About this Abstract |
Meeting |
Advances in Welding and Additive Manufacturing Research 2022
|
Symposium
|
Advances in Welding and Additive Manufacturing Research 2022
|
Presentation Title |
Optimization of Intelligent Additive Manufacturing Process-based on Machine Learning |
Author(s) |
Yuewei Ai, Jie Mei, Yachao Yan, Guangyu Dong |
On-Site Speaker (Planned) |
Jie Mei |
Abstract Scope |
Morphology and microstructure of the cladding layer of workpiece are the key factors for determining morphology and microstructure of the cladding layer of workpiece. While the qualitative analysis has been made by using experimental and numerical simulation methods, it can only obtain the influence trend, so the performance used in manufacturing needs to be improved. In order to solve this problem, the long axis, short axis, center temperature and area parameters of the molten pool in the process of motion are obtained as the quantitative index of molten pool dynamic behaviors. Through building the relational model between additive manufacturing process parameters and cladding layer morphology and building the relationship between process parameters and molten pool temperature field by XGBoost algorithm, this paper reveals the mechanism of the influence of process parameters on molten pool temperature field and quantifies the relationship between process parameters and the cladding layer morphology parameters. This paper also obtains a set of process parameters by the superior morphology parameters through the algorithm model. The optimal process parameters combination obtained by the predictive model is verified by experiment, which improves the intelligent regulation level of additive manufacturing in manufacturing industry. |
Proceedings Inclusion? |
Undecided |