An automatic and accurate method for tool wear inspection using grayscale image probability algorithm based on bayesian inference

Robotics and Computer-Integrated Manufacturing - Tập 68 - Trang 102079 - 2021
Yingguang Li1, Wenping Mou1, Jingjing Li1, Changqing Liu1, James Gao2
1College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2School of Engineering, University of Greenwich, Chatham Maritime, Kent, ME4 4 TB, United Kingdom

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Tài liệu tham khảo

Nouri, 2015, Real-time tool wear monitoring in milling using a cutting condition independent method, Int. J. Machine Tools & Manuf., 89, 1, 10.1016/j.ijmachtools.2014.10.011

Kalvoda, 2010, A cutter tool monitoring in machining process using Hilbert–Huang transform, Int. J. Machine Tools and Manuf., 50, 495, 10.1016/j.ijmachtools.2010.01.006

Rizal, 2017, Cutting tool wear classification and detection using multi-sensor signals and Mahalanobis-Taguchi System, Wear, 376, 1759, 10.1016/j.wear.2017.02.017

Chen, 2019, Mechanism for material removal in ultrasonic vibration helical milling of ti-6al-4v alloy, Int. J. of Machine Tools & Manuf., 138, 1, 10.1016/j.ijmachtools.2018.11.001

Fernández-Robles, 2017, Machine-vision-based identification of broken inserts in edge profile milling heads, Robot Comput Integr Manuf, 44, 276, 10.1016/j.rcim.2016.10.004

Li, 2019, A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning, CIRP Annals-Manuf. Technol., 68, 487, 10.1016/j.cirp.2019.03.010

Lins, 2020, In-process machine vision monitoring of tool wear for cyber-physical production systems, Robot Comput Integr Manuf, 61, 10.1016/j.rcim.2019.101859

Rech, 2018, Toward a new tribological approach to predict cutting tool wear, CIRP Annals-Manuf. Technol., 67, 65, 10.1016/j.cirp.2018.03.014

Wang, 2019, Heterogeneous data-driven hybrid machine learning for tool condition prognosis, CIRP Annals-Manuf. Technol., 68, 455, 10.1016/j.cirp.2019.03.007

Sun, 2020, In-process tool condition forecasting based on a deep learning method, Robot Comput Integr Manuf, 64, 10.1016/j.rcim.2019.101924

Ratava, 2017, Tool condition monitoring in interrupted cutting with acceleration sensors, Robot Comput Integr Manuf, 47, 70, 10.1016/j.rcim.2016.11.008

Sortino, 2003, Application of statistical filtering for optical detection of tool wear, Int. J. Machine Tools and Manuf., 43, 493, 10.1016/S0890-6955(02)00266-3

Dutta, 2013, Application of digital image processing in tool condition monitoring: a review, CIRP J Manuf. Sci Technol., 6, 212, 10.1016/j.cirpj.2013.02.005

Wang, 2014, Fast 3D reconstruction of tool wear based on monocular vision and multi-color structured light illuminator

Čerče, 2015, A new approach to spatial tool wear analysis and monitoring, Strojniški vestnik-J Mechanic. Eng., 61, 489, 10.5545/sv-jme.2015.2512

Čerče, 2015, 3D cutting tool-wear monitoring in the process, J. Mechan. Sci. Technol., 29, 3885, 10.1007/s12206-015-0834-2

Du, 2018, An investigation on measurement and evaluation of tool wear based on 3D topography, Int. J Manuf. Res., 13, 168, 10.1504/IJMR.2018.093263

Zapico, 2017, Cutting-tool wear characterization by means of conoscopic holography, Proce. Manuf., 13, 13

Liu, 2014, Study on Volumetric tool wear measurement using image processing, 1194

Malakizadi, 2016, An FEM-based approach for tool wear estimation in machining, Wear, 368, 10, 10.1016/j.wear.2016.08.007

Li, 2016, An in-depth study of tool wear monitoring technique based on image segmentation and texture analysis, Measurement, 79, 44, 10.1016/j.measurement.2015.10.029

Li, 2017, Detection Method of NC Tool Wear Status Based on Region Growth Method, Manuf. Technol. Machine Tools, 2, 132

Teresa Garcia-Ordas, 2018, Combining shape and contour features to improve tool wear monitoring in milling processes, Int. J. Prod. Res., 56, 3901, 10.1080/00207543.2018.1435919

Zhang, 2013, On-line tool wear measurement for ball-end milling cutter based on machine vision, Computers in ind., 64, 708, 10.1016/j.compind.2013.03.010

Peng, 2019, Machine vision monitoring of tool wear, Mechanic. Sci. Technol., 38, 1257

Dai, 2018, A machine vision system for micro-milling tool condition monitoring, Prec. eng., 52, 183, 10.1016/j.precisioneng.2017.12.006

Xiong, 2011, Cutting tool wear measurement by using active contour model based image processing, 670

Loizou, 2015, Automated wear characterization for broaching tools based on machine vision systems, J Manuf Sys., 37, 558, 10.1016/j.jmsy.2015.04.005

Mikolajczyk, 2018, Predicting tool life in turning operations using neural networks and image processing, Mech Syst Signal Process, 104, 503, 10.1016/j.ymssp.2017.11.022

D'Addona, 2015, Tool wear control through cognitive paradigms, Procedia CIRP, 33, 221, 10.1016/j.procir.2015.06.040

Fernandez-Zelaia, 2019, Statistical calibration and uncertainty quantification of complex machining computer models, Int. J Machine Tools & Manuf., 136, 45, 10.1016/j.ijmachtools.2018.09.004

Buades, 2005, A non-local algorithm for image denoising

Gao, 2020, Big data analytics for smart factories of the future, CIRP Annals – Manuf. Technol, 69, 668, 10.1016/j.cirp.2020.05.002

Wang, 2019, From intelligence science to intelligent manufacturing, Eng., 5, 615, 10.1016/j.eng.2019.04.011