Predicting Mechanical Properties of FDM‐Produced Parts Using Machine Learning Approaches

dc.contributor.authorMahmut Özkül
dc.contributor.authorFatma Kuncan
dc.contributor.authorOsman Ulkir
dc.date.accessioned2025-03-11T12:50:03Z
dc.date.available2025-03-11T12:50:03Z
dc.date.issued2025-02-23
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractAdditive manufacturing (AM), especially fused deposition modeling (FDM), has been widely used in industrial production processes in recent years. The mechanical properties of parts produced by FDM can be predicted through the correct selection of printing parameters. In this study, 25 machine learning (ML) algorithms were used to predict the mechanical properties (hardness, tensile strength, flexural strength, and surface roughness) of acrylonitrile butadiene styrene (ABS) samples fabricated by FDM. Experiments were conducted using three different layer thicknesses (100, 150, 200 μm), infill densities (50%, 75%, 100%), and nozzle temperatures (220°C, 230°C, 240°C). The effects of printing parameters on mechanical properties were investigated through analysis of variance (ANOVA). This analysis results indicated that infill density had the most significant effect on hardness (55.56%), tensile strength (80.02%), and flexural strength (77.13%). In addition, the layer thickness was identified as the most influential parameter on the surface roughness, with an effect of 70.89%. The prediction performance of the ML algorithms was evaluated based on the mean absolute error (MAE), root mean squared error, mean squared error, and R-squared (R2) values. The KSTAR algorithm best predicted both hardness and surface roughness, with MAE values of 0.006 and 0.009, respectively, and an R2 value of up to 0.99. For the prediction of tensile and flexural strength, the MLP algorithm was determined to be the most successful method, achieving high accuracy (R2 > 0.99) for both properties. In addition, comparison graphs between the predicted and actual results showed high overall accuracy, with a particularly strong agreement for hardness, tensile strength, and surface roughness. The study identified the algorithms with the best prediction performance and provided recommendations for predicting the 3D printing process based on these findings.
dc.identifier.citationÖzkül, M., Kuncan, F., & Ulkir, O. (2025). Predicting Mechanical Properties of FDM‐Produced Parts Using Machine Learning Approaches. Journal of Applied Polymer Science, e56899.
dc.identifier.doi10.1002/app.56899
dc.identifier.issn0021-8995
dc.identifier.issn1097-4628
dc.identifier.scopus2-s2.0-85218679201
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1002/app.56899
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8556
dc.identifier.wos001432193700001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorKuncan, Fatma
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofJournal of Applied Polymer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMechanical Properties
dc.subjectSurfaces and Interfaces
dc.subjectThermoplastics
dc.titlePredicting Mechanical Properties of FDM‐Produced Parts Using Machine Learning Approaches
dc.typejournal-article

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