Detection of fracture in ceramic cutting tools from workpiece profile signature using image processing and fast Fourier transform

Lee, Woon Kiow; Ratnam, Mani Maran; Ahmad, Zainal Arifin

doi:10.1016/j.precisioneng.2015.11.001

Cited by 15 publications

(3 citation statements)

References 26 publications

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“…Other studies suggested measuring the wear and the edge profile of the cutting tool using predicted or developed theoretical models, simulation software and edges scanned 2D images . However, there are still some percentage difference and deviations values between the results gained by the theoretical models and the manufacturer data.…”

Section: Resultsmentioning

confidence: 99%

Impact of work hardening, tool wear and geometry response on machinability during turning AL‐6XN super austenitic stainless steel: A work hardening and wear studies on AL‐6XN alloy

Alabdullah

Polishetty

Nomani

2017

Materialwissenschaft Werkst

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This work aimed to identify the work‐hardening regions in shear zones when the AL‐6XN super austenitic stainless steel alloy was machined using a turning machine. A quick‐stop approach was used to generate frozen chip roots. Two cutting speeds of 94 m/min and 65 m/min, a feed rate of 0.2 mm/rev and depth of cut of 1 mm were applied in this study. Microhardness measurements were executed in the hardening region (workpiece–shear zone–chip zone). Results showed that the hardness values were significantly increased in the primary shear zone and the formed chip. The wear of the cutting tool used to machine the alloy was investigated using scanning electron microscope. Excessive notch wear was located on the cutting edge when 65 m/min cutting speed was utilized while at 94 m/min cutting speed, the cutting edge was completely damaged as a flake of the edge was removed due to hard cutting processes. The profile measurement of the cutting tool was executed using an optical profilometer to reveal the changes in the cutting edges locations and profiles and their implication on machining process.

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Section: Resultsmentioning

confidence: 99%

Impact of work hardening, tool wear and geometry response on machinability during turning AL‐6XN super austenitic stainless steel: A work hardening and wear studies on AL‐6XN alloy

Alabdullah

Polishetty

Nomani

2017

Materialwissenschaft Werkst

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“…With the continuous progress of image processing methods, machine vision technology has been applied in the field of ceramic material detection in recent years [8]. For example, Lee et al [9] detected the occurrence of fracture in ceramic cutting tool inserts using the workpiece profile signature. This method extracted the edge profiles of workpiece to sub-pixel accuracy using invariant moment, and transformed the profiles into frequency domain using fast fourier transform to obtain the defects.…”

Section: Introductionmentioning

confidence: 99%

Surface non-destructively inspection of annular ceramic metal coating based on data augmentation and deep learning

LIANG,

Ye,

Wang

et al. 2024

Preprint

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The defects in the metal coating surface of annular ceramic workpiece have significant effects on the conductivity and reliability. Due to the irregularity, small area, and few sample number of defects, it is difficult to achieve efficient and accurate inspection. This paper presents a defect inspection framework based on deep learning for the metal coating surface of annular ceramic workpiece. Firstly, an image acquisition system for the coating surface is designed, and the defects characteristics are analyzed. Then, a surface image data set is constructed through five data augmentation strategies in order to solve the problem of insufficient samples. Finally, a defect detection framework for ceramic metal coating surface based on improved YOLOv7 model is established. By optimizing the clustering algorithm of target box, introducing an attention mechanism, and improving the MPConv structure, the efficient and precise identification of different defects is realized. Experimental results show that the recognition rate of defects including scratch, deficiency, scuffing, and dot is higher than 94%, and the average detection time is about 30 ms. The proposed detection framework based on deep learning shows great application potential in the fields of precise coating and manufacturing of ceramic materials.

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“…Koçer investigated the relationship between surface roughness and image grey [11]. Lee reported a workpiece profile acquisition method using image processing and fast Fourier transform [12,13]. All the above mentioned methods require prior knowledge or equipment transformation for the roughness measurement.…”

Section: Introductionmentioning

confidence: 99%

Noncontact Surface Roughness Estimation Using 2D Complex Wavelet Enhanced ResNet for Intelligent Evaluation of Milled Metal Surface Quality

et al. 2018

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Machined surfaces are rough from a microscopic perspective no matter how finely they are finished. Surface roughness is an important factor to consider during production quality control. Using modern techniques, surface roughness measurements are beneficial for improving machining quality. With optical imaging of machined surfaces as input, a convolutional neural network (CNN) can be utilized as an effective way to characterize hierarchical features without prior knowledge. In this paper, a novel method based on CNN is proposed for making intelligent surface roughness identifications. The technical scheme incorporates there elements: texture skew correction, image filtering, and intelligent neural network learning. Firstly, a texture skew correction algorithm, based on an improved Sobel operator and Hough transform, is applied such that surface texture directions can be adjusted. Secondly, two-dimensional (2D) dual tree complex wavelet transform (DTCWT) is employed to retrieve surface topology information, which is more effective for feature classifications. In addition, residual network (ResNet) is utilized to ensure automatic recognition of the filtered texture features. The proposed method has verified its feasibility as well as its effectiveness in actual surface roughness estimation experiments using the material of spheroidal graphite cast iron 500-7 in an agricultural machinery manufacturing company. Testing results demonstrate the proposed method has achieved high-precision surface roughness estimation.

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Detection of fracture in ceramic cutting tools from workpiece profile signature using image processing and fast Fourier transform

Cited by 15 publications

References 26 publications

Impact of work hardening, tool wear and geometry response on machinability during turning AL‐6XN super austenitic stainless steel: A work hardening and wear studies on AL‐6XN alloy

Impact of work hardening, tool wear and geometry response on machinability during turning AL‐6XN super austenitic stainless steel: A work hardening and wear studies on AL‐6XN alloy

Surface non-destructively inspection of annular ceramic metal coating based on data augmentation and deep learning

Noncontact Surface Roughness Estimation Using 2D Complex Wavelet Enhanced ResNet for Intelligent Evaluation of Milled Metal Surface Quality

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