Assessing the Transferability of Preheating and Extrusion Data for Downstream Quality Prediction in Seamless Copper Tube Manufacturing

Seamless copper tube manufacturing involves sequential processing steps, while the final product quality is assessed through Eddy Current Testing (ECT). Predicting this quality early on using billet heating and hot-extrusion data poses a challenge as the underlying models might exploit production-related signatures instead of transferable process relationships. This study investigates if the production data describing these two stages can predict the classified ECT quality by utilizing random-forest models against different validation strategies, aiming to assess the historical performance, the actual information learned and if this information transfers across tools and time. The analysis covers 1916 observations from 5 months, three products, 62 mandrels and 155 dies at a single plant. Three ECT-based quality classes (A, B, C) were analyzed. Random cross-validation yielded balanced accuracies of 47.49% with all predictors and 46.01% with physical predictors (permutation p = 0.0033 for both). Mandrel-grouped scores were 39.90% and 41.31%, respectively, while the all-predictor score decreased from 50.12% under random holdout to 35.00% on future production. Physical variables retained historical signal, but a stable tool-independent relationship was not established. These findings provide an initial basis for developing context-aware quality-risk and model-applicability assessment, while retaining ECT as the final inspection.