Non data hungry smart composite manufacturing using active transfer learning with sigma point sampling (SPSATL)

Abstract

As an emerging smart manufacturing paradigm, Industry 4.0 employs advancements in sensory technologies and cyber physical systems (CPS), to automate and optimize manufacturing processes. However, using conventional machine learning (ML) in this paradigm can be challenging especially in dynamic systems where materials used in the production plan can change constantly in order to adapt to the market needs. Another factor that currently hinders ML-based automation in many manufacturing processes is the lack of labeled data with usable features. In this paper, we address the aforementioned challenges in an aerospace composites manufacturing case study, by using an active transfer learning (ATL) framework. Active learning, in a semi-supervised setting, functions as a family of ML where the model is trained only on the most relevant labeled data. Transfer learning, on the other hand, is used to create robust ML models that reduce the effect of data distribution shifts in different source and target datasets. In particular, we present an enhanced approach, ATL using Sigma point sampling (SPSATL). SPSATL leverages representative subsets constructed with sigma points to capture true data distributions in a more concise and informative way, making AL procedures faster and more data-efficient. The proposed SPSATL approach is evaluated in a composites autoclave processing case study and the model outperformed the state-of-the-art sampling models in terms of accuracy (as high as 12% increase) and the volume of training data required (as low as 50% decrease).