{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T19:59:50Z","timestamp":1777751990539,"version":"3.51.4"},"reference-count":58,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T00:00:00Z","timestamp":1665532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Metals"],"abstract":"<jats:p>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\u2019 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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/met12101709","type":"journal-article","created":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T22:45:29Z","timestamp":1665614729000},"page":"1709","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Using an Artificial Neural Network Approach to Predict Machining Time"],"prefix":"10.3390","volume":"12","author":[{"given":"Andr\u00e9","family":"Rodrigues","sequence":"first","affiliation":[{"name":"ISEP\u2014School of Engineering, Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida 431, 4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8570-4362","authenticated-orcid":false,"given":"Francisco J. G.","family":"Silva","sequence":"additional","affiliation":[{"name":"ISEP\u2014School of Engineering, Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida 431, 4200-072 Porto, Portugal"},{"name":"INEGI\u2014Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal"}]},{"given":"Vitor F. C.","family":"Sousa","sequence":"additional","affiliation":[{"name":"ISEP\u2014School of Engineering, Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida 431, 4200-072 Porto, Portugal"},{"name":"INEGI\u2014Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0074-7799","authenticated-orcid":false,"given":"Arnaldo G.","family":"Pinto","sequence":"additional","affiliation":[{"name":"ISEP\u2014School of Engineering, Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida 431, 4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4225-6525","authenticated-orcid":false,"given":"Lu\u00eds P.","family":"Ferreira","sequence":"additional","affiliation":[{"name":"ISEP\u2014School of Engineering, Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida 431, 4200-072 Porto, Portugal"},{"name":"INEGI\u2014Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4556-9578","authenticated-orcid":false,"given":"Teresa","family":"Pereira","sequence":"additional","affiliation":[{"name":"ISEP\u2014School of Engineering, Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida 431, 4200-072 Porto, Portugal"},{"name":"INEGI\u2014Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1016\/j.jclepro.2017.07.144","article-title":"Estimating energy consumption of injection moulding for environmental-driven mould design","volume":"168","author":"Matarrese","year":"2017","journal-title":"J. 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