Effect of Dataset Size and Auxiliary Data in Bayesian Learning of Advanced Manufacturing: A Composite Autoclave Processing Diagnostic Study

Abstract

Abstract Recent advances in data-driven predictive modelling have enabled the emergence of intelligent manufacturing enterprises. Nonetheless, most of the present frameworks incorporate non-interpretable black-box machine learning models, often requiring large datasets and yet lacking ‘diagnostic’ modelling capabilities. In the context of advanced composites manufacturing, where the presence of numerous decision factors and uncertainties can rapidly yield failures, training cost/data-efficient, transparent and diagnostic-capable predictive models continue to highly valuable to pertinent industries. This can specifically allow decision-makers on the manufacturing floor to identify the causes or state variables of the process that contribute to the product failure (e.g., due to an excessive exotherm or lag temperature during curing), and thereby saving sizable volume of material scraps due to trial and errors. In this work, a Bayesian learning framework with inverse modelling capabilities for an advanced composites autoclave curing process has been developed and assessed for the first time, while assuming different dataset size availabilities. The advantages of using both a naïve Bayesian network and a highly-connected Bayesian belief network (BBN) are compared and discussed. The results revealed that integration of expert knowledge under highly-connected Bayesian models can offer a favorable predictive performance for root cause analyses, along with apparent tractability for in-situ applications, despite the very limited-volume of training data, when accompanied with carefully selected auxiliary data (e.g. via the use of a proxy thermocouple during the processing based on expert domain).