Applying images processing methods for automation measurement of tool-chip contact length in orthogonal cutting

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Applying images processing methods for automation measurement of tool-chip contact length in orthogonal cutting

FAVIER Camille, LE ROUX Julien, CALAMAZ Madalina, GIRARDOT Jérémie, LIMJE Preshit

Abstract. The simulation of machining process is an essential tool in the digitalization of the entire production chain. Currently, these simulations are not sufficiently precise to avoid the use of experimental tests in order to optimize machining operations and guarantee the quality of the machined parts. Some parameters, such as tool-chip contact length, are still underestimated, although they are critical for controlling heat transfer into the tool and implicitly its wear. In order to validate a numerical cutting simulation model, the tool-chip contact length experimentally measured should be used as a comparative quantity, in the same way as the cutting forces and the morphology of the chips is currently used. The objective of this paper is to propose an automation of tool-chip contact length measurements using image processing algorithms. The proposed algorithm was able to identify and measure the tool-chip contact length on more that 75% of images. The algorithm accuracy is evaluated by comparing computed and manually measured tool-chip contact length, for different cutting conditions. It was found that it overestimates the contact length, especially in the case where the image quality is lower.

Keywords
Chip Formation, Images Processing, Tool-Chip Contact Length

Published online 4/24/2024, 10 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: FAVIER Camille, LE ROUX Julien, CALAMAZ Madalina, GIRARDOT Jérémie, LIMJE Preshit, Applying images processing methods for automation measurement of tool-chip contact length in orthogonal cutting, Materials Research Proceedings, Vol. 41, pp 2075-2084, 2024

DOI: https://doi.org/10.21741/9781644903131-229

The article was published as article 229 of the book Material Forming

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

References
[1] M. Storchak, K. Drewle, C. Menze, T. Stehle, H.-C. Möhring, Determination of the Tool–Chip Contact Length for the Cutting Processes, Materials 15 (2022) 3264. https://doi.org/10.3390/ma15093264
[2] G. Sutter, Chip geometries during high-speed machining for orthogonal cutting conditions, International Journal of Machine Tools and Manufacture 45 (2005) 719–726. https://doi.org/10.1016/j.ijmachtools.2004.09.018
[3] S. Ahasan Iqbal, P. T. Mativenga, M. A. Sheikh, A comparative study of the tool–chip contact length in turning of two engineering alloys for a wide range of cutting speeds, Int. J. Advanced Manufacturing Technology, 42 (2009) 30-40. https://doi.org/10.1007/s00170-008-1582-6
[4] L. Ellersiek, C. Menze, F. Sauer, B. Denkena, H.-C. Mohring, V. Schulze, Evaluation of methods for measuring tool chip contact length in wet machining using different approaches (microtextured tool, in situ visualization and restricted contact tool), Production Engineering 16 (2022) 635–646. https://doi.org/10.1007/s11740-022-01127-w
[5] H. Klippel, S. Pflaum, M. Kuffa, K. Wegener, Automated evaluation of continuous and segmented chip geometries based on image processing methods and a convolutional neural network, Journal of Machine Engineering, 22(4) (2022) 115-132. https://doi.org/10.36897/jme/156091
[6] M. R. Chalak Qazani, V. Pourmostraghimi, M. Moayyedian, S. Pedrammehr, Estimation of tool–chip contact length using optimized machine learning in orthogonal cutting, Engineering Applications of Artificial Intelligence 114 (2022) 105118. https://doi.org/10.1016/j.engappai.2022.105118
[7] S. Arefin, X.Q. Zhang, A. Senthil Kumar, D. Wee Keong Neo, Y. Wang, Study of chip formation mechanism in one-dimensional vibration-assisted machining, Journal of Materials Processing Tech. 291 (2021) 117022. https://doi.org/10.1016/j.jmatprotec.2020.117022
[8] W. Grzesik, An integrated approach to evaluating the tribo-contact for coated cutting inserts, Wear 240 (2000) 9-18. https://doi.org/10.1016/S0043-1648(00)00324-0
[9] F. Barelli, V. Wagner, R. Laheurte, G. Dessein, Ph. Darnis, O. Cahuc, M. Mousseigne, Orthogonal cutting of TA6V alloys with chamfered tools: Analysis of tool–chip contact lengths. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231 (2017) 2384-2395. https://doi.org/10.1177/0954405416629589
[10] Information on https://docs.opencv.org/4.x/d4/d13/tutorial_py_filtering.html
[11] Information on https://pyimagesearch.com/2021/04/28/opencv-smoothing-and-blurring/
[12] Information on https://docs.opencv.org/3.4/d2/d2c/tutorial_sobel_derivatives.html
[13] J. D. Bakos, Chapter 4 – Memory optimization and video processing, in Embedded Systems, 2016, pp. 147-185. https://doi.org/10.1016/B978-0-12-800342-8.00004-3.
[14] Information on https://docs.opencv.org/3.4/d7/d4d/tutorial_py_thresholding.html
[15] Information on https://docs.opencv.org/3.4/db/df6/tutorial_erosion_dilatation.html
[16] Information on https://docs.opencv.org/3.4/d4/d73/tutorial_py_contours_begin.html
[17] C. F. Jekel, G. Venter, pwlf : A Python Library for Fitting 1D Continuous Piecewise Linear Functions, MIT, 2019.