Predicting product quality in continuous manufacturing processes using a scalable robust Gaussian Process approach

This work describes an Artificial Intelligence (AI)-based solution that predicts product quality when applied to a continuous manufacturing process. The proposed solution uses process parameters and product quality measurements that are obtained from a production line. The work detailed herein is problem-driven, showing an application within one of the UK's foundation industries and identifying five key criteria an AI solution should ideally satisfy in continuous manufacturing applications; scalability, modularity, stable out-of-data performance, uncertainty quantification and robustness to unrepresentative data. The shortcomings, relative to these five criteria, of available AI approaches are discussed before a potential solution is presented. The proposed approach involves the application of a generalised product-of-expert Gaussian process whose noise model is constructed from a Dirichlet process. The ability of the model to fulfil the five key criteria and its performance when applied to the foundation industry case study is demonstrated.