{"ID":2851063,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.20339","arxiv_id":"2510.20339","title":"Multi-Task Deep Learning for Surface Metrology","abstract":"A reproducible deep learning framework is presented for surface metrology to predict surface texture parameters together with their reported standard uncertainties. Using a multi-instrument dataset spanning tactile and optical systems, measurement system type classification is addressed alongside coordinated regression of Ra, Rz, RONt and their uncertainty targets (Ra_uncert, Rz_uncert, RONt_uncert). Uncertainty is modelled via quantile and heteroscedastic heads with post-hoc conformal calibration to yield calibrated intervals. On a held-out set, high fidelity was achieved by single-target regressors (R2: Ra 0.9824, Rz 0.9847, RONt 0.9918), with two uncertainty targets also well modelled (Ra_uncert 0.9899, Rz_uncert 0.9955); RONt_uncert remained difficult (R2 0.4934). The classifier reached 92.85% accuracy and probability calibration was essentially unchanged after temperature scaling (ECE 0.00504 -\u003e 0.00503 on the test split). Negative transfer was observed for naive multi-output trunks, with single-target models performing better. These results provide calibrated predictions suitable to inform instrument selection and acceptance decisions in metrological workflows.","short_abstract":"A reproducible deep learning framework is presented for surface metrology to predict surface texture parameters together with their reported standard uncertainties. Using a multi-instrument dataset spanning tactile and optical systems, measurement system type classification is addressed alongside coordinated regression...","url_abs":"https://arxiv.org/abs/2510.20339","url_pdf":"https://arxiv.org/pdf/2510.20339v1","authors":"[\"D. Kucharski\",\"A. Gaska\",\"T. Kowaluk\",\"K. Stepien\",\"M. Repalska\",\"B. Gapinski\",\"M. Wieczorowski\",\"M. Nawotka\",\"P. Sobecki\",\"P. Sosinowski\",\"J. Tomasik\",\"A. Wojtowicz\"]","published":"2025-10-23T08:38:18Z","proceeding":"physics.app-ph","tasks":"[\"physics.app-ph\",\"cs.AI\",\"cs.LG\",\"stat.AP\",\"stat.ML\"]","methods":"[]","has_code":false}