Tool wear monitoring using a fast Hough transform of images of machined surfaces

Mannan, MA; Mian, Zhu; Kassim, Ashraf A.

doi:10.1007/s00138-004-0137-6

Cited by 16 publications

(7 citation statements)

References 19 publications

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“…The structural techniques describe the texture, using the pattern primitives, and their placement rules. Mannan et al [13] and Kassim et al [14] described structural techniques based on the Hough transform of machined surface images to monitor the wear of the single point and milling cutting tool. The structural technique provides very good results as long as it deals with deterministic patterns.…”

Section: Texture Analysismentioning

confidence: 99%

Texture analysis for grinding wheel wear assessment using machine vision

Arunachalam

Ramamoorthy

2007

Proceedings of the Institution of Mechanical Engineers, Part B:

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The surface quality of the components produced in a grinding process mainly depends on the grinding wheel topography. To maintain the quality of the component, the condition of the grinding wheel should be monitored and the periodical dressing has to be done to retain the shape and the sharpness of the cutting edges. In the present paper, the machine-vision-based texture analysis methods are introduced to discriminate the surface condition of the grinding wheel. The grinding wheel images are acquired using a charge coupled device (CCD) camera at different intervals of time during the process and the texture of the grinding wheel image is analysed using appropriate statistical and fractal methods. The variation of the parameters with the condition of the grinding wheel surface is analysed and presented. The evaluated parameters are clearly discriminating the texture of the grinding wheel at every stage. The methods presented in this paper can be used to assess the grinding wheel condition effectively.

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Section: Texture Analysismentioning

confidence: 99%

Texture analysis for grinding wheel wear assessment using machine vision

Arunachalam

Ramamoorthy

2007

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…The width of maximum wear was represented based on the spatial domain. Mannan et al [18] Classified the workpiece surface texture in different types of machine tools and different cutting parameters by the method of rapid Hough transform, which applied to flexible manufacture system and tool condition monitoring. Sun et al [19] researched the algorithm of principal component analysis (PCA) to reconstruct worn image, from which fractal character was extracted to estimate tool life.…”

Section: Introductionmentioning

confidence: 99%

Tool Wear Inspection and Classiﬁcation by Artiﬁcial Neural Network Based on Vision System and Cutting Force Signal.

Abdeltawab

Zhang

et al. 2021

Preprint

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The current work focuses on the cutting tool condition monitoring of end milling based on direct and indirect approach in machining AISI H13 alloy steel. Indirect process parameters such as cutting force signals are measured as responses using force sensor. In order to successfully inspect the milling tool life online for direct approaches, an automated machine vision system was used for tool condition monitoring. The image processing algorithms are developed to extract different features of rotating milling tool. A detection and compensation system for tool wear based on machine vision is designed. Feedforward Back-Propagation Neural Network applied for tool wear classification developed based on many force features. Ten time-domain features extracted and the sensitive features is determined based on Pearson’s correlation coefficient. I-kaz method which integrates between kurtosis and standard deviation is added as input feature with the ten time-domain features. A strong correlation is established between most of time-domain features and tool wear with high correlation coefficient. ANN model applied for classification tool states as normal and abnormal. Experiments with vision system have shown that area of wear at bottom and ﬂank is suitable to inspect in-process. Actual measurements of the tool wear stages are possible to identify the abnormality in cutting using vision system. ANN model showed superior results for tool states classification. The mean squared Error (MSE) for classification model was less than 6.7E-09 and R equal to 1. The model can be used to construct fault estimation mode for tool wear online classification and inspection.

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“…Authors used a set of sensors to collect the information on cutting force, vibration and frequency that was used to extract the fractal dimensions and ultimately flank wear using recurrent neural network. Hough transform was implemented on the texture of machined surface images to detect line segments formed during machining [10,11]. Results showed a strong correlation between the features extracted by Hough Transform and tool wear using multilayer perceptron neural network.…”

Section: Introductionmentioning

confidence: 99%

CNN based tool monitoring system to predict life of cutting tool

Ambadekar¹,

Choudhari

2020

SN Appl. Sci.

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In this study, we present tool wear prediction system to monitor the flank wear of a cutting tool by Machine Learning technique namely, Convolutional Neural Network (CNN). Experimentations were performed on mild steel components under dry cutting condition by carbide inserts as cutting tool. Images of cutting tool and turned component were taken at regular interval using an inverted microscope to measure the progression of flank wear and the corresponding image of component was noted. These images were used as an input to the CNN model that extract the features and classify cutting tool in one of the three wear class namely, low, medium and high. The result of the CNN training set was used to monitor the life of cutting tool and predict its remaining useful life. In this work which is first of its kind, the CNN model gives an accuracy of 87.26% to predict the remaining useful life of a cutting tool. In particular, the study exhibits that CNN method gives good response to the data in the form of images, when used as an indicator of tool wear classification in different classes.

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Tool wear monitoring using a fast Hough transform of images of machined surfaces

Cited by 16 publications

References 19 publications

Texture analysis for grinding wheel wear assessment using machine vision

Texture analysis for grinding wheel wear assessment using machine vision

Tool Wear Inspection and Classiﬁcation by Artiﬁcial Neural Network Based on Vision System and Cutting Force Signal.

CNN based tool monitoring system to predict life of cutting tool

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