Development of Hybrid Intelligent Models for Prediction Machining Performance Measure in End Milling of Ti6Al4V Alloy with PVD Coated Tool under Dry Cutting Conditions
Al-Zubaidi S. A.Ghani J. Che Haron C.H. Mohammed M.N. Jameel Al-Tamimi A.N. M.Sarhan S. Salleh M.S. Abdulrazaq M. Abdullah O.I.
October 2022MDPI
Lubricants
2022#10Issue 10
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.
gravitational search algorithm , optimization , surface roughness , Ti6Al4V alloy
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Department of Automated Manufacturing Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, 10071, Iraq
Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Selangor, Bangi, 43600, Malaysia
Mechanical Engineering Department, College of Engineering, Gulf University, Sanad, 26489, Bahrain
College of Technical Engineering, Al-Farahidi University, Baghdad, 10001, Iraq
Department of Biochemical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, 10071, Iraq
Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Melaka, Durian Tunggal, 76100, Malaysia
Research Center, The University of Mashreq, Baghdad, 10023, Iraq
Department of Energy Engineering, College of Engineering, University of Baghdad, Baghdad, 10071, Iraq
System Technologies and Engineering Design Methodology, Hamburg University of Technology, Hamburg, 21079, Germany
Department of Mechanics, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Department of Automated Manufacturing Engineering
Department of Mechanical and Manufacturing Engineering
Mechanical Engineering Department
College of Technical Engineering
Department of Biochemical Engineering
Fakulti Kejuruteraan Pembuatan
Research Center
Department of Energy Engineering
System Technologies and Engineering Design Methodology
Department of Mechanics
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