On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression

Dutta, Samik; Pal, Surjya K.; Sen, Ranjan

doi:10.1016/j.precisioneng.2015.06.007

Cited by 98 publications

(45 citation statements)

References 26 publications

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“…Different of wavelet function can affected the CWT coefficient value and different CWT coefficient value can have a good or bad relation with the tool wear. In order of CWT to correlate the tool wear condition, the mean average, RMS and peak to valley of CWT coefficient value at low (20), medium (60) and high (100) scales was calculated and extracted. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…In recent year, Bhat et al [19] developed a novel technique for classification of tool condition by using a kernel-based support vector machine method rest on the derived attributes obtained from the GLCM of machined surface. Similar work has also been done by Dutta et al [20]. GLCM, Voronoi tessellation, and discrete wavelet transform based methods is proposed to extract the informative features from surface texture for evaluating the flank wear using SVM based regression models.…”

Section: Introductionmentioning

confidence: 87%

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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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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 87%

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

Kiow¹,

Muda²,

Pauline³

et al. 2018

IJET

View full text Add to dashboard Cite

show abstract

“…• Wavelet transform and GLCM 142,143 • Binary Gabor filter 144 • SVD and DWT transform 145 • Gabor filter and GLCM 146…”

Section: Related Workmentioning

confidence: 99%

Study of statistical methods for texture analysis and their modern evolutions

Ramola

Shakya

Pham

2020

Engineering Reports

100

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Texture analysis is widely performed in the current time as it is considered as an intimate property of the surface. It is widely used in the field of image processing, remote sensing applications, biomedical analysis, document processing, and so on. In this investigation, we present a detailed study of four different methodologies that have been developed for texture classification. These methodologies include gray level cooccurrence matrix (GLCM), local binary pattern (LBP), autocorrelation function (ACF), and histogram pattern. The detailed investigation on these methods suggests that GLCM is best for analyzing the surface texture, land-use/landcover classification, and satellite data processing. LBP is widely used to analyze the facial features of an individual. The autocorrelation is used to identify the regularity of the textured surface. Finally, through histograms, one can visually identify the changes developed while analyzing the texture of the image data. Furthermore, we present a brief summary for newly developed texture classification techniques such as binary Gabor pattern, local spiking pattern, SRITCSD method, scale inversion, and deep perception models for texture analysis. Some benchmark texture datasets used in image processing are also discussed in this work. K E Y W O R D Sautocorrelation function, gray level cooccurrence matrix, histogram, local binary pattern, texture 1 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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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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On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression

Cited by 98 publications

References 26 publications

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

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

Study of statistical methods for texture analysis and their modern evolutions

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

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