Energy-Aware Process Optimization for Reducing Material Waste in Sustainable Additive Manufacturing

Abstract

This study has examined energy-aware process optimization as a means of reducing material waste in sustainable additive manufacturing, with particular attention to metal powder-bed fusion and directed-energy-deposition processes in selected United States industrial and research settings. The central problem has been that additive manufacturing, although frequently characterized as near-net-shape and material-efficient, in practice incurs substantial material waste through support structures, un-fused and degraded powder, failed and out-of-specification builds, and post-processing removal, while simultaneously consuming high specific energy per unit of deposited material, so that gains in one objective are often obtained at the expense of the other. The purpose of the study has been to evaluate whether process parameter control and optimization, energy monitoring and specific energy efficiency, material flow modeling and waste reduction, thermal and melt-pool digital-twin fidelity, and data governance, standardization and compute jointly and significantly improve sustainable manufacturing and waste-reduction outcomes when energy consumption and material waste are treated as coupled objectives within a single multi-objective optimization framework rather than optimized in isolation. A quantitative, cross-sectional, case-based research design has been used, and data have been collected from additive manufacturing process engineers, materials and metallurgy specialists, sustainability and life-cycle-assessment analysts, machine and automation engineers, and manufacturing data scientists engaged in process development and production. Out of 281 distributed questionnaires, 241 valid responses have been retained, producing an 85.8% valid response rate. The analysis plan has included descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation analysis, multiple regression modeling, hypothesis testing, a prototype multi-objective process-parameter optimization evaluation, and Python-driven analytical workflow validation. The findings have shown strong agreement across the constructs, with process parameter control and optimization recording the highest mean score of 4.25, followed by energy monitoring and specific energy efficiency at 4.18, material flow modeling and waste reduction at 4.09, sustainable manufacturing and waste-reduction outcomes at 4.03, thermal and melt-pool digital-twin fidelity at 3.97, and data governance, standardization and compute at 3.90. Reliability has been confirmed through Cronbach’s alpha values ranging from .82 to .91, with overall reliability of .93. Correlation results have shown significant positive relationships between waste-reduction outcomes and process parameter control (r = .69), energy monitoring and efficiency (r = .68), material flow modeling (r = .65), thermal digital-twin fidelity (r = .61), and data governance (r = .58), all at p < .001. The regression model has been significant, F(5, 235) = 55.63, p < .001, explaining 54.2% of the variance.