Controlled heat input in laser machining of PMMA: A GA-RSM approach

Highlights • To estimate the amount of heat imposed on materials due to laser-material interaction and deeply understands what happens when laser interacts with temperature-sensitive nonmetallic Poly-methyl-methacrylate (PMMA), mainly surface temperature and channel depth. • The experimental run was designed using a central composite design of response surface methodology, where laser power varied from 15 watts to 40 watts, and scanning speed varied from 150 mm/sec to 750mm/sec. • Each surface was examined after a single pass, and channel depth were measured using a toolmaker microscope. • Material removal was measured by a high-precision balance meter. • The heating amount is calculated at each power and scanning speed combination by adopting standard equation. • Results showed that the threshold energy for PMMA is Qth = 0.0214 (J). • As the amount of heat increases channel depth increase linearly; on the other hand, relation with the amount of material removal is non linear particularly after at power-speed ratio is 0.4 there is a sudden increase in material removal. • Based on the regression analysis, a multi-objective optimization model using Genetic Algorithm (GA) was developed and validated. Laser micromachining is a precise technique well-suited for temperature-sensitive nonmetallic materials, as it uses heat to remove material from the surface. This research estimates the heat input during laser-material interaction in Poly-methyl-methacrylate (PMMA). It identifies the optimum parametric zone to achieve the desired channel with minimum energy consumption, providing a comprehensive understanding of the effect on channel depth and material removal. The experimental run was designed using a central composite design of response surface methodology (RSM), where laser power varied from 15 watts to 40 watts, and scanning speed varied from 150 mm/sec to 750 mm/sec. Each surface was examined after a single pass, and channel depth and material removal were measured using a toolmaker microscope and a high-precision balance meter, respectively. The threshold energy for PMMA was determined as Q th = 0.0214 (J), while heat input ranged from 0.042 to 0.475 J across the parameter combination found using the standard equation. Channel depth increases linearly with heat input; on the other hand, the relationship between heat input and material removal deviates from linearity and follows an exponential trend as heat approaches 0.4 J. At higher heat input, laser energy is less efficiently utilized for increasing depth as more energy contributes to material removal. The regression analysis revealed a quadratic model for heat and channel depth, and a two-factor interaction model for material removal. Optimal solution within the design space by RSM suggested that a minimum heat of 0.17 J can achieve a maximum depth of 0.15 mm at 40 watts and 420 mm/sec scanning speed. Additionally, based on the regression analysis, a multi-objective optimization technique, Genetic Algorithm (GA), suggested an optimal combination of 35.91 W and 387.72 mm/sec to achieve a depth of 0.145 mm at the same heat input (0.17J ), demonstrating reduced energy usage with comparable machining performance.