Surface roughness prediction in machining using two-stage domain-incremental learning with input dimensionality expansion

Accurate quantification of machined surface roughness is crucial to the characterization of part performance measures, including aerodynamics and biocompatibility. Given this cruciality, there exists a need for predictive metrology models which can predict the surface roughness before a part leaves a machine to reduce metrology-induced bottlenecks and improve production planning efficiency under emergent production paradigms, e.g., Industry 4.0. Current predictive metrology approaches in machining generally train machine learning models on all available input features at once. However, this approach yields a high number of free parameters during all states of training, possibly leading to suboptimal prediction results because of the complexity of simultaneously optimizing all parameters at once. In addition, previous machine learning-enabled surface roughness prediction studies have used limited test dataset sizes, which reduces the reliability and robustness of the reported results. To address these limitations, this study proposes a two-stage model training approach based on domain-incremental learning, wherein a second stage of training is performed using an expanded input domain. The proposed method is evaluated on a 3,000-element experimentally collected testing dataset of machined H13 tool steel surfaces, where it achieves 16.3 % roughness prediction error compared to the 29.5 % error of the conventional single-stage training approach, indicating the suitability of the two-stage training method for reducing surface roughness prediction error. • Developed two-stage learning technique for machined surface roughness prediction. • Established novel domain expansion method to enable dimensionally variable training. • Achieved improved roughness prediction accuracy over non-incremental approaches.