Estimation of the Inference Quality of Machine Learning Models for Cutting Tools Inspection

Kacper Marciniak, Paweł Majewski, Jacek Reiner

2024

Abstract

The ongoing trend in industry to continuously improve the efficiency of production processes is driving the development of vision-based inspection and measurement systems. With recent significant advances in artificial intelligence, machine learning methods are becoming increasingly applied to these systems. Strict requirements are placed on measurement and control systems regarding accuracy, repeatability, and robustness against variation in working conditions. Machine learning solutions are often unable to meet these requirements - being highly sensitive to the input data variability. Given the depicted difficulties, an original method for estimation of inference quality is proposed. It is based on a feature space analysis and an assessment of the degree of dissimilarity between the input data and the training set described using explicit metrics proposed by the authors. The developed solution has been integrated with an existing system for measuring geometric parameters and determining cutting tool wear, allowing continuous monitoring of the quality of the obtained results and enabling the system operator to take appropriate action in case of a drop below the adopted threshold values.

Paper Citation

in Harvard Style

Marciniak K., Majewski P. and Reiner J. (2024). Estimation of the Inference Quality of Machine Learning Models for Cutting Tools Inspection. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 359-366. DOI: 10.5220/0012321900003660

in Bibtex Style

@conference{visapp24,
author={Kacper Marciniak and Paweł Majewski and Jacek Reiner},
title={Estimation of the Inference Quality of Machine Learning Models for Cutting Tools Inspection},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={359-366},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012321900003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Estimation of the Inference Quality of Machine Learning Models for Cutting Tools Inspection
SN - 978-989-758-679-8
AU - Marciniak K.
AU - Majewski P.
AU - Reiner J.
PY - 2024
SP - 359
EP - 366
DO - 10.5220/0012321900003660
PB - SciTePress