{"title":"Multi-physics investigations on the gas-powder flow and the molten pool dynamics during directed energy deposition process","authors":"Chenghong Duan, X. Cao, Xiangpeng Luo, Dazhi Shang, X. Hao","doi":"10.1115/1.4062259","DOIUrl":null,"url":null,"abstract":"\n In order to establish a high-fidelity mechanism model for investigating the molten pool behaviors during directed energy deposition (DED) process, a molten pool dynamics model combined with the discrete element method is developed in present study. The proposed model contains newly added particle sources to further intuitively reproduce the interaction between the discrete powder particles and the molten pool. Meanwhile, the effects of the nozzle structure, carrier gas and shielding gas on the feedstock feeding process are simulated in detail using the gas-powder flow model based on the multi-phase flow theory. The gas-powder flow model is used to provide the reasonable outlet velocities, focal distance and radius of the focal point for the particle sources in the molten pool dynamics model, which solves the difficulty that the motion state of the powder streams obtained by the molten pool dynamics simulation are hard to reproduce the actual situation. Besides, relevant experiments are conducted to verify the accuracy of the developed models. The predicted parameters of the powder streams are consistent with the experiment, and the deviations of the predicted molten pool dimensions are less than 10%. The heat and mass transfer phenomena inside the molten pool are also revealed. Furthermore, the maximum size of the spherical pore defects is predicted to be 18.6 µm, which is underestimated by 7% compared to the microscopic observation. Taken together, the developed numerical model could augment and improve the training samples for the machine learning modelling of DED process.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Science and Engineering-transactions of The Asme","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4062259","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
In order to establish a high-fidelity mechanism model for investigating the molten pool behaviors during directed energy deposition (DED) process, a molten pool dynamics model combined with the discrete element method is developed in present study. The proposed model contains newly added particle sources to further intuitively reproduce the interaction between the discrete powder particles and the molten pool. Meanwhile, the effects of the nozzle structure, carrier gas and shielding gas on the feedstock feeding process are simulated in detail using the gas-powder flow model based on the multi-phase flow theory. The gas-powder flow model is used to provide the reasonable outlet velocities, focal distance and radius of the focal point for the particle sources in the molten pool dynamics model, which solves the difficulty that the motion state of the powder streams obtained by the molten pool dynamics simulation are hard to reproduce the actual situation. Besides, relevant experiments are conducted to verify the accuracy of the developed models. The predicted parameters of the powder streams are consistent with the experiment, and the deviations of the predicted molten pool dimensions are less than 10%. The heat and mass transfer phenomena inside the molten pool are also revealed. Furthermore, the maximum size of the spherical pore defects is predicted to be 18.6 µm, which is underestimated by 7% compared to the microscopic observation. Taken together, the developed numerical model could augment and improve the training samples for the machine learning modelling of DED process.
期刊介绍:
Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining