Abstract

Wear prediction for agricultural soil-engaging components is computationally demanding when discrete element method (DEM) simulations are repeatedly used for design evaluation and operating-parameter screening. In this study, an Archard-inspired Gaussian process regression (GPR) residual-learning surrogate was developed for rapid prediction of the total wear volume calculated by EDEM for a ploughshare. The physical prior was a monotonic operational-parameter proxy motivated by the load and sliding trends in Archard theory, which did not directly use DEM-derived normal force, sliding distance, or frictional work. A soil–ploughshare interaction model was used to generate 100 full-factorial samples with tillage depth, tillage speed, and penetration angle as inputs. The Archard-inspired prior, cubic polynomial Ridge regression, standard GPR, and prior-guided residual GPR were evaluated by cross-validation, repeated random splits, and boundary-level extrapolation tests. Across 30 repeated 90%/10% splits, standard and Archard-inspired GPR achieved mean R2 values of 0.9927 ± 0.0014 and 0.9908 ± 0.0021, respectively. In the 200 mm tillage-depth extrapolation test, the latter performed best, with R2 = 0.9752, RMSE = 0.000252 mm3, and MAPE = 2.68%; however, the former was more accurate in the tillage-speed and penetration-angle extrapolation tests, and the 48% interval coverage of the prior-guided model in the penetration-angle test indicated overconfidence when the prior was biassed. These results show a conditional, rather than universal, benefit of the Archard-inspired prior: it improved extrapolation plausibility for the load-dominated tillage-depth case but did not improve all boundary predictions. The surrogate predicts EDEM-simulated wear, and its engineering validity depends on DEM calibration, the selected wear coefficient, and future soil-bin or field validation.

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