Cutting parameter optimization of Al-6063-O using numerical simulations and particle swarm optimization
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ORIGINAL ARTICLE
Cutting parameter optimization of Al-6063-O using numerical simulations and particle swarm optimization Juan Camilo Osorio-Pinzon1 · Sepideh Abolghasem2
˜ 1 · Juan Pablo Casas-Rodriguez1 · Alejandro Maranon
Received: 13 June 2020 / Accepted: 29 September 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Machining process simulations are commonly used by manufacturing industries to accurately predict machining force, time, and the performance of engineering components. Determination of optimal conditions of machining parameters is fundamental as it directly affects the material properties, surface finish quality, and the cutting tool life, among other objectives. In this work, we propose a multi-objective particle swarm optimization (PSO) algorithm, in order to determine the optimal machining parameters; i.e., rake angle (α), velocity (V ), and cutting feed (f ) using finite element (FE) simulation of orthogonal cutting. We evaluate the optimality of the problem for three objectives: (i) minimize the cutting force, (ii) maximize the microstructure refinement, and (iii) maximize material removal rate (MRR) in machining of Aluminum 6063. Minimum cutting force, higher refinement, and higher MRR are desirable in order to achieve enhanced cutting tool life, higher strength of the material, and higher machining performance, respectively. First, we develop the input-output relationships as well as the in-process parameter correlations using response surface methodology (RSM) and artificial neural network (ANN). Next, we use the particle swarm optimization technique combined with weight aggregation method to solve the multi-objective PSO (MOPSO) problem resulting in Pareto optimal solutions. Finally, we compare three machining conditions from the Pareto front in which one of the objective functions is optimized and the results show that a trade-off point can be drawn among the low cutting force, high microstructure refinement, and high MRR. A sample condition from the Pareto front is created experimentally resulting in good agreement with the model output. The optimization models can potentially enable the achievements of the desired objectives through the integration of the MOPSO algorithm with most of the available finite element simulations of machining. Keywords Orthogonal cutting · Particle swarm optimization · FEM analysis · Microstructure · Material removal rate
1 Introduction Emerging trajectories in the globally competitive environment of manufacturing demand simultaneously increase in productivity while enhancing surface quality as well as often difficult-to-realize material property combinations. Accordingly, a major challenge in metal cutting manufacturing processes is to minimize the manufacturing cost without sacrificing the quality or the enhanced material property of the engineering components. Consequently, the machine Sepideh Abolghasem
[email protected] 1
Department of Mechanical Engineering, Universidad de los Andes, Carrera 1a No. 18A-10, Bogo
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