relation: https://khub.utp.edu.my/scholars/20613/ title: Development of Hybrid Intelligent Models for Prediction Machining Performance Measure in End Milling of Ti6Al4V Alloy with PVD Coated Tool under Dry Cutting Conditions creator: Al-Zubaidi, Salah creator: A.Ghani, Jaharah creator: Che Haron, Che Hassan creator: Mohammed, M.N. creator: Jameel Al-Tamimi, Adnan Naji creator: M.Sarhan, Samaher creator: Salleh, Mohd Shukor creator: Abdulrazaq, M. creator: Abdullah, Oday I. description: 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. © 2022 by the authors. publisher: MDPI date: 2022 type: Article type: PeerReviewed identifier: Al-Zubaidi, Salah and A.Ghani, Jaharah and Che Haron, Che Hassan and Mohammed, M.N. and Jameel Al-Tamimi, Adnan Naji and M.Sarhan, Samaher and Salleh, Mohd Shukor and Abdulrazaq, M. and Abdullah, Oday I. (2022) Development of Hybrid Intelligent Models for Prediction Machining Performance Measure in End Milling of Ti6Al4V Alloy with PVD Coated Tool under Dry Cutting Conditions. Lubricants, 10 (10). ISSN 20754442 relation: https://www.scopus.com/pages/publications/85140627051?origin=resultslist relation: 10.3390/lubricants10100236 identifier: 10.3390/lubricants10100236