Explainable Artificial Intelligence for Manufacturing and Materials Processing: A Review of Engineering Validation and Decision Support

Machine-learning predictions are used to assess material properties, monitor manufacturing processes and support inspection and maintenance. Their engineering value depends on whether the information used by a model can be examined and related to the physical task. This narrative review considers interpretable models, feature attribution, response plots, visual explanations and constrained counterfactuals in manufacturing and materials processing. It distinguishes predictive performance from explanation fidelity, physical evidence and decision usefulness. Selected applications illustrate these differences: process–property modelling in additive manufacturing, acoustic monitoring, tool-condition assessment, ultrasonic defect sizing and uncertainty in production processes. A comparison of these examples shows why the prediction target; data representation and validation procedure must be stated before explanation results are interpreted. The review proposes four categories of explanation use—descriptive, diagnostic, prescriptive and governed—and identifies the additional evidence required for each. The main methodological concerns are dependent inputs, unrealistic perturbations, non-independent observations and changes in the model or operating conditions. Explanation methods can help examine a model and guide further investigation, but process changes require separate checks of feasibility and physical consequences. The narrative selection does not establish the prevalence of individual methods across the field.