eprintid: 20613 rev_number: 3 eprint_status: archive userid: 1 dir: disk0/00/02/06/13 datestamp: 2026-07-06 07:36:49 lastmod: 2026-07-06 07:36:49 status_changed: 2026-07-06 07:36:49 type: article metadata_visibility: show creators_name: Al-Zubaidi, Salah creators_name: A.Ghani, Jaharah creators_name: Che Haron, Che Hassan creators_name: Mohammed, M.N. creators_name: Jameel Al-Tamimi, Adnan Naji creators_name: M.Sarhan, Samaher creators_name: Salleh, Mohd Shukor creators_name: Abdulrazaq, M. creators_name: Abdullah, Oday I. 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 ispublished: pub note: Cited by: 10; All Open Access; Gold Open Access; Green Open Access abstract: 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. date: 2022 publisher: MDPI official_url: https://www.scopus.com/pages/publications/85140627051?origin=resultslist id_number: 10.3390/lubricants10100236 full_text_status: none publication: Lubricants volume: 10 number: 10 refereed: TRUE issn: 20754442 citation: 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