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On-Line Estimation of Laser-Drilled Hole Depth Using a Machine Vision Method

The paper presents a novel method for monitoring and estimating the depth of a laser-drilled hole using machine vision. Through on-line image acquisition and analysis in laser machining processes, we could simultaneously obtain correlations between the machining processes and analyzed images. Based...

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Detalles Bibliográficos
Autores principales: Ho, Chao-Ching, He, Jun-Jia, Liao, Te-Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3472821/
https://www.ncbi.nlm.nih.gov/pubmed/23112593
http://dx.doi.org/10.3390/s120810148
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author Ho, Chao-Ching
He, Jun-Jia
Liao, Te-Ying
author_facet Ho, Chao-Ching
He, Jun-Jia
Liao, Te-Ying
author_sort Ho, Chao-Ching
collection PubMed
description The paper presents a novel method for monitoring and estimating the depth of a laser-drilled hole using machine vision. Through on-line image acquisition and analysis in laser machining processes, we could simultaneously obtain correlations between the machining processes and analyzed images. Based on the machine vision method, the depths of laser-machined holes could be estimated in real time. Therefore, a low cost on-line inspection system is developed to increase productivity. All of the processing work was performed in air under standard atmospheric conditions and gas assist was used. A correlation between the cumulative size of the laser-induced plasma region and the depth of the hole is presented. The result indicates that the estimated depths of the laser-drilled holes were a linear function of the cumulative plasma size, with a high degree of confidence. This research provides a novel machine vision-based method for estimating the depths of laser-drilled holes in real time.
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spelling pubmed-34728212012-10-30 On-Line Estimation of Laser-Drilled Hole Depth Using a Machine Vision Method Ho, Chao-Ching He, Jun-Jia Liao, Te-Ying Sensors (Basel) Article The paper presents a novel method for monitoring and estimating the depth of a laser-drilled hole using machine vision. Through on-line image acquisition and analysis in laser machining processes, we could simultaneously obtain correlations between the machining processes and analyzed images. Based on the machine vision method, the depths of laser-machined holes could be estimated in real time. Therefore, a low cost on-line inspection system is developed to increase productivity. All of the processing work was performed in air under standard atmospheric conditions and gas assist was used. A correlation between the cumulative size of the laser-induced plasma region and the depth of the hole is presented. The result indicates that the estimated depths of the laser-drilled holes were a linear function of the cumulative plasma size, with a high degree of confidence. This research provides a novel machine vision-based method for estimating the depths of laser-drilled holes in real time. Molecular Diversity Preservation International (MDPI) 2012-07-27 /pmc/articles/PMC3472821/ /pubmed/23112593 http://dx.doi.org/10.3390/s120810148 Text en © 2012 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Ho, Chao-Ching
He, Jun-Jia
Liao, Te-Ying
On-Line Estimation of Laser-Drilled Hole Depth Using a Machine Vision Method
title On-Line Estimation of Laser-Drilled Hole Depth Using a Machine Vision Method
title_full On-Line Estimation of Laser-Drilled Hole Depth Using a Machine Vision Method
title_fullStr On-Line Estimation of Laser-Drilled Hole Depth Using a Machine Vision Method
title_full_unstemmed On-Line Estimation of Laser-Drilled Hole Depth Using a Machine Vision Method
title_short On-Line Estimation of Laser-Drilled Hole Depth Using a Machine Vision Method
title_sort on-line estimation of laser-drilled hole depth using a machine vision method
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3472821/
https://www.ncbi.nlm.nih.gov/pubmed/23112593
http://dx.doi.org/10.3390/s120810148
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