheel speed; coolant valve opening; depth of cut.

DOI: 10.1504/IJPQM.2017.082637

International Journal of Productivity and Quality Management, 2017 Vol.20 No.3, pp.344 - 362

Received: 13 Jun 2015
Accepted: 06 Aug 2015
Published online: 04 Mar 2017 *

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