Determination of Tool Wear in Turning Process Using Undecimated Wavelet Transform and Textural Features

Danesh, Mahdi; Khalili, Khalil

doi:10.1016/j.protcy.2015.02.015

Cited by 18 publications

(11 citation statements)

References 13 publications

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“…Plain and twill fabric detection methods can be classified into five aspects: Spectral [9,10], learning [11,12], statistical [13][14][15], model-based [16,17], and structural methods [18,19]. The spectral method based on the Wavelet transform [20] achieved 97.5% detection accuracy with five known defect types and a 93.3% detection accuracy (a slight drop) with three unknown defect types in an evaluation.…”

Section: Related Workmentioning

confidence: 99%

Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means

Zhang

et al. 2019

Applied Sciences

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In this paper, we present a robust and reliable framework based on L0 gradient minimization (LGM) and the fuzzy c-means (FCM) method to detect various fabric defects with diverse textures. In our framework, the L0 gradient minimization is applied to process the fabric images to eliminate the influence of background texture and preserve sharpened significant edges on fabric defects. Then, the processed fabric images are clustered by using the fuzzy c-means. Through continuous iterative calculation, the clustering centers of fabric defects and non-defects are updated to realize the defect regions segmentation. We evaluate the proposed method on various samples, which include plain fabric, twill fabric, star-patterned fabric, dot-patterned fabric, box-patterned fabric, striped fabric and statistical-texture fabric with different defect types and shapes. Experimental results demonstrate that the proposed method has a good detection performance compared with other state-of-the-art methods in terms of both subjective and objective tests. In addition, the proposed method is applicable to industrial machine vision detection with limited computational resources.

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Section: Related Workmentioning

confidence: 99%

Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means

Zhang

et al. 2019

Applied Sciences

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“…Characteristics of qualitative and quantitative morphology of tool wear are of great concern for researchers nowadays. More morphological features other than commonly considered parameter, i.e., average tool wear width, are required for better evaluation of the actual condition of tool which can affect machining process and quality of machined workpieces [2,5]. e study shows that there is prominent effect of these new tool wear parameters on producing quality workpieces and also has an economic advantage by making strategies for timely changing tool inserts.…”

Section: Introductionmentioning

confidence: 99%

“…ere are two main methods to measure tool wear: indirect and direct methods. In the indirect method, tool wear is estimated with the signals coming from different types of sensors such as surface texture of machined workpiece, acoustics, vibration, feed forces, and current consumption [1][2][3][4][5]. e tool wear prediction model is prepared based on the magnitude of collected signals.…”

Section: Introductionmentioning

confidence: 99%

“…Detailed review for the tool condition monitoring indicates that the machine vision system can be extremely useful for the direct measurement of various types of tool wear [6]. Some statistical approaches are also useful in conjunction with machine vision system to find tool wear [1,2,5,7,8]. Some researchers developed their own algorithm for the edge detection and segmentation of tool zone [1,8].…”

Section: Introductionmentioning

confidence: 99%

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Measurements of Tool Wear Parameters Using Machine Vision System

Thakre

Lad

Mala

2019

Modelling and Simulation in Engineering

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Monitoring tool wear is very important in machining industry as it may result in loss of dimensional accuracy and quality of finished product. This work includes the development of machine vision system for the direct measurement of flank wear of carbide cutting tool inserts. This system consists of a digital camera to capture the tool wear image, a good light source to illuminate the tool, and a computer for image processing. A new approach of inline automatic calibration of a pixel is proposed in this work. The captured images of carbide inserts are processed, and the segmented tool wear zone has been obtained by image processing. The vision system extracts tool wear parameters such as average tool wear width, tool wear area, and tool wear perimeter. The results of the average tool wear width obtained from the vision system are experimentally validated with those obtained from the digital microscope. An average error of 3% was found for measurements of all 12 carbide inserts. Scanning electron micrographs of the wear zone indicate the severe abrasion marks and damage to the cutting edge for higher machining time. This study indicates that the efficient and reliable vision system can be developed to measure the tool wear parameters.

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“…The sharp and dull tools can be distinguished based on the texture of a turned surface [15]. Danesh and Khalili [16] also proposed a method to determine tool wear from irregularities of surface texture. Undecimated wavelet transform was used to decompose the surface texture image into sub-images and was then analyzed by gray-level co-occurrence matrix (GLCM) to classify tool wear.…”

Section: Introductionmentioning

confidence: 99%

Application of Wavelet Analysis in Tool Wear Evaluation Using Image Processing Method

Kiow¹,

Muda²,

Pauline³

et al. 2018

IJET

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Tool wear plays a significant role for proper planning and control of machining parameters to maintain the product quality. However, existing tool wear monitoring methods using sensor signals still have limitations. Since the cutting tool operates directly on the workpiece during machining process, the machined surface provides valuable information about the cutting tool condition. Therefore, the objective of present study is to evaluate the tool wear based on the workpiece profile signature by using wavelet analysis. The effect of wavelet families, scale of wavelet and statistical features of the continuous wavelet coefficient on the tool wear is studied. The surface profile of workpiece was captured using a DSLR camera. Invariant moment method was applied to extract the surface profile up to sub-pixel accuracy. The extracted surface profile was analyzed by using continuous wavelet transform (CWT) written in MATLAB. The results showed that average, RMS and peak to valley of CWT coefficients at all scale increased with tool wear. Peak to valley at higher scale is more sensitive to tool wear. Haar was found to be more effective and significant to correlate with tool wear with highest R2 which is 0.9301.

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Determination of Tool Wear in Turning Process Using Undecimated Wavelet Transform and Textural Features

Cited by 18 publications

References 13 publications

Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means

Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means

Measurements of Tool Wear Parameters Using Machine Vision System

Application of Wavelet Analysis in Tool Wear Evaluation Using Image Processing Method

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