Publication
Using an Artificial Neural Network Approach to Predict Machining Time
| dc.contributor.author | Rodrigues, André | |
| dc.contributor.author | Silva, Francisco | |
| dc.contributor.author | Sousa, Vitor F. C. | |
| dc.contributor.author | Pinto, Arnaldo | |
| dc.contributor.author | Pinto Ferreira, Luís | |
| dc.contributor.author | Pereira, Maria Teresa Ribeiro | |
| dc.date.accessioned | 2023-01-18T16:44:42Z | |
| dc.date.available | 2023-01-18T16:44:42Z | |
| dc.date.issued | 2022-10-12 | |
| dc.description.abstract | One of the most critical factors in producing plastic injection molds is the cost estimation of machining services, which significantly affects the final mold price. These services’ costs are determined according to the machining time, which is usually a long and expensive operation. If it is considered that the injection mold parts are all different, it can be understood that the correct and quick estimation of machining times is of great importance for a company’s success. This article presents a proposal to apply artificial neural networks in machining time estimation for standard injection mold parts. For this purpose, a large set of parts was considered to shape the artificial intelligence model, and machining times were calculated to collect enough data for training the neural networks. The influences of the network architecture, input data, and the variables used in the network’s training were studied to find the neural network with greatest prediction accuracy. The application of neural networks in this work proved to be a quick and efficient way to predict cutting times with a percent error of 2.52% in the best case. The present work can strongly contribute to the research in this and similar sectors, as recent research does not usually focus on the direct prediction of machining times relating to overall production cost. This tool can be used in a quick and efficient manner to obtain information on the total machining cost of mold parts, with the possibility of being applied to other industry sectors | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.3390/met12101709 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/21659 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | MDPI | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Machining | pt_PT |
| dc.subject | Machining times | pt_PT |
| dc.subject | Machining time prediction | pt_PT |
| dc.subject | Cost estimation | pt_PT |
| dc.subject | Artificial intelligence | pt_PT |
| dc.subject | Artificial neural networks | pt_PT |
| dc.title | Using an Artificial Neural Network Approach to Predict Machining Time | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 10 | pt_PT |
| oaire.citation.startPage | 1709 | pt_PT |
| oaire.citation.title | Metals | pt_PT |
| oaire.citation.volume | 12 | pt_PT |
| person.familyName | Silva | |
| person.familyName | Sousa | |
| person.familyName | Pinto | |
| person.familyName | Pinto Ferreira | |
| person.familyName | Ribeiro Pereira | |
| person.givenName | Francisco | |
| person.givenName | Vitor | |
| person.givenName | Arnaldo | |
| person.givenName | Luís | |
| person.givenName | Maria Teresa | |
| person.identifier | 1422904 | |
| person.identifier | 2245862 | |
| person.identifier | U-2265-2018 | |
| person.identifier.ciencia-id | B81C-4758-2D59 | |
| person.identifier.ciencia-id | 6917-4643-1AA4 | |
| person.identifier.ciencia-id | 1317-0CD1-8505 | |
| person.identifier.ciencia-id | 5415-323A-131B | |
| person.identifier.ciencia-id | 7A16-77DE-410A | |
| person.identifier.orcid | 0000-0001-8570-4362 | |
| person.identifier.orcid | 0000-0001-8230-7310 | |
| person.identifier.orcid | 0000-0003-0074-7799 | |
| person.identifier.orcid | 0000-0003-4225-6525 | |
| person.identifier.orcid | 0000-0003-4556-9578 | |
| person.identifier.rid | I-5708-2015 | |
| person.identifier.rid | AEO-3561-2022 | |
| person.identifier.scopus-author-id | 56870827300 | |
| person.identifier.scopus-author-id | 57214461815 | |
| person.identifier.scopus-author-id | 24341119600 | |
| person.identifier.scopus-author-id | 57188755263 | |
| person.identifier.scopus-author-id | 56624034400 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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