Energy Consumption Prediction of Fused Deposition 3D Printer Based on Improved Regularized BP Neural Network

2019 5th International Conference on Energy Materials and Environment Engineering

能源学;生态环境科学

Junwen, Chen^1^2^3 ; Gang, Zhao^1^2^3 ; Hua, Zhang^1^2^3

Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan

430081, China^1

Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, China^2

Academy of Green Manufacturing Engineering, Wuhan University of Science and Technology, China^3

关键词: BP neural networks;    Calculation efficiency;    Energy consumption prediction;    Fitting problems;    MATLAB environment;    Predictive modeling;    Process parameters;    Regularization methods;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/295/3/032001/pdf
DOI  :  10.1088/1755-1315/295/3/032001

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

An energy consumption prediction method based on process parameters and neural network was proposed to study the inherent energy consumption characteristics of open source melt deposition 3D printer and improve energy efficiency. An improved regularized network is used for optimization to avoid over-fitting and under-fitting problems. The orthogonal test sample data were trained in MATLAB environment, and the predictive model between process parameters and energy consumption of open source melt deposition 3D printing was established. The energy consumption prediction results of BP networks and regularized networks are compared by analyzing convergence curves and network training charts. The results show that the BP network training has experienced over-fitting, resulting in a prediction energy consumption error of about 10%. The improved regularization method effectively avoids the over-fitting phenomenon and the error of energy consumption prediction is about 1%. It can effectively improve the calculation efficiency of energy consumption prediction, which shows the accuracy of this method in energy consumption prediction.