Modeling pulsed laser micromachining of micro geometries using machine-learning techniques

A wide range of opportunities are emerging in the micro-system technology sector for laser micro-machining systems, because they are capable of processing various types of materials with micro-scale precision. However, few process datasets and machine-learning techniques are optimized for this indus...

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Detalles Bibliográficos
Autores: Teixidor Ezpeleta, Daniel, Grzenda, Maciej, Bustillo Iglesias, Andrés, Ciurana, Quim de
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2015
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/9320
Acceso en línea:http://hdl.handle.net/10256/9320
Access Level:acceso abierto
Palabra clave:Machine learning
Aprenentatge automàtic
Làsers -- Aplicacions industrials
Lasers -- Industrial applications
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spelling Modeling pulsed laser micromachining of micro geometries using machine-learning techniquesTeixidor Ezpeleta, DanielGrzenda, MaciejBustillo Iglesias, AndrésCiurana, Quim deMachine learningAprenentatge automàticLàsers -- Aplicacions industrialsLasers -- Industrial applicationsA wide range of opportunities are emerging in the micro-system technology sector for laser micro-machining systems, because they are capable of processing various types of materials with micro-scale precision. However, few process datasets and machine-learning techniques are optimized for this industrial task. This study describes the process parameters of micro-laser milling and their influence on the final features of the microshapes that are produced. It also identifies the most accurate machine-learning technique for the modelization of this multivariable process. It examines the capabilities of laser micro-machining by performing experiments on hardened steel with a pulsed Nd:YAG laser. Arrays of micro-channels were manufactured using various scanning speeds, pulse intensities and pulse frequencies. The results are presented in terms of the main industrial requirements for any manufactured good: dimensional accuracy (in our case, depth and width of the channels), surface roughness and material removal rate (which is a measure of the productivity of the process). Different machine-learning techniques were then tested on the datasets to build highly accurate models for each output variable. The selected techniques were: k-Nearest Neighbours, neural networks, decision trees and linear regression models. Our analysis of the correlation coefficients and the mean absolute error of all the generated models show that neural networks are better at modelling channel depth and that decision trees are better at modelling material removal rate; both techniques were similar for width and surface roughness. In general, these two techniques show better accuracy than the other two models. The work concludes that decision trees should be used, if information on input parameter relations is sought, while neural networks are suitable when the dimensional accuracy of the workpiece is the main industrial requirement. Extensive datasets are necessary for this industrial task, to provide reliable AI models due to the high rates of noise, especially for some outputs such as roughnessSpringer Verlag2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttp://hdl.handle.net/10256/9320http://hdl.handle.net/10256/9320© Journal of Intelligent Manufacturing, 2015, vol. 26, núm. 4, p. 801-814Articles publicats (D-EMCI)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1007/s10845-013-0835-xinfo:eu-repo/semantics/altIdentifier/issn/0956-5515info:eu-repo/semantics/altIdentifier/eissn/1572-8145info:eu-repo/grantAgreement/EC/FP7/247476Tots els drets reservatsinfo:eu-repo/semantics/openAccessoai:recercat.cat:10256/93202026-05-29T05:05:01Z
dc.title.none.fl_str_mv Modeling pulsed laser micromachining of micro geometries using machine-learning techniques
title Modeling pulsed laser micromachining of micro geometries using machine-learning techniques
spellingShingle Modeling pulsed laser micromachining of micro geometries using machine-learning techniques
Teixidor Ezpeleta, Daniel
Machine learning
Aprenentatge automàtic
Làsers -- Aplicacions industrials
Lasers -- Industrial applications
title_short Modeling pulsed laser micromachining of micro geometries using machine-learning techniques
title_full Modeling pulsed laser micromachining of micro geometries using machine-learning techniques
title_fullStr Modeling pulsed laser micromachining of micro geometries using machine-learning techniques
title_full_unstemmed Modeling pulsed laser micromachining of micro geometries using machine-learning techniques
title_sort Modeling pulsed laser micromachining of micro geometries using machine-learning techniques
dc.creator.none.fl_str_mv Teixidor Ezpeleta, Daniel
Grzenda, Maciej
Bustillo Iglesias, Andrés
Ciurana, Quim de
author Teixidor Ezpeleta, Daniel
author_facet Teixidor Ezpeleta, Daniel
Grzenda, Maciej
Bustillo Iglesias, Andrés
Ciurana, Quim de
author_role author
author2 Grzenda, Maciej
Bustillo Iglesias, Andrés
Ciurana, Quim de
author2_role author
author
author
dc.subject.none.fl_str_mv Machine learning
Aprenentatge automàtic
Làsers -- Aplicacions industrials
Lasers -- Industrial applications
topic Machine learning
Aprenentatge automàtic
Làsers -- Aplicacions industrials
Lasers -- Industrial applications
description A wide range of opportunities are emerging in the micro-system technology sector for laser micro-machining systems, because they are capable of processing various types of materials with micro-scale precision. However, few process datasets and machine-learning techniques are optimized for this industrial task. This study describes the process parameters of micro-laser milling and their influence on the final features of the microshapes that are produced. It also identifies the most accurate machine-learning technique for the modelization of this multivariable process. It examines the capabilities of laser micro-machining by performing experiments on hardened steel with a pulsed Nd:YAG laser. Arrays of micro-channels were manufactured using various scanning speeds, pulse intensities and pulse frequencies. The results are presented in terms of the main industrial requirements for any manufactured good: dimensional accuracy (in our case, depth and width of the channels), surface roughness and material removal rate (which is a measure of the productivity of the process). Different machine-learning techniques were then tested on the datasets to build highly accurate models for each output variable. The selected techniques were: k-Nearest Neighbours, neural networks, decision trees and linear regression models. Our analysis of the correlation coefficients and the mean absolute error of all the generated models show that neural networks are better at modelling channel depth and that decision trees are better at modelling material removal rate; both techniques were similar for width and surface roughness. In general, these two techniques show better accuracy than the other two models. The work concludes that decision trees should be used, if information on input parameter relations is sought, while neural networks are suitable when the dimensional accuracy of the workpiece is the main industrial requirement. Extensive datasets are necessary for this industrial task, to provide reliable AI models due to the high rates of noise, especially for some outputs such as roughness
publishDate 2015
dc.date.none.fl_str_mv 2015
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/9320
http://hdl.handle.net/10256/9320
url http://hdl.handle.net/10256/9320
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/s10845-013-0835-x
info:eu-repo/semantics/altIdentifier/issn/0956-5515
info:eu-repo/semantics/altIdentifier/eissn/1572-8145
info:eu-repo/grantAgreement/EC/FP7/247476
dc.rights.none.fl_str_mv Tots els drets reservats
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Tots els drets reservats
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
dc.source.none.fl_str_mv © Journal of Intelligent Manufacturing, 2015, vol. 26, núm. 4, p. 801-814
Articles publicats (D-EMCI)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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