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The primary objective of this study was to develop a novel data-driven machine learning-based multiobjective robust optimisation strategy to improve the overall quality of multivariate manufacturing processes. The new strategy was conceptualised considering a manufacturing environment with unreplicated non-normal data observations and limited opportunity for off-line sequential design of experiments. At a macro level, the new strategy adopts suitable artificial intelligence-based process models and a fine-tuned non-dominated sorting genetic algorithm-II (NSGA-II) to derive robust efficient process setting conditions. These robust solutions are iteratively derived considering process model predictive uncertainties, process setting sensitivities, and variance-covariance structure of uncontrollable multivariate non-normal inputs (or covariates). These solutions are also ranked based on multicriteria decision-making (MCDM) techniques to facilitate implementation. In this study, the quality of the best-ranked solutions was compared (w.r.t. closeness to specified multiple targets and predicted multivariate output variabilities) with those of the solutions obtained from parametric and commercial software-based approaches using three different real-life manufacturing cases.

A novel machine learning-based multiobjective robust optimisation strategy for quality improvement of multivariate manufacturing processes