Towards generalizable machine learning prediction of downskin surface roughness in laser powder bed fusion

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This study addresses these challenges by (i) demonstrating a downskin surface roughness classification model, trained on the largest reported dataset for downskin roughness (400 downskin specimens spanning five builds and two ferrous alloys) and (ii) conducting a thorough investigation of the model’s generalizability. Additionally, this study highlights critical issues such as data imbalance, generalization to unseen data, and the importance of rigorous evaluation. By implementing robust ML practices, we focused on model performance across different training and evaluation domains. Our findings indicate satisfactory performance when using the more conservative balanced accuracy metric, achieving about 95% inter-domain and 83% intra-domain accuracy