Density prediction for selective laser melting fabricated of CuCrZr alloy using hybrid Gaussian boosted regression

Selective laser melting (SLM), an emerging technology, constructs components through layer-by-layer material deposition and has gained popularity in the industry due to its advantages such as shorter lead time, higher flexibility, lower material wastage, and the capability to fabricate complex geometries. However, the development of process databases for new materials is often time-consuming and laborious because SLM involves multiple physical fields and multiple process steps with numerous process parameters. Recently, machine learning is renowned for its excellent capabilities in tasks such as classification, regression, and clustering. In this study, hybrid Gaussian boosted regression that combines Gaussian process regression with gradient boosting machine was used to obtain a process database for CuCrZr alloy, optimizing for density with laser power and scanning speed as characteristic parameters, under limited samples. A machine learning model was developed using fivefold cross-training on 36 datasets. With a determination coefficient (R2) of 0.96587, the model demonstrated a high level of fit. Next, by extending the prediction range, we achieved process parameters for the highest five densities of samples. Finally, the model’s precision was confirmed with experiments on the five predicted maximum densities, with all predictions falling within a ±0.09% error margin from the experimental values. This research precisely predicted the densities of SLM-formed CuCrZr parts, created a comprehensive process parameter database, and substantiated both theoretical and practical backing for the 3D printing of CuCrZr parts.