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Selective laser melting (SLM) of AlSi10Mg benefits automotive and aerospace industries because they can fabricate complex profiles exhibiting superior (strength-to-weight ratio, thermal conductivity , and robust wear resistance) performances in printed parts. Precise control of process variables (laser power: LP, scan speed: SS, and focal plane : FP) is essential to attain balanced properties (lightweight material with higher hardness, HV and lesser wear rate, WR) that could widen the material structural applications. Systematic framework models were established for the SLM process , focusing on the experimental plans, analyzing the process insights, and optimizing conflicting outputs. Central composite design (CCD) matrices were applied for planning experiments corresponding to influencing control variables such as LP, SS, and FP. All linear factors (excluding the FP on HV) showed profound effects with a hierarchy listed according to their notable contribution: SS > LP > FP. The LP with HV and SS and FP with WR pose a strong linear relationship. The interaction terms (LP × SS, LP × FP) were statistically significant for HV and WR. The empirical equation derived with a better coefficient of determination (0.9934 for HV and 0.9738 for WR) predicted ten random experimental cases with an overall absolute percent deviation equal to 3.65 % for WR and 3.62 % for HV. A strong correlation coefficient (R 2 = 85.1 %) with an inverse relationship (higher HV value resulting in lower WR) was established between the WR and HV, tested against 1000 data points. The derived predictive empirical equations ensure that HV values can be estimated for the known values of WR. The Osprey Optimization Algorithm (OOA) determined a single optimized condition (among three case studies, maximum weight fractions to HV and minimum to WR resulted in highest overall desirability (OD) value equal to 0.9735), which resulted in higher HV and low WR experimental values equal to 143.4 HV and 208.7 μm. The computed absolute percent deviation in prediction is 1.58 % for HV and 1.72 % for WR, respectively. Scanning electron micrographs of optimal, sub-optimal and other multi-objective optimization conditions validated the microstructure and property relationship. The wear morphology depicts an uneven and flaky structure with a wide range of particle shapes and sizes (∼50 μm) exhibiting abrasive wear mode with probable delamination due to high load and stresses. The Energy-dispersive X-ray analysis of the wear track of the optimal condition sample confirmed the composition of AlSi10Mg alloy. The predictive and optimization models enable novice industrial personnel to achieve enhanced material properties efficiently, minimizing experimental trials, material usage, time, resources and effort.
Gadlegaonkar et al. (Mon,) studied this question.