Predicting Mechanical Properties of FDM‐Produced Parts Using Machine Learning Approaches
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Dosyalar
Tarih
2025-02-23
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Additive 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.
Açıklama
Anahtar Kelimeler
Mechanical Properties, Surfaces and Interfaces, Thermoplastics
Kaynak
Journal of Applied Polymer Science
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
Sayı
Künye
Özkül, M., Kuncan, F., & Ulkir, O. (2025). Predicting Mechanical Properties of FDM‐Produced Parts Using Machine Learning Approaches. Journal of Applied Polymer Science, e56899.
