Machine-learning-based monitoring and optimization of processing parameters in 3D printing

ABSTRACT

Additive manufacturing (AM), commonly known as 3D printing, is a rapidly growing technology. Guaranteeing the quality and mechanical strength of printed parts is an active research area. Most of the existing methods adopt open-loop-like Machine Learning (ML) algorithms that can be used only for predicting properties of printed parts without any quality assuring mechanism. Some closed-loop approaches, on the other hand, consider a single adjustable processing parameter to monitor the properties of a printed part. This work proposes both open-loop and closed-loop ML models and integrates them to monitor the effects of processing parameters on the quality of printed parts. By using experimental 3D printing data, an open-loop classification model formulates the relationship between processing parameters and printed part properties. Then, a closed-loop control algorithm that combines open-loop ML models and a fuzzy inference system is constructed to generate optimized processing parameters for better printed part properties. The proposed system realizes the application of a closed-loop control system to AM.

KEYWORDS:

• Additive manufacturing
• closed-loop
• 3D printing
• digital manufacturing
• machine learning
• processing parameters

Nomenclature

3D	=	Three Dimensional
AM	=	Additive Manufacturing
CAD	=	Computer Aided Design
DNN	=	Deep Neural Network
DT	=	Decision Tree
FLC	=	Fuzzy Logic Controller
LR	=	Logistic Regression
ML	=	Machine Learning
MVLR	=	Multi-Variate Linear Regression
NN	=	Neural Network
RF	=	Random Forest
SVM	=	Support Vector Machine
WAAM	=	Wire and Arc Additive Manufacturing

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Key Research and Development Program of China (No. 2018YFB1700403); National Natural Science Foundation of China under Grants U1909204, U1909218, U1811463, 61872365 & 61806198; CAS Key Technology Talent Program (Zhen Shen); The Guangdong Basic and Applied Basic Research Foundation under Grant 2021B1515140034; The Foshan Science and Technology Innovation Team Project under Grant 2018IT100142; The Scientific Instrument Developing Project of the Chinese Academy of Sciences under Grant No. YZQT014; CAS STS Dongguan Joint Project 20201600200072