2017
1.
Fernández-Robles, Laura; Azzopardi, George; Alegre, Enrique; Petkov, Nicolai
Machine-vision-based identification of broken inserts in edge profile milling heads Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: machine vision, pattern recognition, visual quality inspection
@article{Fernandez2017,
title = {Machine-vision-based identification of broken inserts in edge profile milling heads},
author = {Laura Fern\'{a}ndez-Robles and George Azzopardi and Enrique Alegre and Nicolai Petkov},
doi = {https://doi.org/10.1016/j.rcim.2016.10.004},
year = {2017},
date = {2017-04-01},
urldate = {2017-04-01},
journal = {Robotics and Computer-Integrated Manufacturing},
volume = {44},
pages = {276-283},
abstract = {This paper presents a reliable machine vision system to automatically detect inserts and determine if they are broken. Unlike the machining operations studied in the literature, we are dealing with edge milling head tools for aggressive machining of thick plates (up to 12 centimetres) in a single pass. The studied cutting head tool is characterised by its relatively high number of inserts (up to 30) which makes the localisation of inserts a key aspect. The identification of broken inserts is critical for a proper tool monitoring system. In the method that we propose, we first localise the screws of the inserts and then we determine the expected position and orientation of the cutting edge by applying some geometrical operations. We compute the deviations from the expected cutting edge to the real edge of the inserts to determine if an insert is broken. We evaluated the proposed method on a new dataset that we acquired and made public. The obtained result (a harmonic mean of precision and recall 91.43%) shows that the machine vision system that we present is effective and suitable for the identification of broken inserts in machining head tools and ready to be installed in an on-line system.},
keywords = {machine vision, pattern recognition, visual quality inspection},
pubstate = {published},
tppubtype = {article}
}
This paper presents a reliable machine vision system to automatically detect inserts and determine if they are broken. Unlike the machining operations studied in the literature, we are dealing with edge milling head tools for aggressive machining of thick plates (up to 12 centimetres) in a single pass. The studied cutting head tool is characterised by its relatively high number of inserts (up to 30) which makes the localisation of inserts a key aspect. The identification of broken inserts is critical for a proper tool monitoring system. In the method that we propose, we first localise the screws of the inserts and then we determine the expected position and orientation of the cutting edge by applying some geometrical operations. We compute the deviations from the expected cutting edge to the real edge of the inserts to determine if an insert is broken. We evaluated the proposed method on a new dataset that we acquired and made public. The obtained result (a harmonic mean of precision and recall 91.43%) shows that the machine vision system that we present is effective and suitable for the identification of broken inserts in machining head tools and ready to be installed in an on-line system.
2.
Fernández-Robles, Laura; Azzopardi, George; Alegre, Enrique; Petkov, Nicolai; Castejón-Lima, Manuel
Identification of milling inserts in situ based on a versatile machine vision system Journal Article
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, machine vision, trainable filters, visual quality inspection
@article{fernandez2017identification,
title = {Identification of milling inserts in situ based on a versatile machine vision system},
author = {Laura Fern\'{a}ndez-Robles and George Azzopardi and Enrique Alegre and Nicolai Petkov and Manuel Castej\'{o}n-Lima},
doi = {https://doi.org/10.1016/j.jmsy.2017.08.002},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Journal of Manufacturing Systems},
volume = {45},
pages = {48-57},
publisher = {2017},
abstract = {This paper proposes a novel method for in situ localization of multiple inserts by means of machine vision techniques, a challenging issue in the field of tool wear monitoring. Most existing research works focus on evaluating the wear of isolated inserts after been manually extracted from the head tool. The method proposed solves this issue of paramount importance, as it frees the operator from continuously monitoring the machining process and allows the machine to continue operating without extracting the milling head for wear evaluation. We use trainable COSFIRE filters without requiring any manual intervention. This trainable approach is more versatile and generic than previous works on the topic, as it is not based on, and does not require, any domain knowledge. This allows an automatic application of the method to new machines without the need of specific knowledge on machine vision. We use an experimental dataset that we published to test the effectiveness of the method. We achieved very good performance with an F1 score of 0.9674, in the identification of multiple milling head inserts. The proposed approach can be considered as a general framework for the localization and identification of machining pieces from images taken from mechanical monitoring systems.},
keywords = {brain-inspired, machine vision, trainable filters, visual quality inspection},
pubstate = {published},
tppubtype = {article}
}
This paper proposes a novel method for in situ localization of multiple inserts by means of machine vision techniques, a challenging issue in the field of tool wear monitoring. Most existing research works focus on evaluating the wear of isolated inserts after been manually extracted from the head tool. The method proposed solves this issue of paramount importance, as it frees the operator from continuously monitoring the machining process and allows the machine to continue operating without extracting the milling head for wear evaluation. We use trainable COSFIRE filters without requiring any manual intervention. This trainable approach is more versatile and generic than previous works on the topic, as it is not based on, and does not require, any domain knowledge. This allows an automatic application of the method to new machines without the need of specific knowledge on machine vision. We use an experimental dataset that we published to test the effectiveness of the method. We achieved very good performance with an F1 score of 0.9674, in the identification of multiple milling head inserts. The proposed approach can be considered as a general framework for the localization and identification of machining pieces from images taken from mechanical monitoring systems.