Abstract
In the manufacture of metal parts, it is crucial to measure the tool wear accurately and quickly to improve machining automation, avoid tool change error, and perform tool wear compensation. In this study, an image-based on-machine and direct measurement model is proposed for the circumferential cutting edge wear of a milling cutter. First, a charge-coupled device sensor is applied to collect the one-dimensional (1D) projected images of the cross section on a circumferential cutting edge. The multiple-frame 1D images under one rotation angle of cutter are stitched into a rectangular image. Then, the multiple rectangular images in one rotation of the cutter are stitched to form a two-dimensional (2D) image. Also, the image edges are positioned through the image edge detection algorithms. Finally, based on the outer edges of the rectangular images for a same cutting edge, the tool diameter is measured; from the relationship of the radius wear and the flank wear on a circumferential cutting edge, the flank wear is obtained. The improved watershed algorithm based on the marked grayscale gradient image, the pixel level positioning algorithm based on Sobel operator, and the sub-pixel level positioning algorithm based on the grayscale moment are adopted for image edge detection. The performed experiments validate that the proposed image-based method is an effective way for measuring wear of the circumferential cutting edge of a milling cutter.
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Jamshidi M, Rimpault X, Balazinski M, Chatelain J (2020) Fractal analysis implementation for tool wear monitoring based on cutting force signals during CFRP/titanium stack machining. Int J Adv Manuf Technol 106:3859–3868. https://doi.org/10.1007/s00170-019-04880-y
Liu X, Wang W, Jiang R, Xiong Y, Lin K (2020) Tool wear mechanisms in axial ultrasonic vibration assisted milling in-situ TiB2/7050Al metal matrix composites. Advances in Manufacturing 8:252–264. https://doi.org/10.1007/s40436-020-00294-2
Cu F, Zuperl U (2011) Real-time cutting tool condition monitoring in milling. Strojniski Vestnik/Journal of Mechanical Engineering 57:142–150. https://doi.org/10.5545/sv-jme.2010.079
Krishnakumar P, Rameshkumar K, Ramachandran KI (2018) Machine learning based tool condition classification using acoustic emission and vibration data in high speed milling process using wavelet features. Intelligent Decision Technologies 12:265–282. https://doi.org/10.3233/IDT-180332
Da Silva RHL, Da Silva MB, Hassui A (2016) A probabilistic neural network applied in monitoring tool wear in the end milling operation via acoustic emission and cutting power signals. Mach Sci Technol 20:386–405. https://doi.org/10.1080/10910344.2016.1191026
Wu G, Li G, Pan W, Wang X, Ding S (2020) A prediction model for the milling of thin-wall parts considering thermal-mechanical coupling and tool wear. Int J Adv Manuf Technol 107:4645–4659. https://doi.org/10.1007/s00170-020-05346-2
Pattnaik SK, Behera M, Padhi S, Dash P, Sarangi SK (2020) Study of cutting force and tool wear during turning of aluminium with WC, PCD and HFCVD coated MCD tools. Manuf Rev. https://doi.org/10.1051/mfreview/2020026
Du D, Sun J, Yang S, Chen W (2018) An investigation on measurement and evaluation of tool wear based on 3D topography. Int J Manuf Res 13:168–182. https://doi.org/10.1504/IJMR.2018.093263
Kim J, Moon D, Lee D, Kim J, Kang M, Kim KH (2002) Tool wear measuring technique on the machine using CCD and exclusive jig. J Mater Process Technol 130–131:668–674. https://doi.org/10.1016/S0924-0136(02)00733-1
Jurkovic J, Korosec M, Kopac J (2005) New approach in tool wear measuring technique using CCD vision system. Int J Mach Tools Manuf 45:1023–1030. https://doi.org/10.1016/j.ijmachtools.2004.11.030
Wang WH, Hong GS, Wong YS (2006) Flank wear measurement by a threshold independent method with sub-pixel accuracy. Int J Mach Tools Manuf 46:199–207. https://doi.org/10.1016/j.ijmachtools.2005.04.006
Castejon M, Alegre E, Barreiro J, Hernandez LK (2007) On-line tool wear monitoring using geometric descriptors from digital images. Int J Mach Tools Manuf 47:1847–1853. https://doi.org/10.1016/j.ijmachtools.2007.04.001
Zhang J, Zhang C, Guo S, Zhou L (2012) Research on tool wear detection based on machine vision in end milling process. Prod Eng Res Devel 6:431–437. https://doi.org/10.1007/s11740-012-0395-5
Zhi G, He D, Sun W, Zhou Y, Pan X, Gao C (2021) An edge-labeling graph neural network method for tool wear condition monitoring using wear image with small samples. Meas Sci Technol. https://doi.org/10.1088/1361-6501/abe0d9
Wu X, Liu Y, Zhou X, Mou A (2019) Automatic identification of tool wear based on convolutional neural network in face milling process. Sensors (Switzerland). https://doi.org/10.3390/s19183817
Bergs T, Holst C, Gupta P, Augspurger T (2020) Digital image processing with deep learning for automated cutting tool wear detection. Procedia Manufacturing 48:947–958. https://doi.org/10.1016/j.promfg.2020.05.134
Sun X, Xu Q, Zhu L (2019) An effective Gaussian fitting approach for image contrast enhancement. IEEE Access 7:31946–31958. https://doi.org/10.1109/ACCESS.2019.2900717
Gester D, Simon S (2018) A spatial moments sub-pixel edge detector with edge blur compensation for imaging metrology, Houston, TX, United states, 2018[C]. Ins Electrical and Electronics Eng Inc
Fan Q, Zhang Y, Bao F, Yao X, Zhang C (2016) Rational function interpolation algorithm based on parameter optimization. Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics 28:2034–2042. https://doi.org/10.11834/jig.170369
Peng S, Su W, Hu X, Liu C, Wu Y, Nam H (2018) Subpixel edge detection based on edge gradient directional interpolation and Zernike moment. 2018 Int Conf Comp Sci Software Eng (CSSE 2018):106–116. https://doi.org/10.12783/dtcse/csse2018/24488
Bagga PJ, Makhesana MA, Patel KM (2021) A novel approach of combined edge detection and segmentation for tool wear measurement in machining. Prod Eng Res Devel 15:519–533. https://doi.org/10.1007/s11740-021-01035-5
Zhu K, Yu X (2017) The monitoring of micro milling tool wear conditions by wear area estimation. Mech Syst Signal Process 93:80–91. https://doi.org/10.1016/j.ymssp.2017.02.004
Nausheen N, Seal A, Khanna P, Halder S (2018) A FPGA based implementation of Sobel edge detection. Microprocess Microsyst 56:84–91. https://doi.org/10.1016/j.micpro.2017.10.011
Wang B, Chen LL, Cheng J (2018) New result on maximum entropy threshold image segmentation based on P system. Optik 163:81–85. https://doi.org/10.1016/j.ijleo.2018.02.062
Chen H, Shen X, Long J (2016) Threshold optimization framework of global thresholding algorithms using gaussian fitting. Jisuanji Yanjiu yu Fazhan/Computer Research and Development 53:892–903. https://doi.org/10.7544/issn1000-1239.2016.20140508
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This study is supported by the Jiangsu Provincial Key Research and Development Program (Grant No. BE2020779) and National Key Laboratory of Science and Technology on Helicopter Transmission (Grant No. HTL-O-21G10).
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Ruijun Liang designed the project and wrote the manuscript. Yang Li and Lei He performed the experiments. All authors participated in the interpretation of the results.
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Liang, R., Li, Y., He, L. et al. A novel image-based method for wear measurement of circumferential cutting edges of end mills. Int J Adv Manuf Technol 120, 7595–7608 (2022). https://doi.org/10.1007/s00170-022-09215-y
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DOI: https://doi.org/10.1007/s00170-022-09215-y