均方误差
能源消耗
熔融沉积模型
克里金
沉积(地质)
高斯过程
计算机科学
3D打印
工艺工程
模拟
数学
算法
机械工程
高斯分布
工程类
统计
机器学习
电气工程
物理
生物
古生物学
量子力学
沉积物
作者
Mohamed Achraf El youbi El idrissi,Loubna Laaouina,Adil Jeghal,Hamid Tairi,Moncef Zaki
摘要
Additive manufacturing (AM) technologies are growing more and more in the manufacturing industry; the increase in world energy consumption encourages the quantification and optimization of energy use in additive manufacturing processes. Orientation of the part to be printed is very important for reducing energy consumption. Our work focuses on defining the most appropriate direction for minimizing energy consumption. In this paper, twelve machine learning (ML) algorithms are applied to model energy consumption in the fused deposition modelling (FDM) process using a database of the FDM 3D printing of isovolumetric mechanical components. The adequate predicted model was selected using four performance criteria: mean absolute error (MAE), root mean squared error (RMSE), R-squared (R2), and explained variance score (EVS). It was clearly seen that the Gaussian process regressor (GPR) model estimates the energy consumption in FDM process with high accuracy: R2 > 99%, EVS > 99%, MAE < 3.89, and RMSE < 5.8.
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