Development of hybrid intelligent models for prediction machining performance measure in end milling of Ti6Al4V alloy with PVD coated tool under dry cutting conditions

Ti6Al4V alloy is widely used in aerospace and medical applications. It is classified as a difficult to machine material due to its low thermal conductivity and high chemical reactivity. In this study, hybrid intelligent models have been developed to predict surface roughness when end milling Ti6Al4V alloy with a Physical Vapor Deposition PVD coated tool under dry cutting conditions. Back propagation neural network (BPNN) has been hybridized with two heuristic optimization techniques, namely: gravitational search algorithm (GSA) and genetic algorithm (GA). Taguchi method was used with an L27 orthogonal array to generate 27 experiment runs. Design expert software was used to do analysis of variances (ANOVA). The experimental data were divided randomly into three subsets for training, validation, and testing the developed hybrid intelligent model. ANOVA results revealed that feed rate is highly affected by the surface roughness followed by the depth of cut. One-way ANOVA, including a Post-Hoc test, was used to evaluate the performance of three developed models. The hybrid model of Artificial Neural Network-Gravitational Search Algorithm (ANN-GSA) has outperformed Artificial Neural Network (ANN) and Artificial Neural Network-Genetic Algorithm (ANN-GA) models. ANN-GSA achieved minimum testing mean square error of 7.41 × 10−13 and a maximum R-value of 1. Further, its convergence speed was faster than ANN-GA. GSA proved its ability to improve the performance of BPNN, which suffers from local minima problems.

Subjects

optimization

Ti6Al4V alloy

gravitational search algorithm

surface roughness

DDC Class

530: Physik

540: Chemie

600: Technik

620: Ingenieurwissenschaften

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

lubricants-10-00236-v2.pdf

Size

6.22 MB

Format

Adobe PDF

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Development of hybrid intelligent models for prediction machining performance measure in end milling of Ti6Al4V alloy with PVD coated tool under dry cutting conditions