Multi-objective Optimization of Laser Beam Machining Process Parameters

Laser beam machining (LBM) is one of the most widely used thermal energy based non-contact type advance machining process. In recent years, researchers have explored a number of ways to improve the LBM process performance by analyzing different factors that affect the quality characteristics of LBM process such as kerf width, kerf taper, surface finish etc. It is revealed from the literature that process performance of the LBM process can be improved considerably by appropriate selection of laser beam process parameters. However, researchers had mostly employed statistical methods such as Taguchi, Response Surface Methodology (RSM), Grey Relation Analysis (GRA), Principal Component Analysis (PCA) etc. for optimization of the LBM process. Although these methods are applied for multiobjective optimization, these are based on weights approach and provide single optimum solution. Hence, in this work an attempt is made for multi-objective optimization of LBM process using posteriori approach known as non-dominated sorting genetic algorithm (NSGA) in which the set of non-dominated optimum solutions is obtained. This provides the ready reference to process planner to choose appropriate set of optimum solutions as per requirement. For this work the experiments are carried out on the CO2 laser machine to cut 3 mm thick plate of material stainless steel AISI 321. The process parameters considered are assist gas pressure, cutting speed, laser power, and pulse frequency. The quality performance measures are in terms of kerf width, kerf taper and surface roughness.