期刊
ADDITIVE MANUFACTURING
卷 55, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.addma.2022.102873
关键词
Laser powder bed fusion (LPBF); Infrared thermography; Powder thickness; Recoater crash; Neural network; Additive manufacturing
资金
- U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office [DE-EE0007613]
- Clean Energy Smart Manufacturing Innovation Institute (CESMII)
This paper investigates the in-situ characterization of the powder layer using thermographic inspection and proposes a method to predict part distortion and prevent damage to the recoater system and powder layer abrasion.
The laser powder bed fusion (LPBF) process is strongly influenced by the characteristics of the powder layer, including its thickness and thermal transport properties. This paper investigates in-situ characterization of the powder layer using thermographic inspection. A thermal camera monitors the temperature history of the powder surface immediately after a layer of new powder is deposited by the recoating system. During this process, thermal energy diffuses from the underlying solid part, eventually raising the temperature of the above powder layer. Guided by 1D modeling of this heat-up process, experiments show how the parameterized thermal history can be correlated with powder layer thickness and its thermal conductivity. A neural network, based on the parameterized thermal history, further improves the correlation after training. It is used to predict the part distortion for an unsupported structure. This method detects serious part distortion several layers before the part breaks through the powder layer and interacts with the recoater. This approach can be automated to prevent catastrophic recoater crashes or abrasion of soft wipers and has the potential to monitor local properties of the powder layer in-situ.
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