Leakage-Controlled Feature Fusion on a Synthetic Benchmark for Mechanical Assembly Accuracy

This study evaluates leakage-controlled feature fusion for five shafting-adjustment tasks and one bevel-gear contact-pattern task on a synthetic benchmark. The complete TransXGB procedure combines fold-standardized raw inputs, out-of-fold Transformer latents, and out-of-fold XGBoost leaf features. Ten methods were evaluated on identical repeated outer folds; the prespecified primary criterion was the six-task equal-weight mean standardized MAE across the pooled S0, S1, and S2 scenarios. Ridge achieved a slightly lower primary error than full TransXGB (0.211481 versus 0.211622), and inner selection over five Ridge penalties produced the same ordering (0.211430 versus 0.211622). In S1 and S2, TransXGB errors were 0.53% and 1.99% lower than prespecified Ridge, but both descriptive task-resampling intervals crossed zero. These scenarios were deliberately constructed with smooth and threshold nonlinearities, so the differences do not establish a general nonlinear advantage. Complete same-batch ablation outputs show that fixed inclusion of the XGBoost leaf branch did not yield a net error reduction in these six tasks, while raw-plus-Transformer was lowest in five tasks and Transformer removal worsened both pump tasks in all five folds. The leaf branch remains a leakage-controlled test of complementary tree-partition information; its result favors task-selective rather than fixed fusion. The benchmark does not demonstrate aggregate superiority or production accuracy.