Automated wear characterization for broaching tools based on machine vision systems

Loizou, Jamie; Tian, Wenmeng; Robertson, John L.; Camelio, Jaime A.

doi:10.1016/j.jmsy.2015.04.005

Cited by 59 publications

(23 citation statements)

References 11 publications

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“…Wang et al, 2018). A system has been extensively studied and implemented using Computer Vision as an effective tool for quality control of products can be distinguished from object detection (Loizou et al, 2015;Tootooni et al, 2016), identification of goal (Fernández-Robles et al, 2017), control based on vision (Schmidt & Wang, 2014;T. Wang et al, 2018), and defective inspection of the product (Aminzadeh & Kurfess, 2015;Jeffrey Kuo et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

A Framework to Develop Intelligent System for Capturing Product Features Using Open Cv Technique

Saif

Yusof

Ahmed

et al. 2020

JTOM

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This research aims to propose an innovative framework using ISO14649 standard to detect defects in manufactured shaped objects or geometric surfaces of non-linear products of the CNC machine. The significant importance in order to recognize the potential to improve industry product quality inspection and encourage the waste of timing machines and product rejection. Open Computer Vision (Open CV) offers a smart, non-contact measurement and cost-effective technique to fulfil the requirements. The framework depends on the new technique of Open CV, which includes two parts: an intelligent selection of work-piece capturing the image for a particular inspection of the planar interfaces such as the hole, rectangular, pocket one, and the symmetric lighting model comparison approach for measurement of defects in the matched images. The contribution of this study is to build a structure in the computer vision method with a convolution neural network that predicts the classification of the feature for better accuracy and emphasizes the significant characteristics of the image processing technique coupled with experimental data on demanding image datasets and quality inspection measures.

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Section: Related Workmentioning

confidence: 99%

A Framework to Develop Intelligent System for Capturing Product Features Using Open Cv Technique

Saif

Yusof

Ahmed

et al. 2020

JTOM

View full text Add to dashboard Cite

show abstract

“…Kim et al [13] researched the mean values, maximum values, and area of tool wear, which improved the accuracy and efficiency of the detection. Loizou et al [14] collected worn images in the case of spindle motion to analyze tool wear area. Zhang et al [15] Used the new tool as the template and the gray level of the new tool image as the threshold.…”

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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“…These technologies have attracted considerable attention from local and international scholars. J. et al [ 16 ] presented a new measurement system that quantifies broaching tool wear on the basis of the overall wear area. The proposed method uses automated image cropping and digital imaging processing tools to determine the affected area without requiring any manual intervention.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision Tool-Setting System of Numerical Control Machine Tool

Hou

Zhang

Yang

2020

Sensors

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An automatic tool-setting and workpiece online detecting system was proposed to study the key technologies of next-generation intelligent vision computerized numerical control (CNC) machines. A computer vision automatic tool-setting system for a CNC machine was set up on the basis of the vision tool-setting principle. A rapid vision calibration method based on the position feedback from the CNC machine was proposed on the basis of the theory of traditional vision system calibration. The coordinate mapping relationship of the image and the CNC machine, the tool-setting mark point on the workpiece, and the tool tip were calibrated. The vision system performance testing and system calibration experiments were performed. Experimental results indicated that the time consumption was 128 ms in image processing. The precision of tool setting and measuring was less than 1 μm. The workpiece positioning and processing online detection function of the system can completely meet the requirements of visual CNC machine application, and the system has wide application prospects.

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Automated wear characterization for broaching tools based on machine vision systems

Cited by 59 publications

References 11 publications

A Framework to Develop Intelligent System for Capturing Product Features Using Open Cv Technique

A Framework to Develop Intelligent System for Capturing Product Features Using Open Cv Technique

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

Computer Vision Tool-Setting System of Numerical Control Machine Tool

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