Multi-Task Deep Learning for Surface Metrology
This work addresses the need for calibrated predictions to inform instrument selection in metrological workflows, representing an incremental improvement with domain-specific applications.
The paper tackled the problem of predicting surface texture parameters and their uncertainties in surface metrology using a multi-task deep learning framework, achieving high R2 scores (e.g., 0.9824 for Ra) and 92.85% accuracy for instrument classification, but noted challenges with RONt_uncert (R2 0.4934) and negative transfer in multi-output models.
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 -> 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.