Green Manufacturing

Laser machining has become a critical process in contemporary manufacturing, with higher precision, less tool wear, and the ability to machine complex geometries. AL6061 alloy, with its strength-to-weight ratio, corrosion resistance, and machinability, is extensively used in aerospace, automotive, and structural applications. Nevertheless, conventional machining of the alloy tends to be energy-hungry and causes extensive tool wear. While laser machining offers a non-contact option, it too can be energy-intensive and thermally damaging if not optimized correctly. The present study examines the sustainability of AL6061 alloy laser machining through optimization of some of the most critical process parameters, namely laser power, scanning speed, and pulse frequency, using a multiobjective strategy. Experimental investigation used a Taguchi L9 orthogonal array and recorded responses like surface roughness, material removal rate (MRR), and energy utilization. Data analysis involved the use of Grey Relational Analysis (GRA), allowing for the consideration of several conflicting objectives at one time. The experiments determined the best parameter setting of 18W laser power, 150 mm/s scan speed, and 60 kHz pulse rate. At these parameters, surface roughness decreased by 50% (from 6.2 µm to 3.1 µm), MRR rose by 27%, and energy efficiency rose by 19%. Statistical validation using ANOVA established the significance of the chosen parameters for sustainable machining results. This research bridges an important research gap by combining performance measures with environmental factors, providing a blueprint for industries looking to implement sustainable laser machining techniques. The results not only advance energy-efficient manufacturing but also demonstrate the potential of intelligent optimization methods in green manufacturing systems. Future research can investigate AI-based adaptive control, real-time feedback, and full lifecycle analysis to further enhance the sustainability of laser machining processes.