Ulkir Osman, Bayraklılar Mehmet Said, Kuncan Melih
Department of Electric and Energy, Mus Alparslan University, Mus, Turkey.
Department of Civil Engineering, Siirt University, Siirt, Turkey.
3D Print Addit Manuf. 2024 Oct 22;11(5):e1909-e1920. doi: 10.1089/3dp.2023.0189. eCollection 2024 Oct.
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), -squared ( ), and explained variance score (EVS). It has been seen that the Gaussian Process Regression model predicts energy consumption in the AM with high accuracy: = 0.99, EVS = 0.99, MAE = 0.016, RMSE = 0.022, and MSE = 0.00049.
制造业对增材制造（AM）方法的兴趣与日俱增。能源消耗的增加要求对快速成型技术中的能源消耗进行优化。本研究旨在通过确定的生产参数将能源消耗降至最低。首选四种机器学习算法对基于熔融沉积建模的3D打印机的能源消耗进行建模。对实时测量的测试样本数据进行训练，建立了3D制造参数与能源消耗之间的预测模型。使用五个性能标准对预测模型进行评估。这些标准是均方误差（MSE）、平均绝对误差（MAE）、均方根误差（RMSE）、决定系数（R²）和解释方差得分（EVS）。结果发现，高斯过程回归模型能够高精度地预测增材制造中的能源消耗：R² = 0.99，EVS = 0.99，MAE = 0.016，RMSE = 0.022，MSE = 0.00049。
