Energy Consumption Prediction of Additive Manufactured Tensile Strength Parts Using Artificial Intelligence
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Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Mary Ann Liebert, Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The manufacturing sector's interest in additive manufacturing (AM) methods is increasing daily. The increase in energy consumption requires optimization of energy consumption in rapid prototyping technology. This study aims to minimize energy consumption with determined production parameters. Four machine learning algorithms are preferred to model the energy consumption of the fused deposition modeling-based 3D printer. The real-time measured test sample data were trained, and the prediction model between the parameters of 3D fabrication and the energy consumption was created. The predicted model was evaluated using five performance criteria. These are mean square error (MSE), mean absolute error (MAE), root mean squared error (RMSE), R-squared (R2), and explained variance score (EVS). It has been seen that the Gaussian Process Regression model predicts energy consumption in the AM with high accuracy: R2 = 0.99, EVS = 0.99, MAE = 0.016, RMSE = 0.022, and MSE = 0.00049.
Açıklama
Anahtar Kelimeler
energy consumption, additive manufacturing, machine learning, fused deposition modeling, 3D printer
Kaynak
3d Printing and Additive Manufacturing
WoS Q Değeri
Q3
Scopus Q Değeri
Q1
Cilt
11
Sayı
5
