Machine-vision-based identification of broken inserts in edge profile milling heads

Fernández-Robles, Laura; Azzopardi, George; Alegre, Enrique; Petkov, Nicolai

doi:10.1016/j.rcim.2016.10.004

Cited by 77 publications

(42 citation statements)

References 35 publications

(23 reference statements)

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“…Our proposal [4] is very effective (accuracy equals to 99.61%) for the localisation of the cutting edges of inserts in an edge profile milling machine. Following up this result, we studied how to recognise broken inserts because it is critical for a proper tool monitoring system [5]. The method that we presented first localises the screws of the inserts and then determines the expected positions and orientations of the cutting edges using known geometrical information.…”

Section: Automatic Localisation Of Broken Inserts In Edge Profile Milmentioning

confidence: 99%

Recognition and retrieval of objects in diverse applications

Fernández-Robles

2018

ELCVIA

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This work proposes and evaluates object description and retrieval techniques in different real applications. It addresses the classification of boar spermatozoa according to acrosome integrity using several methods based on invariant local features. In addition, it provides two new methods for insert localisation and an automatic solution for the recognition of broken inserts in edge profile milling heads that can be set up in-process without delaying any machining operations. Finally, it evaluates different clusterings of keypoints for object retrieval and proposes a new descriptor, named colour COSFIRE, in the scope of the European project Advisory System Against Sexual Exploitation of Children.

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Section: Automatic Localisation Of Broken Inserts In Edge Profile Milmentioning

confidence: 99%

Recognition and retrieval of objects in diverse applications

Fernández-Robles

2018

ELCVIA

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“…This method results in fast and accurate tool state detection. Fernández-Robles et al [ 19 ] presented a reliable machine vision system to automatically detect inserts and determine if they were broken. The aforementioned studies show that tool identification and workpiece positioning based on computer vision technology have been well-applied in intelligent control and closed-loop acknowledgement of CNC machine tools.…”

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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“…Fernández-Robles et al developed an algorithm to measure the defects in cutting edges of milling inserts online without disturbing the machining operation. A three-stage algorithm consists of edge preserving smoothing filter, computation of image gradient, and assessment of damage of cutting edge using geometrical properties [15].…”

Section: Introductionmentioning

confidence: 99%

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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Machine-vision-based identification of broken inserts in edge profile milling heads

Cited by 77 publications

References 35 publications

Recognition and retrieval of objects in diverse applications

Recognition and retrieval of objects in diverse applications

Computer Vision Tool-Setting System of Numerical Control Machine Tool

Measurements of Tool Wear Parameters Using Machine Vision System

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