Multi-Objective Optimization of Quality in Laser Cutting Based on Response Surface Model

Abstract:

Prediction and optimization of quality characteristics is an important means to improve the quality of laser cutting. Kerf width (KW) and material removal rate (MRR) are selected as the quality characteristics in this paper. The fitting response surface models (RSM) of KW and MRR are considered as the optimization objective function in pulsed Nd: YAG laser cutting of alloy steel for multi-objective optimization. An improved Pareto genetic algorithm is used in the optimization, and the significant factors have been found. The predicted results are basically consistent with the experimental. Therefore, the method used in this paper can be used for optimization of KW and MRR in pulse Nd: YAG laser cutting. The study can provide theoretical basis for the prediction and optimization of quality in laser cutting.

• Periodical: Advanced Materials Research (Volumes 756-759)
• Pages: 3712-3716
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.756-759.3712
• Online since: September 2013
• Authors: Hui Juan Hao, Mao Li Wang, Feng Qi Hao
• Keywords: Improved Pareto Genetic Algorithm, Laser Cutting, Multi-Objective Optimization (MOP), RSM

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