{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T23:48:16Z","timestamp":1777938496749,"version":"3.51.4"},"reference-count":25,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2021,2,16]],"date-time":"2021-02-16T00:00:00Z","timestamp":1613433600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications"],"published-print":{"date-parts":[[2021,6]]},"abstract":"<jats:p>The last decades have been driven by significant progress in the computational capacity, which have been supporting the development of increasingly realistic and detailed simulations. However, despite these improvements, several problems still do not have an effective solution, due to their numerical complexity. As a result, the answer to these problems can be improved by using techniques that enable the description of phenomena with less resolution, but with lower computational costs, which is the case of the reduced order models. The main objective of this article is the presentation of a new approach for reduced order model development and application in the design and optimization of structural parts. The selected method is the artificial neural networks. Artificial neural networks allow the prediction of certain variables based on a given dataset. Two typical case studies are addressed: the first is a fixed plate subjected to uniformly distributed pressure and the second is a reinforced panel also subjected to internal pressure, with regular reinforcements to improve the specific strength. With this method, a substantial reduction in the simulation time is observed, being, approximately, 40 times faster than the solution obtained with Ansys. The developed neural network has a relative average difference of about 20 %, which is considered satisfactory given the complexity of the problem and considering it is a first application of these networks in this domain. In conclusion, this research made it possible to highlight the potential of reduced order model: including the shorter response time, the less computational resources, and the simplification of problems in detriment of less resolution in the description of structural behaviour. Given these advantages, it is expected that these models will play a key role in future applications, as in digital twins.<\/jats:p>","DOI":"10.1177\/1464420721992445","type":"journal-article","created":{"date-parts":[[2021,2,16]],"date-time":"2021-02-16T07:02:36Z","timestamp":1613458956000},"page":"1271-1286","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Stress\u2013strain evaluation of structural parts using artificial neural networks"],"prefix":"10.1177","volume":"235","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7557-7002","authenticated-orcid":false,"given":"Jo\u00e3o PA","family":"Ribeiro","sequence":"first","affiliation":[{"name":"Faculty of Engineering, University of Porto, Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0054-0771","authenticated-orcid":false,"given":"S\u00e9rgio MO","family":"Tavares","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Porto, Porto, Portugal"}]},{"given":"Marco","family":"Parente","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Porto, Porto, Portugal"}]}],"member":"179","published-online":{"date-parts":[[2021,2,16]]},"reference":[{"key":"bibr1-1464420721992445","doi-asserted-by":"crossref","unstructured":"Hartmann D, Herz M, Wever U. Model order reduction a key technology for digital twins. In: Keiper W, Milde A and Volkwein S (eds)\n                      Reduced-order modeling (ROM) for simulation and optimization: powerful algorithms as key enablers for scientific computing.\n                      Berlin: Springer International Publishing, 2018, pp. 167\u2013179.","DOI":"10.1007\/978-3-319-75319-5_8"},{"key":"bibr2-1464420721992445","unstructured":"Panetta K. Gartner Top 10 Strategic Technology Trends for 2020, 2019, www.gartner.com\/smarterwithgartner\/gartner-top-10-strategic-technology-trends-for-2020\/ (accessed 26 January 2021)."},{"key":"bibr3-1464420721992445","unstructured":"Hurley B. A faster way to design rockets: scientific machine learning, www.techbriefs.com\/component\/content\/article\/tb\/stories\/blog\/36755 2020, (accessed 26 January 2021)."},{"key":"bibr4-1464420721992445","unstructured":"Kulp B.\n                      What is a reduced order model and what\u2019s its product development role?\n                      Canonsburg, PA: Ansys Inc. 2019."},{"key":"bibr5-1464420721992445","author":"Ramirez A.","journal-title":"16th international conference on harmonics and quality of power"},{"key":"bibr6-1464420721992445","doi-asserted-by":"crossref","unstructured":"Schilders WH, van der Vorst HA, Rommes J.\n                      Model order reduction: theory, research aspects and applications. The European Consortium for Mathematics in Industry\n                      . Berlin Heidelberg: Springer-Verlag, 2008.","DOI":"10.1007\/978-3-540-78841-6"},{"key":"bibr7-1464420721992445","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-75714-8"},{"key":"bibr8-1464420721992445","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-7012(00)00201-3"},{"key":"bibr9-1464420721992445","doi-asserted-by":"crossref","unstructured":"Mijwil M. Artificial neural networks advantages and disadvantages, www.linkedin.com\/pulse\/artificial-neural-networks-advantages-disadvantages-maad-m-mijwel\/ (2018, accessed 26 January 2021).","DOI":"10.58496\/MJBD\/2021\/006"},{"key":"bibr10-1464420721992445","doi-asserted-by":"publisher","DOI":"10.1016\/S0895-4356(96)00002-9"},{"key":"bibr11-1464420721992445","doi-asserted-by":"crossref","unstructured":"Chen C, Li K, Duan M, et\u00a0al. Chapter 6 \u2013 extreme learning machine and its applications in big data processing. In: Hsu HH, Chang CY and Hsu CH (eds)\n                      Big data analytics for sensor-network collected intelligence.\n                      [Intelligent Data-Centric Systems]. Cambridge, MA: Academic Press, 2017, pp. 117\u2013150.","DOI":"10.1016\/B978-0-12-809393-1.00006-4"},{"key":"bibr12-1464420721992445","first-page":"310","volume":"4","author":"Sharma S","year":"2020","journal-title":"Int J Eng Appl Sci Technol"},{"key":"bibr13-1464420721992445","first-page":"111","volume":"1","author":"Olgac A","year":"2011","journal-title":"Int J Artif Intell Exp Syst"},{"key":"bibr14-1464420721992445","author":"Ding B","year":"2018","journal-title":"Chinese control and decision conference (CCDC)"},{"key":"bibr15-1464420721992445","first-page":"1344","volume":"47","author":"Ittiyavirah S","year":"2013","journal-title":"J Theor Appl Inform Technol"},{"key":"bibr16-1464420721992445","doi-asserted-by":"publisher","DOI":"10.1109\/2.485891"},{"key":"bibr17-1464420721992445","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3167-5"},{"key":"bibr18-1464420721992445","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-43162-8"},{"key":"bibr19-1464420721992445","author":"Adel A","year":"2016","journal-title":"IEEE international conference on electronics, circuits and systems (ICECS)"},{"key":"bibr20-1464420721992445","volume-title":"Deep learning with python","author":"Chollet F.","year":"2017"},{"key":"bibr21-1464420721992445","doi-asserted-by":"crossref","unstructured":"Li J, Cheng Jh, Shi Jy, et\u00a0al. Brief introduction of back propagation (BP) neural network algorithm and its improvement. In: Jin D and Lin S (eds)\n                      Advances in computer science and information engineering.\n                      Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 553\u2013558.","DOI":"10.1007\/978-3-642-30223-7_87"},{"key":"bibr22-1464420721992445","doi-asserted-by":"publisher","DOI":"10.1201\/9781315104621"},{"key":"bibr23-1464420721992445","volume-title":"Theory of plates and shells","author":"Timoshenko S","year":"1959"},{"key":"bibr24-1464420721992445","unstructured":"Seixas L.\n                      Modelos de elementos finitos para c\u00e1lculo estrutural em c\u00f3digo\n                      aberto.\n                      Master\u2019s Thesis, Faculdade de Engenharia da Universidade do Porto, Porto, 2019."},{"key":"bibr25-1464420721992445","volume-title":"PLANE183","author":"Ansys.","year":"2019"}],"container-title":["Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1464420721992445","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/1464420721992445","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1464420721992445","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:41:03Z","timestamp":1777696863000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/1464420721992445"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,16]]},"references-count":25,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["10.1177\/1464420721992445"],"URL":"https:\/\/doi.org\/10.1177\/1464420721992445","relation":{},"ISSN":["1464-4207","2041-3076"],"issn-type":[{"value":"1464-4207","type":"print"},{"value":"2041-3076","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,16]]}}}