Abstract
In machining parameters optimization of a chatter-free milling process, the inevitable surface location error (SLE) reflecting the machined workpiece dimension accuracy has been barely considered as one objective representing the machining quality, lowering the optimization accuracy. Therefore, this paper provides an approach to establish a multi-objective optimization model, where the material removal rate (MRR) represents the machining efficiency and the SLE predicted in time-domain represents the machining quality. The non-dominated sorting genetic algorithm (NSGA-II) method is used to solve the multi-objective model and provide pareto optimal solutions to first determine some ideal optimal solutions. Then the analytic hierarchy process (AHP) and grey target decision (GTD) methods are combined to select one most satisfactory optimal solution which has a well balance between the MRR and SLE. A multi-objective model was established and taken as a case study to maximize the MRR and minimize the SLE. Comparison study was performed on this multi-objective model and two other mono-objective models for obtaining the optimal MRR and SLE respectively, which was combined with the influences of machining parameters on SLE to show the necessity of conducting a multi-objective optimization. Milling tests were conducted based on the solved optimal machining parameters, and the well consistence between the measured and predicted SLEs shows that the proposed multi-objective optimization method can provide an effective approach to balance the machining efficiency and quality when there are conflicts between different objectives.
Highlights
The numerical control machining applied in the production of modern industry is essentially a process for removing the workpiece materials
Given the lack of surface location error application in machining parameters optimization, the material removal rate (MRR) and SLE are designed as the objectives in this paper to establish a multi-objective optimization model for obtaining an optimal machining parameters combination, which can realize a well balance between the milling efficiency and quality
CASE STUDY AND RESULTS DISCUSSION To better illustrate the application of the proposed multiobjective optimization method and validate its feasibility in real machining process, a case study that contains a simulation of the machining parameters optimization and a series of experiments has been performed on a CNC vertical machining center
Summary
The numerical control machining applied in the production of modern industry is essentially a process for removing the workpiece materials. Sahu and Andhare [13] proposed a multi-objective optimization method considering the power consumption, material removal rate, surface roughness and tool wear in high speed milling, and combined the response surface methodology and genetic algorithm to select optimal machining parameters. The surface roughness has been already used in representing the machining quality, but it is manly considered as one constraint and expressed based on empirical formulas and approximate models [16], [17] Another important machining quality index named surface location error (SLE) has still not attained much attention in the machining parameters optimization [18]. Given the lack of surface location error application in machining parameters optimization, the MRR and SLE are designed as the objectives in this paper to establish a multi-objective optimization model for obtaining an optimal machining parameters combination, which can realize a well balance between the milling efficiency and quality.
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