A system for automated tool wear monitoring and classification using computer vision

Friedrich, Markus; Gerber, Thomas C.; Dumler, Jonas; Döpper, Frank

doi:10.1016/j.procir.2023.06.073

Cited by 3 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A system for semi-automated tool wear monitoring and classification with the use of computer vision was developed by Friedrich et al [103]. A low-cost but high-resolution camera (12.3 megapixels 4056 × 3040) was placed in-machine and used for creating a database of worn and unused tools.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Degree of Degradation of Brake Pad Friction Surfaces Using Image Processing

Mandziy,

Ivasenko,

Berehulyak

et al. 2024

Lubricants

View full text Add to dashboard Cite

The improvement of drilling rig systems to ensure a reduction in unproductive time spent on lowering and lifting operations for replacing drilling tools and restoring the performance of drilling equipment units is an important task. At the same time, considerable attention is paid to the reliable and efficient operation of the braking systems of drilling rig winches. In the process of operation, the polymer pads periodically come into contact with the outer cylindrical surface of the metal pulley during braking, work in extreme conditions and wear out intensively, so they need periodic replacement. Tests were carried out on a modernized stand and in industrial conditions for the brakes of drilling winches. A methodology for evaluating the degradation of the brake pad friction surface during its operation is proposed. The assessment of the degradation degree is carried out based on the image of the brake pad surface using image processing techniques. Geometric transformations of the input image were performed to avoid perspective distortions caused by the concave shape of the brake pads and the spatial angle at which the image is acquired to avoid glares. The crack detection step was implemented based on the scale-space theory, followed by contour detection and skeletonization. The ratios of the area and perimeter of segmented and skeletonized cracks to the total area were chosen as integral characteristics of the degradation degree. With the help of scanning electron microscopy, the character of the destruction of the friction surface and the degradation of the polymer material was investigated. Experimental studies were performed, and the application of the proposed method is illustrated.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation of the Degree of Degradation of Brake Pad Friction Surfaces Using Image Processing

Mandziy,

Ivasenko,

Berehulyak

et al. 2024

Lubricants

View full text Add to dashboard Cite

show abstract

A Generative AI approach to improve in-situ vision tool wear monitoring with scarce data

Garcia-Perez,

Gomez-Silva,

de la Escalera-Hueso

2024

J Intell Manuf

View full text Add to dashboard Cite

An in-situ tool wear measurement method based on super-pixels and enhanced corner detection algorithm

Wang,

Wang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

In robotic milling scenarios, lighting conditions that are difficult to optimize and random angles of the cutting edge often lead to inaccuracies in in-situ tool wear detection. This paper proposes a method based on machine vision to accurately measure the wear parameters of end mills. Firstly, a cropping algorithm based on polar coordinate transformation is introduced to extract the Region of Interest (ROI) from the flank face of the tool bottom. Subsequently, a segmentation algorithm that combines super-pixels and multi-thresholding is proposed, with a focus on maintaining the connectivity of the damage regions during segmentation. Finally, an algorithm based on improved corner detection is presented to reconstruct the tool breakage region. Experimental results demonstrate that the proposed method achieves precise tool wear measurement despite random cutting edge angles and exhibits good robustness under varying lighting conditions. In the in-situ detection experiment, the method achieved a measurement accuracy of 97% for maximum wear width and 95% for wear area, indicating the effectiveness of the proposed method.

show abstract

A system for automated tool wear monitoring and classification using computer vision

Cited by 3 publications

References 4 publications

Evaluation of the Degree of Degradation of Brake Pad Friction Surfaces Using Image Processing

Evaluation of the Degree of Degradation of Brake Pad Friction Surfaces Using Image Processing

A Generative AI approach to improve in-situ vision tool wear monitoring with scarce data

An in-situ tool wear measurement method based on super-pixels and enhanced corner detection algorithm

Contact Info

Product

Resources

About