Optimizing parameters for additive manufacturing: a study on the vibrational performance of 3D printed cantilever beams using material extrusion

Basit Öğe Kaydı
dc.contributor.authorEkerer, Sabri Can
dc.contributor.authorBoga, Cem
dc.contributor.authorSeyedzavvar, Mirsadegh
dc.contributor.authorKoroglu, Tahsin
dc.contributor.authorFarsadi, Touraj
dc.date.accessioned2025-01-06T17:36:48Z
dc.date.available2025-01-06T17:36:48Z
dc.date.issued2024
dc.description.abstractPurposeThis study aims to investigate the impact of different printing parameters on the free vibration characteristics of 3D printed cantilever beams. Through a comprehensive analysis of material extrusion (ME) variables such as extrusion rate, printing pattern and layer thickness, the study seeks to enhance the understanding of how these parameters influence the vibrational properties, particularly the natural frequency, of printed components.Design/methodology/approachThe experimental design involves conducting a series of experiments using a central composite design approach to gather data on the vibrational response of ABS cantilever beams under diverse ME parameters. These parameters are systematically varied across different levels, facilitating a thorough exploration of their effects on the vibrational behavior of the printed specimens. The collected data are then used to develop a predictive model leveraging a hybrid artificial neural network (ANN)/ particle swarm optimization (PSO) approach, which combines the strengths of ANN in modeling complex relationships and PSO in optimizing model parameters.FindingsThe developed ANN/PSO hybrid model demonstrates high accuracy in predicting the natural frequency of 3D printed cantilever beams, with a correlation ratio (R) of 0.9846 when tested against experimental data. Through iterative fine-tuning with PSO, the model achieves a low mean square error (MSE) of 1.1353e-5, underscoring its precision in estimating the vibrational characteristics of printed specimens. Furthermore, the model's transformation into a regression model enables the derivation of surface response characteristics governing the vibration properties of 3D printed objects in response to input parameters, facilitating the identification of optimal parameter configurations for maximizing vibration characteristics in 3D printed products.Originality/valueThis study introduces a novel predictive model that combines ANNs with PSO to analyze the vibrational behavior of 3D printed ABS cantilever beams produced under various ME parameters. By integrating these advanced methodologies, the research offers a pioneering approach to precisely estimating the natural frequency of 3D printed objects, contributing to the advancement of predictive modeling in additive manufacturing.
dc.identifier.doi10.1108/RPJ-03-2024-0146
dc.identifier.issn1355-2546
dc.identifier.issn1758-7670
dc.identifier.scopus2-s2.0-85205732999
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1108/RPJ-03-2024-0146
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2007
dc.identifier.wosWOS:001322372200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherEmerald Group Publishing Ltd
dc.relation.ispartofRapid Prototyping Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectCantilever beam
dc.subject3D printing
dc.subjectNatural frequency
dc.subjectANN/PSO model
dc.subjectResponse surface
dc.titleOptimizing parameters for additive manufacturing: a study on the vibrational performance of 3D printed cantilever beams using material extrusion
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu