AI-powered hybrid metaheuristic optimization for predicting surface roughness and kerf width in CO 2 laser cutting of 3D-printed PLA-CF composites

This study explores the impact of CO 2 laser cutting parameters on surface roughness and kerf width of 3D-printed Carbon Fiber reinforced Polylactic Acid (PLA-CF) composites while developing phenomenological models using hybrid artificial intelligence techniques. PLA-CF composites possess certain mechanical properties and surface quality. The surface roughness and kerf width values were measured under different laser cutting conditions (such as plate thickness, power, and cutting speed) and were predicted using multiple linear regression, particle swarm optimization-based adaptive neuro fuzzy inference system, and ant colony optimization-based adaptive neuro fuzzy inference system models. Experimental results showed that kerf width and surface roughness are influenced significantly by laser cutting parameters, showing the importance of accurately selecting the parameters. The most dominant factor entered the model as cutting speed: as cutting speed was increased, kerf width decreased, but higher levels of power resulted in kerf width. Thickness provided a non-linear input: kerf width decreased from 2 to 2.5 mm, then increased to 4 mm. The least kerf width (0.809 mm) was obtained at 90 W power and 9 mm/s cutting speed, with 2.5 mm thickness. Surface roughness increased with cutting speed and power, with minimum surface roughness (1.878 µm) at 2 mm thickness and 90 W power with 3 mm/s cutting speed. Among the hybrid artificial intelligence models, the particle swarm optimization-based adaptive neuro fuzzy inference system model gave the best accuracy, achieving the lowest mean squared error and highest correlation coefficient, whereas the ant colony optimization-based adaptive neuro fuzzy inference system model performed better than multiple linear regression but not better than particle swarm optimization. These results, therefore, validate the applicability of hybrid artificial intelligence models for predicting surface quality and kerf width during CO 2 laser cutting.