AI-Driven Optimization of Laser Surface Texturing for Sustainable Manufacturing

Sustainable manufacturing requires the application of processes that reduce the environmental impact without compromising product quality and reliability. Among different surface modification techniques, laser texturing is a promising sustainable process that combines high precision, absence of hazardous by-products and low material wastage. In the current research, the effect of laser treatment process parameters on the Ti6Al4V sample surface roughness is investigated. Experimental outcomes show robust relationships between surface topography and process conditions, with roughness parameters (Sz and Sa) being significantly influenced by scanning speed and power. Subsequently, an artificial neural network (ANN) model trained was applied on the experimental data to predict the roughness parameters with good accuracy at a minimum mean absolute error. The integration of AI-based predictive models with green surface texturing offers new avenues for efficient process design in manufacturing sectors of the future, including biomedical processes.