Abstract
This paper presents a reliable machine vision system to automatically estimate and visualize tool wear in micro milling manufacturing. The estimation of tool wear is very important for tool monitoring systems and image sensors configure a cheap and reliable solution. This system provides information to decide whether a tool should be replaced so the quality of the machined piece is ensured and the tool does not collapse. In the method that we propose, we first delimit the area of interest of the micro milling tool and then we delimit the worn area. The worn area is visualized and estimated while errors are computed against the ground truth proposed by experts. The method is mainly based on morphological operations and k-means algorithm. Other approaches based on pure morphological operations and on Otsu multi threshold algorithms were also tested. The obtained result (a harmonic mean of precision and recall 90.24 (±2.78)%) shows that the machine vision system that we present is effective and suitable for the estimation and visualization of tool wear in micro milling machines and ready to be installed in an on-line system.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Aliustaoglu, C., Ertunc, H.M., Ocak, H.: Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system. Mech. Syst. Signal Process. 23(2), 539–546 (2009). http://www.sciencedirect.com/science/article/pii/S0888327008000642
Canny, A.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 8(6), 679–698 (1986)
Cheng, K., Huo, D.H.: Micro Cutting: Fundamentals and Applications. Wiley, Chichester (2013)
Cheng, K., Niu, Z.C., Wang, R.C., Rakowski, R., Bateman, R.: Smart cutting tools and smart machining: development approaches, and their implementation and application perspectives. Chin. J. Mech. Eng. 30(5), 1162–1176 (2017). https://link.springer.com/article/10.1007/s10033-017-0183-4
Danielsson, P.E., Seger, O.: Generalized and separable sobel operators. In: Freeman, H. (ed.) Machine Vision for Three-Dimensional Scenes, pp. 347–379. Academic Press (1990). https://www.sciencedirect.com/science/article/pii/B9780122667220500166
D’Addona, D., Teti, R.: Image data processing via neural networks for tool wear prediction. Procedia CIRP 12, 252–257 (2013). http://www.sciencedirect.com/science/article/pii/S2212827113006859. Eighth CIRP Conference on Intelligent Computation in Manufacturing Engineering
Fernández-Robles, L., Azzopardi, G., Alegre, E., Petkov, N.: Machine-vision-based identification of broken inserts in edge profile milling heads. Rob. Comput. Integr. Manuf. 44, 276–283 (2017). http://www.sciencedirect.com/science/article/pii/S0736584515300806
Fernández-Robles, L., Azzopardi, G., Alegre, E., Petkov, N., Castejón-Limas, M.: Identification of milling inserts in situ based on a versatile machine vision system. J. Manuf. Syst. 45, 48–57 (2017). http://www.sciencedirect.com/science/article/pii/S0278612517301231
Geusebroek, J.M., Smeulders, A.W.M., van de Weijer, J.: Fast anisotropic gauss filtering. IEEE Trans. Image Process. 12(8), 938–943 (2003)
Hough, P.: Method and Means for Recognizing Complex Patterns. U.S. Patent 3.069.654, December 1962
Karuppusamy, N.S., Pal Pandian, P., Lee, H.S., Kang, B.Y.: Tool wear and tool life estimation based on linear regression learning. In: 2015 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 17–21, August 2015
Kovesi, P.: Fast almost-gaussian filtering. In: 2010 International Conference on Digital Image Computing: Techniques and Applications, pp. 121–125, December 2010
Leem, C.S., Dornfeld, D.A.: Design and implementation of sensor-based tool-wear monitoring systems. Mech. Syst. Signal Process. 10(4), 439–458 (1996). http://www.sciencedirect.com/science/article/pii/S088832709690031X
Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Schmitt, R., Cai, Y., Pavim, A.: Machine vision system for inspecting flank wear on cutting tools. ACEEE Int. J. Control Syst. Instrum. 3, 27–31 (2012)
Yang, Z., Liu, L., Peng, K., Li, S., Zhang, J., Gai, L.: Monitoring method of high-speed tool wear level based on machine vision. Int. J. Signal Process. Image Process. Pattern Recogn. 10(6), 23–38 (2017)
Yen, C.L., Lu, M.C., Chen, J.L.: Applying the self-organization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting. Mech. Syst. Signal Process. 34(1), 353–366 (2013). http://www.sciencedirect.com/science/article/pii/S0888327012001859
Zhu, K., Yu, X.: The monitoring of micro milling tool wear conditions by wear area estimation. Mech. Syst. Signal Process. 93, 80–91 (2017)
Acknowledgements
We gratefully acknowledge the financial support of Spanish Ministry of Economy, Industry and Competitiveness, through grant DPI2016-79960-C3-2-P.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Fernández-Robles, L., Charro, N., Sánchez-González, L., Pérez, H., Castejón-Limas, M., Alfonso-Cendón, J. (2018). Tool Wear Estimation and Visualization Using Image Sensors in Micro Milling Manufacturing. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-92639-1_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92638-4
Online ISBN: 978-3-319-92639-1
eBook Packages: Computer ScienceComputer Science (R0)