Designing porthole aluminium extrusion dies on the basis of eXplainable Artificial Intelligence

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/132676
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dc.contributorDiseño en Ingeniería y Desarrollo Tecnológico (DIDET)es_ES
dc.contributorReconocimiento de Formas e Inteligencia Artificiales_ES
dc.contributor.authorLlorca-Schenk, Juan-
dc.contributor.authorRico-Juan, Juan Ramón-
dc.contributor.authorSanchez-Lozano, Miguel-
dc.contributor.otherUniversidad de Alicante. Departamento de Expresión Gráfica, Composición y Proyectoses_ES
dc.contributor.otherUniversidad de Alicante. Departamento de Lenguajes y Sistemas Informáticoses_ES
dc.date.accessioned2023-03-10T07:18:06Z-
dc.date.available2023-03-10T07:18:06Z-
dc.date.issued2023-03-09-
dc.identifier.citationExpert Systems with Applications. 2023, 222: 119808. https://doi.org/10.1016/j.eswa.2023.119808es_ES
dc.identifier.issn0957-4174 (Print)-
dc.identifier.issn1873-6793 (Online)-
dc.identifier.urihttp://hdl.handle.net/10045/132676-
dc.description.abstractThis paper shows the development of a tool with which to solve the most critical aspect of the porthole die design problem using a predictive model based on machine learning (ML). The model relies on a large amount of geometrical data regarding successful porthole die designs, information on which was obtained thanks to a collaboration with a leading extrusion company. In all cases, the dies were made of H-13 hot work steel and the billet material was 6063 aluminium alloy. The predictive model was chosen from a series of probes with different algorithms belonging to various ML families, which were applied to the analysis of geometrical data corresponding to 596 ports from 88 first trial dies. Algorithms based on the generation of multiple decision trees together with the boosting technique obtained the most promising results, the best by far being the CatBoost algorithm. The explainability of this model is based on a post-hoc approach using the SHAP (SHapley Additive exPlanations) tool. The results obtained with this ML-based model are notably better than those of a previous model based on linear regression as regards both the R2 metric and the results obtained with the application examples. An additional practical advantage is its explainability, which is a great help when deciding the best way in which to adjust an initial design to the predictive model. This ML-based model is, therefore, an optimal means to integrate the experience and know-how accumulated through many designs over time in order to apply it to new designs. It will also provide an aid in generating the starting point for the design of high-difficulty dies, in order to minimise the number of FEM (finite element method) simulation/correction iterations required until an optimal solution is achieved. It is not aimed to eliminate FEM simulation from the design tasks, but rather to help improve and accelerate the whole process of designing porthole dies. The work presented herein addresses a validation model for a very common porthole die typology: four cavity and four port per cavity dies for 6xxx series aluminium alloys. However, a wide range of research regarding the generalisation of this model or its extension to other porthole die typologies must still be carried out.es_ES
dc.description.sponsorshipThis work was partially supported by the DIDET Group (Diseño en Ingeniería y Desarrollo Tecnológico) at the University of Alicante (UA VIGROB-032).es_ES
dc.languageenges_ES
dc.publisherElsevieres_ES
dc.rights© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)es_ES
dc.subjectAluminium extrusiones_ES
dc.subjectMachine learninges_ES
dc.subjectDie designes_ES
dc.subjectExplainable machine learninges_ES
dc.subjectHollow profilees_ES
dc.subjectPortholees_ES
dc.titleDesigning porthole aluminium extrusion dies on the basis of eXplainable Artificial Intelligencees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.peerreviewedsies_ES
dc.identifier.doi10.1016/j.eswa.2023.119808-
dc.relation.publisherversionhttps://doi.org/10.1016/j.eswa.2023.119808es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
Appears in Collections:INV - DIDET - Artículos de Revistas
INV - GRFIA - Artículos de Revistas

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