Effect of Ironing Parameters on Surface Quality and Energy Demand ofFDM-Fabricated Proximal Humerus Plates: A Box-Behnken and Machine Learning Study

Abstract

This study investigates the effects of ironing parameters on surface quality and energy demand in FDM fabrication of proximal-humerus-inspired PLA plate-like demonstrator, aiming to quantify how ironing settings influence surface roughness (Ra) and electrical energy consumption (EC), thereby revealing the quality-efficiency trade-off in the finishing stage. Four ironing related process variables including ironing flow (IF), ironing line spacing (ILS), ironing speed (IS), and ironing inset (II) were investigated at three levels using a BBD. Experimental results showed that Ra varied between 4.52 and 8.52 mu m, whereas EC ranged from 261.15 to 545.39 Wh, indicating that improved surface finish generally requires higher energy input. Analysis of variance confirmed that IF, ILS, and IS significantly affect Ra (p < 0.001), while EC is predominantly governed by IS and IF (p < 0.001). To model and predict Ra and EC from ironing parameters, LR, ANN, and GPR-Bayes models were developed and evaluated using a repeated group-based fivefold cross-validation strategy. For Ra prediction, LR and GPR-Bayes showed similar performance, while ANN showed lower performance. For EC prediction, GPR-Bayes achieved the best overall performance, with R-2 = 0.8624 +/- 0.1741.