A Multi-Criteria Reanalysis of Electrical-Discharge Diamond Grinding Using Regression Models and DEFMOT

This paper presents an integrated modelling and decision-support reanalysis of a published 24-run experiment on diamond-spark grinding (electrical-discharge diamond grinding) of two hard alloys—the tungsten-free cermet TN-20 and the WC–TiC–Co alloy HS123—machined jointly with C45 steel; no new experiments are performed. Established components are deliberately combined into one reproducible workflow: quadratic response-surface models fitted by least squares and by minimax (Chebyshev) approximation, validation by prediction-oriented criteria including nested leave-one-out cross-validation of the entire model-selection pipeline, the addressable DEFMOT representation of the 94-factor grid formalized as an ε-constraint procedure, and benchmarking against desirability-function and Pareto analyses. Minimax fitting reduces the maximum absolute residual by 22.5–36.9% at the cost of higher aggregate errors. Nested validation exposes model-selection instability for the TN-20 responses, and a dedicated sensitivity analysis shows that the surrogate-model choice can change the recommended regime: the TN-20 compromise is efficient or one grid step from efficient under all three surrogate families, whereas the preferred HS123 regime shifts qualitatively (including a reversal of the wheel-speed setting) between least-squares and minimax surrogates. A residual-bootstrap analysis propagates data uncertainty through the complete optimization and quantifies how frequently each recommended regime is re-selected. Within the legacy cost basis, point estimates indicate comparable productivity (difference below 9%), an approximately 35% lower specific machining cost for TN-20 and approximately 1.8 times higher diamond consumption; the 95% confidence intervals for the between-material contrasts include zero, so experimental confirmation is required before industrial substitution. The framework quantifies, rather than hides, how surrogate uncertainty propagates into the engineering decision.