Artificial neural network-based parameter identification of a beam-3D tip attachment system

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dc.authorid0000-0003-3070-6365
dc.contributor.authorGokdag, Hakan
dc.contributor.authorKati, Hilal Doganay
dc.date.accessioned2026-02-08T15:15:35Z
dc.date.available2026-02-08T15:15:35Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn this study, an artificial neural network (ANN)-based approach is proposed for estimating the tip mass attachment and structural damping parameters in a beam-3D mass system undergoing combined bending and torsional vibrations. The study begins with a detailed explanation of the calculation of the frequency response functions (FRFs) for this specific system. Subsequently, a difference vector is defined, based on the discrepancy between experimental and numerical FRF curves. This vector is dependent on ten parameters: the mass of the tip attachment, its mass moments of inertia, and the coordinates of the mass center, and the structural damping ratios of the beam. An orthogonal design method is then employed to create a design space for these parameters, and the elements of the difference vector are calculated for each point within this space. The points in the design space and the computed difference vectors are utilized as input and output data for training the ANN. The optimization process conducted with the obtained ANN model allows for realistic estimation of the tip mass parameters and damping values. The analysis reveals that as the design space widens, the parameter estimation process becomes increasingly challenging. This, in turn, necessitates a larger number of points in the design space and more neurons in the hidden layers of the trained network. In cases where the design space is small to medium in size, the parameter estimation errors are observed to be <5%. However, for wider design spaces, the estimation errors tend to increase.
dc.identifier.doi10.1080/15397734.2025.2507098
dc.identifier.endpage8218
dc.identifier.issn1539-7734
dc.identifier.issn1539-7742
dc.identifier.issue12
dc.identifier.scopus2-s2.0-105005584136
dc.identifier.scopusqualityQ1
dc.identifier.startpage8197
dc.identifier.urihttps://doi.org/10.1080/15397734.2025.2507098
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5865
dc.identifier.volume53
dc.identifier.wosWOS:001490801500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Inc
dc.relation.ispartofMechanics Based Design of Structures and Machines
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectArtificial neural network
dc.subjectfrequency response function
dc.subjectbeam vibration
dc.subjectparameter identification
dc.subjectoptimization
dc.titleArtificial neural network-based parameter identification of a beam-3D tip attachment system
dc.typeArticle

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