Short pulse solid-state Raman laser and its application to laser-machining

Author(s):  
K. Deki ◽  
T. Arisawa ◽  
A. Nishimura ◽  
T. Usami ◽  
Y. Shimobeppu ◽  
...  
Author(s):  
Keith Mahoney ◽  
David Hwang ◽  
AnnMarie L. Oien ◽  
Glenn T. Bennett ◽  
Mark Kukla ◽  
...  
Keyword(s):  

Crystals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 114
Author(s):  
Hui Zhao ◽  
Shibo Dai ◽  
Siqi Zhu ◽  
Hao Yin ◽  
Zhen Li ◽  
...  

In the past few decades, the multifunctional optical crystals for all-solid-state Raman lasers have been widely studied by many scholars due to their compactness, convenience and excellent performance. In this review, we briefly show two kinds of multifunctional Raman crystals: self-Raman (laser and Raman effects) crystals and self-frequency-doubled Raman (frequency-doubling and Raman effects) crystals. We firstly introduce the properties of the self-Raman laser crystals, including vanadate, tungstate, molybdate and silicate doped with rare earth ions, as well as self-frequency-doubled Raman crystals, including KTiOAsO4 (KTA) and BaTeMo2O9 (BTM). Additionally, the domestic and international progress in research on multifunctional Raman crystals is summarized in the continuous wave, passively Q-switched, actively Q-switched and mode-locked regimes. Finally, we present the bottleneck in multifunctional Raman crystals and the outlook for future development. Through this review, we contribute to a general understanding of multifunctional Raman crystals.


Author(s):  
Michael D. T. McDonnell ◽  
Daniel Arnaldo ◽  
Etienne Pelletier ◽  
James A. Grant-Jacob ◽  
Matthew Praeger ◽  
...  

AbstractInteractions between light and matter during short-pulse laser materials processing are highly nonlinear, and hence acutely sensitive to laser parameters such as the pulse energy, repetition rate, and number of pulses used. Due to this complexity, simulation approaches based on calculation of the underlying physical principles can often only provide a qualitative understanding of the inter-relationships between these parameters. An alternative approach such as parameter optimisation, often requires a systematic and hence time-consuming experimental exploration over the available parameter space. Here, we apply neural networks for parameter optimisation and for predictive visualisation of expected outcomes in laser surface texturing with blind vias for tribology control applications. Critically, this method greatly reduces the amount of experimental laser machining data that is needed and associated development time, without negatively impacting accuracy or performance. The techniques presented here could be applied in a wide range of fields and have the potential to significantly reduce the time, and the costs associated with laser process optimisation.


Laser Physics ◽  
2016 ◽  
Vol 27 (1) ◽  
pp. 015405
Author(s):  
Zhiqiong Chen ◽  
Xihong Fu ◽  
Hangyu Peng ◽  
Jun Zhang ◽  
Li Qin ◽  
...  
Keyword(s):  

2019 ◽  
Vol 119 ◽  
pp. 105660 ◽  
Author(s):  
Milan Frank ◽  
Sergei N. Smetanin ◽  
Michal Jelínek ◽  
David Vyhlídal ◽  
Vladislav E. Shukshin ◽  
...  

Author(s):  
Milan Frank ◽  
Václav Kubeček ◽  
Michal Jelínek ◽  
Sergei Smetanin ◽  
L. I. Ivleva ◽  
...  

2017 ◽  
Author(s):  
Yoshihiro Nishigata ◽  
Cheng-Yeh Lee ◽  
Yuji Miyamoto ◽  
Katsuhiko Miyamoto ◽  
Yung-Fu Chen ◽  
...  

2018 ◽  
Vol 43 (11) ◽  
pp. 2527 ◽  
Author(s):  
Milan Frank ◽  
Sergei N. Smetanin ◽  
Michal Jelínek ◽  
David Vyhlídal ◽  
Lyudmila I. Ivleva ◽  
...  

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