Exploration of synchronized dual-beam laser melting with high speed video imaging
Not Available
Vanmunster, L.↗
Engineering topics
Guss, G.
Publications and source records attributed to Guss, G..
Improving the laser performance of ion beam sputtered dielectric thin films through the suppression of nanoscale defects by employing a xenon sputtering gas
Laser damage-prone precursors in high index materials such as hafnia are believed to be the primary limiter in the performance of dielectric multilayer films to advance ultra-high power and energy laser applications. Removing or suppressing these precursors is the key to fabricating laser damage resistant thin films for the enabling technologies. Early work has revealed that nanobubbles formed by entrapped argon (Ar) working gas in ion beam sputtering (IBS) produced hafnia films are primarily responsible for the onset of laser damage upon exposure to UV, ns-laser pulses. In this study, we demonstrate that the UV ns-laser damage onset of IBS produced hafnia films can be improved to 3.1 +/- 0.2 J/cm 2 by substituting the conventional Ar working gas with xenon (Xe), a nearly 1 J/cm 2 increase from that of the Ar produced hafnia films. In addition to the suppression of the overall point-defect density of the hafnia films, the reduction of the Xe entrapment eliminates the nanobubbles and the generation of plasmas that initiates the laser damage. The defect suppression and its correlation to the increase in laser damage threshold is revealed by the combined analysis of Rutherford backscattering spectroscopy, electron paramagnetic resonance spectroscopy, transmission electron microscopy, and laser damage testing. Monte Carlo simulations suggest a much smaller entrapment of Xe gas by comparison to Ar, which is attributed to the significant difference in the energy of the reflected neutrals (3X) which are likely to be implanted. These results provide an effective process route with a fundamental understanding for producing high laser damage resistant dielectric films for high power and high energy laser applications.
36 MATERIALS SCIENCE↗
Photodiode-based machine learning for optimization of laser powder bed fusion parameters in complex geometries
We report the quality of parts produced through laser powder bed fusion additive manufacturing can be irregular, with complex geometries sometimes exhibiting dimensional inaccuracies and defects. For optimal part quality, laser process parameters should be selected carefully prior to printing and adjusted during the print if necessary. This is challenging since approaches to control and optimize the build parameters need to take into account the part geometry, the material, and the complex physics of laser powder bed fusion. This work describes a data-driven approach using experimental diagnostics for the optimization of laser process parameters prior to printing. A training dataset is generated by collecting high speed photodiode signal data while printing simple parts containing key geometry features with various process parameter strategies. Supervised learning approaches are employed to train both a forward model and an inverse model. The forward model takes as inputs track-wise geometry features and laser parameters and outputs the photodiode signal along the scan path. The inverse model takes as inputs the geometry features and photodiode signal and predicts the laser parameters. Given the part geometry and a desired photodiode signal, the inverse model can thus determine the required laser parameters. Two test parts which contain defect-prone features are used to assess the validity of the inverse model. The use of the model leads to improved part quality (higher dimensional accuracy, reduced dross, reduced distortion) for both test geometries.
36 MATERIALS SCIENCE↗
Investigation of UV, ns-laser damage resistance of hafnia films produced by electron beam evaporation and ion beam sputtering deposition methods
Laser-induced damage in coating materials with a high index of refraction, such as hafnia, limits the performance of high power and high energy laser systems. Understanding the underlying physics responsible for laser damage holds the key for developing damage-resistant optical films. Previous studies have reported a substantial difference in laser damage onset for hafnia films produced by different deposition methods, yet the underlying mechanisms for the observed difference remain elusive. We combined laser damage testing with analytical characterizations and theoretical simulations to investigate the response of hafnia films produced by electron (e-) beam evaporation vs ion beam sputtering (IBS) methods upon UV ns-laser exposure. We found that e-beam produced hafnia films were overall more damage resistant; in addition, we observed a polarization anisotropy associated with the onset of damage in the e-beam films, while this effect was absent in the latter films. The observed differences can be attributed to the stark contrast in the pressure inside the pores inherent in both films. The high pressure inside the IBS-induced nanobubbles has been shown to reduce the threshold for laser-induced plasma breakdown leading to film damage. The polarization effects in the e-beam coatings can be related to the asymmetric electric field intensification induced by the columnar void structure. Our findings provide a fundamental basis for developing strategies to produce laser damage-resistant coatings for UV pulsed laser applications.
36 MATERIALS SCIENCE↗
Process optimization of complex geometries using feed forward control for laser powder bed fusion additive manufacturing
Additive manufacturing (AM) enables the fabrication of complex designs that are difficult to create by other means. Metal parts manufactured by laser powder bed fusion (LPBF) can incorporate intricate design features and demonstrate desirable mechanical properties. However, printing a part that is qualified for its intended application often involves reprinting and discarding many parts to eliminate defects, improve dimensional accuracy, and increase repeatability. The process of iteratively converging on the appropriate build parameters increases the time and cost of creating functional LPBF manufactured parts. This work describes a fast, scalable method for part-scale process optimization of arbitrary geometries. Additionally, the computational approach uses feature extraction to identify scan vectors in need of parameter adaptation and applies results from simulation-based feed forward control models. This method provides a framework to quickly optimize complex parts through the targeted application of models with a range of fidelity and by automating the transfer of optimization strategies to new part designs. The computational approach and algorithmic framework are described, a software package is implemented, the method is applied to parts with complex features, and parts are printed on a customized open architecture LPBF machine.
3D printing↗