Simultaneous high-speed x-ray transmission imaging and absolute dynamic absorptance measurements during high-power laser-metal processing
| Autor: | Cang Zhao, Jack Tanner, Alexandra B. Artusio-Glimpse, Paul A. Williams, Tao Sun, Niranjan D. Parab, Brian J. Simonds |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Materials science business.industry X-ray 02 engineering and technology 010501 environmental sciences Laser 01 natural sciences law.invention 020901 industrial engineering & automation Optics law Absorptance General Earth and Planetary Sciences Radiometry Laser power scaling business Porosity Absorption (electromagnetic radiation) Keyhole 0105 earth and related environmental sciences General Environmental Science |
| Zdroj: | Procedia CIRP. 94:775-779 |
| ISSN: | 2212-8271 |
| DOI: | 10.1016/j.procir.2020.09.135 |
| Popis: | During high-power laser metal processing, the absorbed light is intimately related to the molten metal cavity shape. For the first time, this relationship is observed directly and simultaneously by implementing state-of-the-art techniques of high-speed x-ray imaging and integrating-sphere radiometry. Experiments were performed on Ti-6Al-4V solid and powder under single spot laser illumination for laser conditions that cause keyhole formation and collapse. The data from x-ray imaging corroborates that a dramatic rise in laser absorption is due to keyhole formation. We also find that the keyhole area correlates most strongly with energy absorption followed closely by keyhole depth. Furthermore, time synchronization reveals correlations between keyhole fluctuations and sinusoidal variations in energy absorption that occur during nominally “stable” keyhole conditions. Absorption data show a 24 % periodic change in absorbed laser power at a 50 kHz frequency. The absorption peaks correlate to relatively large, open keyholes, whereas images taken at the troughs reveal keyholes with substantial undulations of the keyhole sidewalls. These observations give crucial, quantitative data for computational modeling whose aim is to predict porosity and defect formation. |
| Databáze: | OpenAIRE |
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