LAUSR

Abstract A Cyber-Additive Manufacturing Network (CAMNet) integrates connected additive manufacturing processes with advanced data analytics as computation services to support personalized product realization. However, highly personalized product designs (e.g., geometries) in CAMNet limit the sample size for each design, which may lead to unsatisfactory accuracy for computation services, e.g., a low prediction accuracy for quality modeling. Motivated by the modeling challenge, we proposed a data-driven model called family learning to jointly model similar-but-non-identical products as family members by quantifying the shared information among these products in the CAMNet. Specifically, the amount of shared information for each product is estimated by optimizing a similarity generation model based on design factors, which directly improve the prediction accuracy for the family learning model. The advantages of the proposed method are illustrated by both simulations and a real case study of the selective laser melting process. This family learning method can be broadly applied to data-driven modeling in a network with similar-but-non-identical connected systems.