Multi-Task Deep Learning for Surface Metrology Article Swipe
D. Kucharski
,
Adam Gąska
,
Tomasz Kowaluk
,
Krzysztof Stępień
,
Marta Rępalska
,
Bartosz Gapiński
,
Michał Wieczorowski
,
Michał Nawotka
,
Piotr Sobecki
,
P. Sosinowski
,
J. Tomášík
,
Adam Wójtowicz
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25247471
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25247471
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, we jointly address measurement system type classification and regression of key surface parameters—arithmetic mean roughness (Ra), mean peak-to-valley roughness (Rz), and total roundness deviation (RONt)—alongside their reported standard uncertainties. Uncertainty is modelled via quantile and heteroscedastic regression heads, with post hoc conformal calibration used to obtain calibrated prediction intervals. On a held-out test set, high fidelity was achieved by single-target regressors (coefficients of determination: Ra 0.9824, Rz 0.9847, RONt 0.9918), with two uncertainty targets also well modelled (standard uncertainty of Ra 0.9899, standard uncertainty of Rz 0.9955); the standard uncertainty of RONt remained more difficult to learn (0.4934). The classifier reached 92.85% accuracy, and probability calibration was essentially unchanged after temperature scaling (expected calibration error 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 for informing instrument selection and acceptance decisions in metrological workflows.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s25247471
- https://www.mdpi.com/1424-8220/25/24/7471/pdf?version=1765209331
- OA Status
- gold
- OpenAlex ID
- https://openalex.org/W4417123728
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4417123728Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s25247471Digital Object Identifier
- Title
-
Multi-Task Deep Learning for Surface MetrologyWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-12-08Full publication date if available
- Authors
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D. Kucharski, Adam Gąska, Tomasz Kowaluk, Krzysztof Stępień, Marta Rępalska, Bartosz Gapiński, Michał Wieczorowski, Michał Nawotka, Piotr Sobecki, P. Sosinowski, J. Tomášík, Adam WójtowiczList of authors in order
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https://doi.org/10.3390/s25247471Publisher landing page
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https://www.mdpi.com/1424-8220/25/24/7471/pdf?version=1765209331Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/25/24/7471/pdf?version=1765209331Direct OA link when available
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0Total citation count in OpenAlex
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| abstract_inverted_index.Ra | 94, 110 |
| abstract_inverted_index.Rz | 96, 115 |
| abstract_inverted_index.by | 88 |
| abstract_inverted_index.in | 178 |
| abstract_inverted_index.is | 5, 60 |
| abstract_inverted_index.of | 39, 92, 109, 114, 120 |
| abstract_inverted_index.on | 148 |
| abstract_inverted_index.to | 10, 74, 125 |
| abstract_inverted_index.we | 30 |
| abstract_inverted_index.The | 128 |
| abstract_inverted_index.and | 27, 37, 50, 64, 133, 175 |
| abstract_inverted_index.for | 7, 156, 171 |
| abstract_inverted_index.hoc | 70 |
| abstract_inverted_index.key | 40 |
| abstract_inverted_index.the | 117, 149 |
| abstract_inverted_index.two | 101 |
| abstract_inverted_index.via | 62 |
| abstract_inverted_index.was | 86, 136, 154 |
| abstract_inverted_index.→ | 146 |
| abstract_inverted_index.RONt | 98, 121 |
| abstract_inverted_index.also | 104 |
| abstract_inverted_index.deep | 2 |
| abstract_inverted_index.high | 84 |
| abstract_inverted_index.mean | 43, 46 |
| abstract_inverted_index.more | 123 |
| abstract_inverted_index.post | 69 |
| abstract_inverted_index.set, | 83 |
| abstract_inverted_index.test | 82, 150 |
| abstract_inverted_index.type | 35 |
| abstract_inverted_index.used | 73 |
| abstract_inverted_index.well | 105 |
| abstract_inverted_index.with | 16, 68, 100, 160 |
| abstract_inverted_index.(Ra), | 45 |
| abstract_inverted_index.(Rz), | 49 |
| abstract_inverted_index.These | 165 |
| abstract_inverted_index.Using | 21 |
| abstract_inverted_index.after | 139 |
| abstract_inverted_index.error | 144 |
| abstract_inverted_index.learn | 126 |
| abstract_inverted_index.naive | 157 |
| abstract_inverted_index.their | 17, 55 |
| abstract_inverted_index.total | 51 |
| abstract_inverted_index.92.85% | 131 |
| abstract_inverted_index.heads, | 67 |
| abstract_inverted_index.models | 162 |
| abstract_inverted_index.obtain | 75 |
| abstract_inverted_index.system | 34 |
| abstract_inverted_index.0.00503 | 147 |
| abstract_inverted_index.0.00504 | 145 |
| abstract_inverted_index.0.9824, | 95 |
| abstract_inverted_index.0.9847, | 97 |
| abstract_inverted_index.0.9899, | 111 |
| abstract_inverted_index.address | 32 |
| abstract_inverted_index.better. | 164 |
| abstract_inverted_index.dataset | 24 |
| abstract_inverted_index.jointly | 31 |
| abstract_inverted_index.optical | 28 |
| abstract_inverted_index.predict | 11 |
| abstract_inverted_index.provide | 167 |
| abstract_inverted_index.reached | 130 |
| abstract_inverted_index.results | 166 |
| abstract_inverted_index.scaling | 141 |
| abstract_inverted_index.split). | 151 |
| abstract_inverted_index.surface | 8, 12, 41 |
| abstract_inverted_index.tactile | 26 |
| abstract_inverted_index.targets | 103 |
| abstract_inverted_index.texture | 13 |
| abstract_inverted_index.trunks, | 159 |
| abstract_inverted_index.0.9918), | 99 |
| abstract_inverted_index.0.9955); | 116 |
| abstract_inverted_index.Negative | 152 |
| abstract_inverted_index.achieved | 87 |
| abstract_inverted_index.fidelity | 85 |
| abstract_inverted_index.held-out | 81 |
| abstract_inverted_index.learning | 3 |
| abstract_inverted_index.modelled | 61, 106 |
| abstract_inverted_index.observed | 155 |
| abstract_inverted_index.quantile | 63 |
| abstract_inverted_index.remained | 122 |
| abstract_inverted_index.reported | 18, 56 |
| abstract_inverted_index.spanning | 25 |
| abstract_inverted_index.standard | 19, 57, 112, 118 |
| abstract_inverted_index.suitable | 170 |
| abstract_inverted_index.systems, | 29 |
| abstract_inverted_index.together | 15 |
| abstract_inverted_index.transfer | 153 |
| abstract_inverted_index.(0.4934). | 127 |
| abstract_inverted_index.(expected | 142 |
| abstract_inverted_index.(standard | 107 |
| abstract_inverted_index.accuracy, | 132 |
| abstract_inverted_index.conformal | 71 |
| abstract_inverted_index.decisions | 177 |
| abstract_inverted_index.deviation | 53 |
| abstract_inverted_index.difficult | 124 |
| abstract_inverted_index.framework | 4 |
| abstract_inverted_index.informing | 172 |
| abstract_inverted_index.metrology | 9 |
| abstract_inverted_index.presented | 6 |
| abstract_inverted_index.roughness | 44, 48 |
| abstract_inverted_index.roundness | 52 |
| abstract_inverted_index.selection | 174 |
| abstract_inverted_index.unchanged | 138 |
| abstract_inverted_index.acceptance | 176 |
| abstract_inverted_index.calibrated | 76, 168 |
| abstract_inverted_index.classifier | 129 |
| abstract_inverted_index.instrument | 173 |
| abstract_inverted_index.intervals. | 78 |
| abstract_inverted_index.parameters | 14 |
| abstract_inverted_index.performing | 163 |
| abstract_inverted_index.prediction | 77 |
| abstract_inverted_index.regression | 38, 66 |
| abstract_inverted_index.regressors | 90 |
| abstract_inverted_index.workflows. | 180 |
| abstract_inverted_index.Uncertainty | 59 |
| abstract_inverted_index.calibration | 72, 135, 143 |
| abstract_inverted_index.essentially | 137 |
| abstract_inverted_index.measurement | 33 |
| abstract_inverted_index.predictions | 169 |
| abstract_inverted_index.probability | 134 |
| abstract_inverted_index.temperature | 140 |
| abstract_inverted_index.uncertainty | 102, 108, 113, 119 |
| abstract_inverted_index.metrological | 179 |
| abstract_inverted_index.multi-output | 158 |
| abstract_inverted_index.reproducible | 1 |
| abstract_inverted_index.(coefficients | 91 |
| abstract_inverted_index.single-target | 89, 161 |
| abstract_inverted_index.classification | 36 |
| abstract_inverted_index.determination: | 93 |
| abstract_inverted_index.peak-to-valley | 47 |
| abstract_inverted_index.uncertainties. | 20, 58 |
| abstract_inverted_index.heteroscedastic | 65 |
| abstract_inverted_index.multi-instrument | 23 |
| abstract_inverted_index.(RONt)—alongside | 54 |
| abstract_inverted_index.parameters—arithmetic | 42 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5071239103 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 12 |
| corresponding_institution_ids | https://openalex.org/I46597724 |
| citation_normalized_percentile |