Minimizing Material Waste and Energy Consumption in 3D Printing Using Multi-Objective Machine Learning Optimization
Keywords:
additive manufacturing; fused deposition modeling; energy prediction; support material; orientation selection; Pareto optimization; grouped validation.Abstract
Fused deposition modeling can reduce cutting waste, yet it still consumes electricity and sacrificial support material. This study develops a multi-objective machine learning method for selecting resource-efficient printing orientations. The public r3DiM benchmark supplied 184 PET-G printing records from 68 mechanical components. Each component had two or three tested orientations under fixed printer settings. Total printing energy was measured experimentally, while PrusaSlicer estimated support filament mass. Fourteen geometry and pre-build features supported seven regression algorithms. Repeated component-grouped validation prevented orientations of one component from crossing validation boundaries. XGBoost produced the best energy predictions, with a mean RMSE of 9.174 Wh. Its mean R² reached 0.969 across grouped folds. Extra Trees best predicted support waste, with 0.656 g RMSE and 0.768 mean R². Cross-fitted Pareto selection then evaluated every available orientation for each unseen component. The selected orientations reduced mean energy by 5.44% against the near-zero-degree baseline. The bootstrap 95% interval ranged from 3.06% to 8.04%. However, mean support savings were −0.020 g and were not significant. Only 11.8% of components improved both objectives. These findings show that accurate prediction does not guarantee balanced resource savings. The proposed workflow offers a transparent basis for orientation decisions. Future studies should add mechanical quality, broader materials, and continuous process settings.
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Copyright (c) 2026 Rafat S. A. Abumandil, Abdussalam Ali Ahmed, Omar Ahemed Mohamed Edbeib (Author)
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