Online multi-response assessment using Taguchi and artificial neural network

Abstract

Engineering problems often embody many characteristics of a multi-response optimisation problem, and these responses are often conflicting in nature. To address this issue, this work uses grey-based Taguchi method to express surface roughness of drilled holes and drill flank wear into an equivalent single response. Experiments have been conducted in a radial drilling machine with five input parameters using L27 orthogonal array. It has been observed that combined response of flank wear and surface roughness is affected by almost all input parameters; however, drill diameter is statistically most significant. The prediction results obtained via. Taguchi method is compared with Back Propagation Neural Network (BPNN).