Lightweight design of lattice-embedded brake pedals using artificial intelligence -based optimization

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dc.authorid0000-0002-1481-5572
dc.contributor.authorKurt, Enes
dc.contributor.authorYildiz, Ali Riza
dc.contributor.authorInkaya, Tulin
dc.contributor.authorOzcan, Ahmet Remzi
dc.contributor.authorGokdag, Istemihan
dc.date.accessioned2026-02-08T15:15:47Z
dc.date.available2026-02-08T15:15:47Z
dc.date.issued2026
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThe application of lattice structures has become increasingly important in designing complex components due to additive manufacturing (AM) advancements. Various types and design parameters of lattice structures allow weight reduction while maintaining the required strength and improving mechanical properties, with the strength varying based on these parameters. One common approach to calculating this strength is by using software solvers like SimSolid, which employs the meshless analysis solution (MAS). Considering the variety of parameters, the complexity of lattice structures, and the computational difficulties in analysis methods, identifying the optimal lattice structure for a design is highly challenging. To overcome this challenge, artificial neural networks (ANNs) are integrated into the optimization algorithm used in this study. The training data for the ANN are obtained from the analysis results of the designs generated using the design parameters selected by the Latin hypercube sampling (LHS) method. The ANNs integrated non-dominated sorting genetic algorithm II (NSGA-II) optimization algorithm is used to minimize the mass while ensuring the strength of the material by keeping the maximum stress within the permissible limits. The method is applied to the weight reduction of the brake pedal, approximately 26.96 % is achieved while maintaining the required strength under existing conditions.
dc.identifier.doi10.1515/mt-2025-0390
dc.identifier.endpage95
dc.identifier.issn0025-5300
dc.identifier.issn2195-8572
dc.identifier.issue1
dc.identifier.scopus2-s2.0-105022478428
dc.identifier.scopusqualityQ2
dc.identifier.startpage83
dc.identifier.urihttps://doi.org/10.1515/mt-2025-0390
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5967
dc.identifier.volume68
dc.identifier.wosWOS:001616629900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWalter De Gruyter Gmbh
dc.relation.ispartofMaterials Testing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectlattice structures
dc.subjectoptimization
dc.subjectartificial neural networks
dc.subjectmeshless analysis
dc.titleLightweight design of lattice-embedded brake pedals using artificial intelligence -based optimization
dc.typeArticle

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