Journal
SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES
Volume 39, Issue 3, Pages 533-548Publisher
SPRINGER INDIA
DOI: 10.1007/s12046-014-0244-7
Keywords
Particle swarm optimization; artificial neural network; back propagation algorithm; flank wear; drilling
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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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