Publicação
Machine Learning Methods for Quality Prediction in Thermoplastics Injection Molding
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | |
| datacite.subject.sdg | 07:Energias Renováveis e Acessíveis | |
| datacite.subject.sdg | 09:Indústria, Inovação e Infraestruturas | |
| datacite.subject.sdg | 11:Cidades e Comunidades Sustentáveis | |
| dc.contributor.author | Silva, Bruno | |
| dc.contributor.author | Sousa, João | |
| dc.contributor.author | Alenya, Guillem | |
| dc.date.accessioned | 2026-04-20T15:04:05Z | |
| dc.date.available | 2026-04-20T15:04:05Z | |
| dc.date.issued | 2021-12 | |
| dc.description | EISBN - 978-1-6654-4231-2 | |
| dc.description | Date of Conference: 09-10 December 2021 | |
| dc.description | Link de acesso ao documento: https://upcommons.upc.edu/server/api/core/bitstreams/bb8cfda3-3d76-429c-8382-02d542f820e2/content | |
| dc.description.abstract | Nowadays, competitiveness is a reality in all industrial fields and the plastic injection industry is not an exception. Due to the complex intrinsic changes that the parameters undergo during the injection process, it is essential to monitor the parameters that influence the quality of the final part to guarantee a superior quality of service provided to customers. Quality requirements impose the development of intelligent systems capable to detect defects in the produced parts. This article presents a first step towards building an intelligent system for classifying the quality of produced parts. The basic approach of this work is machine learning methods (Artificial Neural Networks and Support Vector Machines) and techniques that combine the two previous approaches (ensemble method). These are trained as classifiers to detect conformity or even defect types in parts. The data analyzed were collected at a plastic injection company in Portugal. The results show that these techniques are capable of incorporating the non-linear relationships between the process variables, which allows for a good accuracy (≈99%) in the identification of defects. Although these techniques present good accuracy, we show that taking into account the history of the last cycles and the use of combined techniques improves even further the performance. The approach presented in this article has a number of potential advantages for online predicting of parts quality in injection molding processes. | eng |
| dc.description.sponsorship | This work was partially supported by the Spanish State Research Agency through the project CHLOE-GRAPH (PID2020-118649RB-l00) and by FCT—Portuguese Foundation for Science and Technology under project grant UIDB/00308/2020. | |
| dc.identifier.citation | B. Silva, J. Sousa and G. Alenya, "Machine Learning Methods for Quality Prediction in Thermoplastics Injection Molding," 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 2021, pp. 1-6, doi: https://doi.org/10.1109/ICECET52533.2021.9698455. | |
| dc.identifier.doi | 10.1109/icecet52533.2021.9698455 | |
| dc.identifier.isbn | 978-1-6654-4232-9 | |
| dc.identifier.isbn | 978-1-6654-4231-2 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/16156 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | IEEE Canada | |
| dc.relation | Institute for Systems Engineering and Computers at Coimbra - INESC Coimbra | |
| dc.relation.hasversion | https://ieeexplore.ieee.org/document/9698455 | |
| dc.relation.ispartof | 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET) | |
| dc.rights.uri | N/A | |
| dc.subject | Artificial Neural Network | |
| dc.subject | Support Vector Machines | |
| dc.subject | Injection Molding | |
| dc.subject | Machine Learning | |
| dc.title | Machine Learning Methods for Quality Prediction in Thermoplastics Injection Molding | eng |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.awardNumber | UIDB/00308/2020 | |
| oaire.awardTitle | Institute for Systems Engineering and Computers at Coimbra - INESC Coimbra | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00308%2F2020/PT | |
| oaire.citation.conferenceDate | 2021-12 | |
| oaire.citation.conferencePlace | Cape Town, South Africa | |
| oaire.citation.title | International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021 | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Miguel Lopes e Silva | |
| person.familyName | Sousa | |
| person.givenName | Bruno | |
| person.givenName | João | |
| person.identifier.ciencia-id | 121F-8DCE-FEE4 | |
| person.identifier.ciencia-id | C816-809B-4484 | |
| person.identifier.gsid | 55971568600 | |
| person.identifier.orcid | 0000-0001-5139-1994 | |
| person.identifier.orcid | 0000-0002-7567-4910 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| relation.isAuthorOfPublication | 98d8d9cc-bde7-4886-81c6-9e23ba0775dd | |
| relation.isAuthorOfPublication | 7678f744-5e50-4458-8811-33e1fbc63013 | |
| relation.isAuthorOfPublication.latestForDiscovery | 98d8d9cc-bde7-4886-81c6-9e23ba0775dd | |
| relation.isProjectOfPublication | 254d9223-2e3b-4754-bae9-c98986d80921 | |
| relation.isProjectOfPublication.latestForDiscovery | 254d9223-2e3b-4754-bae9-c98986d80921 |
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- Nowadays, competitiveness is a reality in all industrial fields and the plastic injection industry is not an exception. Due to the complex intrinsic changes that the parameters undergo during the injection process, it is essential to monitor the parameters that influence the quality of the final part to guarantee a superior quality of service provided to customers. Quality requirements impose the development of intelligent systems capable to detect defects in the produced parts. This article presents a first step towards building an intelligent system for classifying the quality of produced parts. The basic approach of this work is machine learning methods (Artificial Neural Networks and Support Vector Machines) and techniques that combine the two previous approaches (ensemble method). These are trained as classifiers to detect conformity or even defect types in parts. The data analyzed were collected at a plastic injection company in Portugal. The results show that these techniques are capable of incorporating the non-linear relationships between the process variables, which allows for a good accuracy (≈99%) in the identification of defects. Although these techniques present good accuracy, we show that taking into account the history of the last cycles and the use of combined techniques improves even further the performance. The approach presented in this article has a number of potential advantages for online predicting of parts quality in injection molding processes.
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