Performance evaluation of a manufacturing process under uncertainty using Bayesian networks

This paper proposes a systematic framework using Bayesian networks to aggregate the uncertainty from multiple sources for the purpose of uncertainty quantification (UQ) in the prediction of performance of a manufacturing process. Energy consumption, one of the key metrics of manufacturing process sustainability performance, is used to illustrate the proposed methodology. The prediction of energy consumption is not straightforward due to the presence of uncertainty in many process variables and the models used for prediction. The uncertainty is both aleatory (statistical) and epistemic (lack of knowledge); both sources of uncertainty are considered in the proposed UQ methodology. The uncertainty sources occur at different stages of the manufacturing process and do not combine in a straightforward manner, thus a Bayesian network approach is found to be advantageous in uncertainty aggregation. A dimension reduction approach through variance-based global sensitivity analysis is proposed to reduce the number of variables in the system and facilitate scalability in high-dimensional problems. The proposed methodologies for uncertainty quantification and dimension reduction are demonstrated using two examples - an injection molding process and a welding process.