An Active Transfer Learning (ATL) Framework for Smart Manufacturing with Limited Data: Case Study on Material Transfer in Composites Processing

Abstract

Unprecedented advances in Machine Learning (ML), cloud computing and sensory technology promise to enable the manufacturing industry to respond rapidly to changes in marked needs while maintaining product quality and minimizing costs. Despite the unparalleled advantages that ML offers, critical limiting factors have prevented the exhaustive cultivation of ML in advanced manufacturing. Constant shifts in the process configuration and lack of sufficient fully-descriptive data restrict the performance of predictive ML models. This paper proposes to partly address these shortfalls with an active transfer learning (ATL) model that is applied to an aerospace composites manufacturing case study. The proposed ATL framework requires 1) developing an AI-based optimal experimental design using Active Learning (AL) to maximize the information gain from the limited number of allowable manufacturing trials, and 2) equipping the manufacturing process with a robust Transfer Learning (TL) model that is trained on limited available data and is immune to shifts in the process settings. The results suggest that uncertainty-based AL approaches can significantly decrease the dependency on large datasets for obtaining accurate process models. Furthermore, in comparison with traditional TL approaches, the proposed framework represents a practical solution to further reduce the necessity for generating expensive data in advanced manufacturing applications for developing reliable and transferable predictive models.