Mechanical performance and ANNbased prediction of Co-Cr dental alloys with gyroid cellular structures produced by LPBF technology

Çalışkan, Cemal İrfan; ŞAHİN, MERYEM; Koca, Aliihsan; Tarakçı, Gürkan; Okbaz, Abdulkerim; Dalkılıç, Ahmet Selim

doi:10.1177/09544062241307458

Mechanical performance and ANNbased prediction of Co-Cr dental alloys with gyroid cellular structures produced by LPBF technology

Yazarlar (6)

Cemal İrfan Çalışkan İstanbul Teknik Üniversitesi, Türkiye

Dr. Öğr. Üyesi Meryem ŞAHİN Tokat Gaziosmanpaşa Üniversitesi, Türkiye

Aliihsan Koca İstanbul Teknik Üniversitesi, Türkiye

Gürkan Tarakçı

Abdulkerim Okbaz Türkiye

Ahmet Selim Dalkılıç Türkiye

Makale Türü	Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
Dergi Adı	Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (Q3)
Dergi ISSN	0954-4062 Wos Dergi Scopus Dergi
Dergi Tarandığı Indeksler	SCI-Expanded
Makale Dili	Türkçe	Basım Tarihi	05-2025
Cilt / Sayı / Sayfa	239 / 9 / 3298–3316	DOI	10.1177/09544062241307458
Makale Linki	https://journals.sagepub.com/eprint/H67WDBFGFSJZWGVMYDBT/full

Özet

Gyroid structures exhibit significant potential in the fields of lightweight structural design, heat transfer, energy absorption, and biological applications. The use of gyroid for implants in dentistry is currently not sufficiently widespread. The research encompasses design, compression testing, cellular investigation using a digital microscope, and the application of artificial neural networks (ANNs) using data gained from the compression test. The ANN study and the test phase in which gyroid geometries are addressed by dental three-point bending tests are novel in this field. In the field of dentistry, this study compares the usability of five distinct gyroid design characteristics, including one model without a gyroid structure. During the testing, we found that the m1 model had an average maximum strength of 600 N, while the m3 model achieved 230 N. The remaining models achieved an approximate strength of 200 N. In the mechanical performance evaluation of the samples, a 40% weight reduction was achieved. An ANN model has been developed to predict the force experienced by gyroid structures under certain deformations depending on the infill ratio. This model was trained with data obtained from a three-point bending test. Using grid search and Monte Carlo cross-validation, the optimal multilayer perceptron structure was determined to have 12 neurons in the hidden layer, a mini-batch size of 8, and a learning rate of 0.0001. The Adam optimization algorithm was used to train the ANN model, which was constructed using the TensorFlow library. Evaluation metrics were used to test the model’s performance, and the results showed strong generalization capability and high accuracy with coefficient of determination ( R2) of 0.997, mean squared error (MSE) of 3.337E-05 kN2, root mean square error of (RMSE) 0.005777 kN, and mean absolute error (MAE) of 0.003633 kN on the test dataset.

Anahtar Kelimeler

BM Sürdürülebilir Kalkınma Amaçları

Atıf Sayıları

Mechanical performance and ANNbased prediction of Co-Cr dental alloys with gyroid cellular structures produced by LPBF technology

Dergi Adı	PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
Yayıncı	SAGE Publications Ltd
Açık Erişim	Hayır
ISSN	0954-4062
E-ISSN	2041-2983
CiteScore	4,1
SJR	0,440
SNIP	0,890

Mechanical performance and ANNbased prediction of Co-Cr dental alloys with gyroid cellular structures produced by LPBF technology

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