Image edge detection based tool condition monitoring with morphological component analysis.

The measurement and monitoring of tool condition are keys to the product precision in the automated manufacturing. To meet the need, this study proposes a novel tool wear monitoring approach based on the monitored image edge detection. Image edge detection has been a fundamental tool to obtain features of images. This approach extracts the tool edge with morphological component analysis. Through the decomposition of original tool wear image, the approach reduces the influence of texture and noise for edge measurement. Based on the target image sparse representation and edge detection, the approach could accurately extract the tool wear edge with continuous and complete contour, and is convenient in charactering tool conditions. Compared to the celebrated algorithms developed in the literature, this approach improves the integrity and connectivity of edges, and the results have shown that it achieves better geometry accuracy and lower error rate in the estimation of tool conditions.

[1]  Bing Li,et al.  A weighted multi-scale morphological gradient filter for rolling element bearing fault detection. , 2011, ISA transactions.

[2]  Eduardo Carlos Bianchi,et al.  Tool Condition Monitoring of Single-Point Dresser Using Acoustic Emission and Neural Networks Models , 2014, IEEE Transactions on Instrumentation and Measurement.

[3]  Colin Bradley,et al.  A machine vision system for tool wear assessment , 1997 .

[4]  Wen-June Wang,et al.  A novel edge detection method based on the maximizing objective function , 2007, Pattern Recognit..

[5]  Marc Thomas,et al.  Cyclostationarity approach for monitoring chatter and tool wear in high speed milling , 2014 .

[6]  Jianning Chi,et al.  Enhancement of Textural Differences Based on Morphological Component Analysis , 2015, IEEE Transactions on Image Processing.

[7]  Davud Asemani,et al.  Surface defect detection in tiling Industries using digital image processing methods: analysis and evaluation. , 2014, ISA transactions.

[8]  Wei Liu,et al.  A Hybrid FEM/MoM Technique for 3-D Electromagnetic Scattering From a Dielectric Object Above a Conductive Rough Surface , 2016, IEEE Geoscience and Remote Sensing Letters.

[9]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[11]  Bryan W. Scotney,et al.  Edge Detecting for Range Data Using Laplacian Operators , 2010, IEEE Transactions on Image Processing.

[12]  Guillermo Sapiro,et al.  Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.

[13]  Wenhui Wang,et al.  Flank wear measurement by a threshold independent method with sub-pixel accuracy , 2006 .

[14]  G. Yen,et al.  Fault classification on vibration data with wavelet based feature selection scheme , 2006, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[15]  Ming-Chyuan Lu,et al.  Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling , 2011, The International Journal of Advanced Manufacturing Technology.

[16]  Dawei Qi,et al.  Multi-scale Edge Detection of Wood Defect Images Based on the Dyadic Wavelet Transform , 2010, 2010 International Conference on Machine Vision and Human-machine Interface.

[17]  Enrique Alegre,et al.  Use of contour signatures and classification methods to optimize the tool life in metal machining , 2009, Estonian Journal of Engineering.

[18]  Krzysztof Jemielniak,et al.  Advanced monitoring of machining operations , 2010 .

[19]  Hare Krishna Mohanta,et al.  Online monitoring and control of particle size in the grinding process using least square support vector regression and resilient back propagation neural network. , 2015, ISA transactions.

[20]  Johan De Vriendt.  Accuracy of the zero crossings of the second directional derivative as an edge detector , 1993, Multidimens. Syst. Signal Process..

[21]  Weidong Li,et al.  The milling tool wear monitoring using the acoustic spectrum , 2012 .

[22]  A Volkan Atli,et al.  A computer vision-based fast approach to drilling tool condition monitoring , 2006 .

[23]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[24]  Michael Elad,et al.  Submitted to Ieee Transactions on Image Processing Image Decomposition via the Combination of Sparse Representations and a Variational Approach , 2022 .

[25]  Xiong Chen,et al.  Remote sensing image denoising application by generalized morphological component analysis , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[26]  Mohamed-Jalal Fadili,et al.  Morphological Component Analysis: An Adaptive Thresholding Strategy , 2007, IEEE Transactions on Image Processing.

[27]  Zhu Mian,et al.  Connectivity oriented fast Hough transform for tool wear monitoring , 2004, Pattern Recognit..

[28]  Frank L. Lewis,et al.  Tool Wear Monitoring Using Acoustic Emissions by Dominant-Feature Identification , 2011, IEEE Transactions on Instrumentation and Measurement.

[29]  Farhat Fnaiech,et al.  Application of higher order spectral features and support vector machines for bearing faults classification. , 2015, ISA transactions.

[30]  Biao Huang,et al.  Process monitoring using kernel density estimation and Bayesian networking with an industrial case study. , 2015, ISA transactions.

[31]  Antonio J. Plaza,et al.  Multiple Morphological Component Analysis Based Decomposition for Remote Sensing Image Classification , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Birgit Vogel-Heuser,et al.  Sparse representation and its applications in micro-milling condition monitoring: noise separation and tool condition monitoring , 2014 .

[33]  Tony Lindeberg.  Edge Detection and Ridge Detection with Automatic Scale Selection , 2004, International Journal of Computer Vision.

[34]  M. Sortino,et al.  Application of statistical filtering for optical detection of tool wear , 2003 .

[35]  Y. S. Tarng,et al.  An automated flank wear measurement of microdrills using machine vision , 2006 .

[36]  W. Wang,et al.  Flank wear measurement by successive image analysis , 2005, Comput. Ind..

[37]  Robert M. Haralick,et al.  Digital Step Edges from Zero Crossing of Second Directional Derivatives , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Yen-Wei Chen,et al.  A Machine Learning-Based Framework for Automatic Visual Inspection of Microdrill Bits in PCB Production , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[39]  G G Yen,et al.  Pattern classification by a neurofuzzy network: application to vibration monitoring. , 2000, ISA transactions.