Machine Learning-based selection of measurement technique for surface metrology: a pilot study

• Autorzy: Kucharski Dawid , Gapiński Bartosz , Wieczorowski Michał , Gąska Adam , Sładek Jerzy , Kowaluk Tomasz , Rępalska Marta , Tomasik Jan , Stępień Krzysztof , Makieła Włodzimierz , Nawotka Michał , Ślusarski Łukasz
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

artificial intelligence; machine learning; surface metrology; data processing; coordinate measuring machine

• Wydawca: Główny Urząd Miar
• Czasopismo: Metrology & Hallmark
• Rocznik: 2024
• Tom: Vol. 1
• Strony: art. no. 3
• Opis fizyczny: Bibliogr. 28 poz.

Twórcy

autor

Kucharski Dawid

• Institute of Mechanical Technology, Poznan University of Technology

autor

Gapiński Bartosz

• Institute of Mechanical Technology, Poznan University of Technology

autor

Wieczorowski Michał

• Institute of Mechanical Technology, Poznan University of Technology

autor

Gąska Adam

• Laboratory of Coordinate Metrology, Cracow University of Technology

autor

Sładek Jerzy

• Laboratory of Coordinate Metrology, Cracow University of Technology

autor

Kowaluk Tomasz

• Institute of Metrology and Biomedical Engineering, Warsaw University of Technology

autor

Rępalska Marta

• Institute of Metrology and Biomedical Engineering, Warsaw University of Technology

autor

Tomasik Jan

• Institute of Metrology and Biomedical Engineering, Warsaw University of Technology

autor

Stępień Krzysztof

• Department of Metrology and Non-conventional Manufacturing Methods, Kielce University of Technology

autor

Makieła Włodzimierz

• Department of Metrology and Non-conventional Manufacturing Methods, Kielce University of Technology

autor

Nawotka Michał

• Central Office of Measures, Warsaw

autor

Ślusarski Łukasz

• Central Office of Measures, Warsaw

Bibliografia

• [1] A. M. Turing, “I.—COMPUTING machinery and intelligence,” Mind, vol. LIX, no. 236, pp. 433–460, Oct. 1950.
• [2] A. Eser, E. A. Ayyildiz, M. Ayyildiz, and F. Kara, “Artificial intelligence-based surface roughness estimation modelling for milling of aa6061 alloy,” Advances in Materials Science and Engineering, vol. 2021, 2021.
• [3] I. Abu-Mahfouz, A. E. Rahman, and A. Banerjee, “Surface roughness prediction in turning using three artificial intelligence techniques; a comparative study,” in Procedia Computer Science, 2018, vol. 140, pp. 258–267.
• [4] U. L. Adizue, B. Z. Balazs, and M. Takacs, “Surface roughness prediction applying artificial neural network at micro machining,” IOP Conference Series: Materials Science and Engineering, vol. 1246, no. 1, p. 012034, Aug. 2022.
• [5] A. Bustillo, D. Y. Pimenov, M. Matuszewski, and T. Mikolajczyk, “Using artificial intelligence models for the prediction of surface wear based on surface isotropy levels,” Robotics and Computer-Integrated Manufacturing, vol. 53, pp. 215–227, Oct. 2018.
• [6] D. Y. Pimenov, A. Bustillo, and T. Mikolajczyk, “Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth,” Journal of Intelligent Manufacturing, vol. 29, no. 5, pp. 1045–1061, Jun. 2018.
• [7] V. Dubey, A. K. Sharma, and D. Y. Pimenov, “Prediction of surface roughness using machine learning approach in mql turning of aisi 304 steel by varying nanoparticle size in the cutting fluid,” Lubricants, vol. 10, no. 5, p. 81, May 2022.
• [8] T. Batu, H. G. Lemu, and H. Shimels, “Application of artificial intelligence for surface roughness prediction of additively manufactured components,” Materials, vol. 16, no. 18, p. 6266, Sep. 2023.
• [9] D. Soler, M. Telleria, M. B. García-Blanco, E. Espinosa, M. Cuesta, and P. J. Arrazola, “Prediction of Surface roughness of slm built parts after finishing processes using an artificial neural network,” J ournal of Manufacturing and Materials Processing, vol. 6, no. 4, p. 82, Aug. 2022.
• [10] R. Leach, Optical measurement of surface topography. Springer Berlin Heidelberg, 2011, p. 323.
• [11] M. Liu, B. Cheung, N. Senin, S. Wang, R. Su, and R. Leach, “On-machine surface defect detection using light scattering and deep learning,” Journal of the Optical Society of America A, no. June, Jun. 2020.
• [12] F. Pan, B. Dong, W. Xiao, and P. Ferraro, “Stitching sub-aperture in digital holography based on machine learning,” Optics Express, vol. 28, no. 5, p. 6537, Mar. 2020.
• [13] C. Zuo, J. Qian, S. Feng, W. Yin, Y. Li, P. Fan, J. Han, K. Qian, and Q. Chen, “Deep learning in optical metrology: A review,” Light: Science & Applications, vol. 11, no. 1, p. 39, Dec. 2022.
• [14] O. Obajemu, M. Mahfouf, M. Papananias, T. E. McLeay, and V. Kadirkamanathan, “An interpretable machine learning based approach for process to areal Surface metrology informatics,” Surface Topography: Metrology and Properties, vol. 9, no. 4, 2021.
• [15] S. H. Mian, U. Umer, O. Abdulhameed, and H. Alkhalefah, “Prognosis and multiobjective optimization of the sampling plan for cylindricity evaluation,” Mathematical Problems in Engineering, vol. 2020, pp. 1–24, Nov. 2020.
• [16] S. Raman, R. C. Gilbert, and T. B. Trafalis, “Coordinate metrology for adaptive form verification,” Manufacturing Letters, vol. 1, nos. 2-4, pp. 59–61, Dec. 2013.
• [17] M. Wieczorowski, D. Kucharski, P. Sniatala, G. Krolczyk, P. Pawlus, and B. Gapinski, “Theoretical considerations on application of artificial intelligence in coordinate metrology,” in 2021 6th International Conference on Nanotechnology for Instrumentation and Measurement (NanofIM), 2021, pp. 1–4.
• [18] M. Wieczorowski, D. Kucharski, P. Sniatala, P. Pawlus, G. Krolczyk, and B. Gapinski, “A novel approach to using artificial intelligence in coordinate metrology including nano scale,” Measurement, vol. 217, p. 113051, Aug. 2023.
• [19] R Core Team, R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, 2020.
• [20] M. Kuhn, Caret: Classification and regression training. 2022.
• [21] J. Allaire and F. Chollet, Keras: R interface to ’keras’. 2023.
• [22] J. Allaire and Y. Tang, Tensorflow: R interface to ’ten-sorflow’. 2022.
• [23] B. S. Den, Uncertainty analysis of experimental data with r. Taylor & Francis Group, 2017, pp. 1–207.
• [24] “Evaluation of measurement data: Supplement 1 to the ‘Guide to the expression of uncertainty in measurement’—Propagation of distributions using aMonte Carlo method,” International Bureau of Weights; Measures (BIPM), Sérres, France, Standard, 2008.
• [25] Y. Xie, Knitr: A general-purpose package for dynamic report generation in r. 2022.
• [26] Y. Xie, Dynamic documents with R and knitr, 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC, 2015.
• [27] Y. Xie, “Knitr: A comprehensive tool for reproducible research in R,” in Implementing reproducible computational research, V. Stodden, F. Leisch, and R. D. Peng, Eds. Chapman; Hall/CRC, 2014.
• [28] J. Brownlee, “Your first machine learning project in R step-by-step.” https://machinelearningmastery.com/machine-learning-in-r-step-by-step/, 2016.