Sustainable Additive Manufacturing Process Optimization for Enhanced Mechanical Performance

Abstract

Additive manufacturing has emerged as a transformative production technology enabling complex geometries, material efficiency, and digital manufacturing flexibility. Despite its advantages, concerns persist regarding energy consumption, material waste, and inconsistent mechanical performance across process parameters. Sustainable additive manufacturing requires simultaneous optimization of environmental sustainability and mechanical integrity. This research develops and empirically validates a structural optimization model linking process parameters, energy efficiency, material utilization efficiency, and thermal stability with mechanical performance outcomes in additive manufacturing systems. Drawing upon sustainable manufacturing theory and process optimization principles, the study conceptualizes sustainability driven process optimization as a multidimensional construct influencing tensile strength, fatigue resistance, and dimensional accuracy. A quantitative research design was employed using Partial Least Squares Structural Equation Modeling to evaluate relationships among laser power control, layer thickness optimization, build orientation strategy, energy monitoring systems, material recycling integration, and resulting mechanical performance indicators. Data were collected from 389 mechanical engineers, additive manufacturing specialists, and production managers across aerospace, biomedical, and automotive sectors. Measurement model results confirmed reliability and convergent validity with composite reliability values above 0.88 and average variance extracted above 0.62. Structural model findings indicate that optimized process parameters significantly improve mechanical performance beta 0.46 p less than 0.001, while energy efficiency beta 0.29 and material utilization efficiency beta 0.33 also contribute positively. Thermal stability mediates the relationship between process parameters and mechanical performance. The model explains 61 percent of the variance in mechanical performance. The findings demonstrate that sustainable additive manufacturing requires integrated control of energy, material, and thermal dynamics rather than isolated parameter adjustments. The study contributes a validated interdisciplinary optimization framework supporting environmentally responsible manufacturing while maintaining superior mechanical properties.