Metrology & Hallmark

https://gum.gov.pl/wye/content/volume-29/6016,Machine-Learning-Based-Selection-of-Measurement-Technique-for-Surface-Metrology-.html
13.04.2026, 14:16

Machine Learning-Based Selection of Measurement Technique for Surface Metrology: A pilot study

Authors Dawid Kucharski, Bartosz Gapiński, Michał Wieczorowski - Institute of Mechanical Technology, Poznan University of Technology; Adam Gąska, Jerzy Sładek - Laboratory of Coordinate Metrology, Cracow University of Technology; Tomasz Kowaluk, Marta Rępalska, Jan Tomasik - Institute of Metrology and Biomedical Engineering, Warsaw University of Technology; Krzysztof Stępień, Włodzimierz Makieła - Department of Metrology and Non-conventional Manufacturing Methods, Kielce University of Technology; Michał Nawotka, Łukasz Ślusarski - Central Office of Measures, Warsaw.

Abstract

The study introduces the application of machine learning (ML) for surface texture metrology in decision-making support for measurement system preliminary selection. The paper delves into the intricate data filtering considerations and the diverse metrological parameters involved across different measurement techniques. Tailored to the specifics of the measuring object, surface texture parameters, and factors such as measurement technique and uncertainty, the algorithm developed offers predictive capabilities. Drawing from a database of available metrological devices streamlines the operator's task by predicting the appropriate system before conducting measurements. Preliminary results from the validation of prediction models are also provided.

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