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A Fast Prediction Model for Liquid Metal Transfer Modes during the Wire Arc Additive Manufacturing Process.
Ouyang, Jiaqi; Li, Mingjian; Lian, Yanping; Peng, Siyi; Liu, Changmeng.
Affiliation
  • Ouyang J; Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, China.
  • Li M; Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, China.
  • Lian Y; Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, China.
  • Peng S; School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
  • Liu C; School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Materials (Basel) ; 16(7)2023 Apr 06.
Article in En | MEDLINE | ID: mdl-37049203
ABSTRACT
The liquid metal transfer mode in wire arc additive manufacturing (WAAM), plays an important role in determining the build quality. In this study, a fast prediction model based on the Young-Laplace equation, momentum equation, and energy conservation, is proposed, to identify the metal transfer modes, including droplet, liquid bridge, and wire stubbing, for a given combination of process parameters. To close the proposed model, high-fidelity numerical simulations are applied, to obtain the necessary inputs required by the former. The proposed model's accuracy and effectiveness are validated by using experimental data and high-fidelity simulation results. It is proved that the model can effectively predict the transition from liquid bridge, to droplet and wire stubbing modes. In addition, its errors in dripping frequency and liquid bridge height range from 6% to 18%. Moreover, the process parameter windows about transitions of liquid transfer modes have been established based on the model, considering wire feed speed, travel speed, heat source power, and material parameters. The proposed model is expected to serve as a powerful tool for the guidance of process parameter optimization, to achieve high-quality builds.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Materials (Basel) Year: 2023 Document type: Article Affiliation country: China Publication country: CH / SUIZA / SUÍÇA / SWITZERLAND

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Materials (Basel) Year: 2023 Document type: Article Affiliation country: China Publication country: CH / SUIZA / SUÍÇA / SWITZERLAND