Current volume

Deep Learning Applications for Surface Texture Metrology: A Project Progress Report

Metrology & Hallmark

Authors Dawid Kucharski, Bartosz Gapiński, Michał Wieczorowski (Poznan University of Technology), Adam Gąska (Cracow University of Technology), Tomasz Kowaluk, Marta Rępalska, Jan Tomasik, (Warsaw University of Technology) Krzysztof Stępień (Kielce University of Technology), Michał Nawotka (Central Office of Measures), Piotr Sobecki (Central Office of Measures, National Information Processing Institute), Adam Wójtowicz (Central Office of Measures, Cracow University of Technology)

Artificial Intelligence Deep Learning metrology Surface Texture

Abstract

The paper provides a progress report on the project "Application of Artificial Intelligence in Surface Irregularities Measurements," funded by the Polish Ministry of Education and Science. The study utilises surface topography measurements from tactile and non-tactile systems to develop AI algorithms for metrology scenarios and predicting surface texture parameters. The deep learning approach achieved 93% accuracy in classifying measurement systems and a regression model with training and validation losses of approximately 9% and 2%. These advancements enhance accuracy, efficiency, and automation in surface characterisation, with potential applications in materials science, manufacturing, and quality control. The findings support AI-driven models as future decision-making tools in measurement preparation.

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Metrology and Hallmark is devoted to the multidisciplinary study and practice of high accuracy engineering and metrology. The journal takes novel achievements in all fields of measurement and instruments science & technology.

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