# Multi-Task Deep Learning for Surface Metrology

arXiv: https://arxiv.org/abs/2510.20339 · HTML: https://mlsanity.com/paper/2510.20339/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Builds on (most cited first, 2 of 2)

- [Fringe pattern analysis using deep learning](https://mlsanity.com/paper/1807.02757/) (2018)
- [DeepInspect: An AI-Powered Defect Detection for Manufacturing Industries](https://mlsanity.com/paper/2311.03725/) (2023)

## Abstract (arXiv)

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.