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Adaptive four-level modeling of laser cooling of solids
Laser cooling of rare-earth doped solids has been demonstrated across a wide range of material platforms, inspiring the development of simple phenomenological models such as the four-level model to elucidate the universal properties of laser cooling under various operating conditions. However, these models usually require the input of full absorption spectra that must be provided experimentally or by additional complicated atomic modeling. In this Letter, we propose that a four-level model, when extended to admit effective energy levels adaptive to the pumping photon energy, can accurately predict the cooling efficiency as a function of temperature and pumping frequency using only few inputs such as the absorption coefficient measured at a single frequency and temperature. Our model exploits the quasi-equilibrium properties of the excitation of rare-earth ions for the determination of the effective four energy levels. The model is validated against published experimental results for a span of materials including ytterbium/thulium-doped glass and crystals. With the verified model, we derive explicit expressions for the optimal frequency and the operating bandwidth of pumping lasers. Our model significantly simplifies the modeling process of laser cooling and is expected to stimulate further development of optical refrigeration.
Optimization of stochastic feature properties in laser powder bed fusion
Process parameter selection in laser powder bed fusion (LPBF) controls the as-printed dimensional tolerances, pore formation, surface quality and microstructure of printed metallic structures. Measuring the stochastic mechanical performance for a wide range of process parameters is cumbersome both in time and cost. As such, in this study, we overcome these hurdles by using high-throughput tensile (HTT) testing of over 250 dogbone samples to examine process-driven performance of strut-like small features, ~1 mm 2 in austenitic stainless steel (316 L). The output mechanical properties, porosity, surface roughness and dimensional accuracy were mapped across the printable range of laser powers and scan speeds using a continuous wave laser LPBF machine. Tradeoffs between ductility and strength are shown across the process space and their implications are discussed. While volumetric energy density deposited onto a substrate to create a melt-pool can be a useful metric for determining bulk properties, it was not found to directly correlate with output small feature performance.
Unveiling mechanisms and onset threshold of humping in high-speed laser welding
The fabrication of fuel cells relies on a rapid laser welding process. However, challenges arise with the occurrence of humping when the welding speed surpasses a critical threshold, which poses difficulties in achieving a smooth surface finish and a consistent weld strength. This study aims to elucidate the humping mechanisms by analyzing the morphology of molten pool and the characteristics of melt flow at varying welding speeds via in situ synchrotron high-speed X-ray imaging and computational fluid dynamics simulations. Our findings indicate that the short keyhole rear wall, the high backward melt velocity, and the prolonged tail of molten pool are the primary factors contributing to the onset of humping. Furthermore, a dimensionless humping index (π h ) was introduced, which successfully captured the onset threshold of humping across different literatures. This index not only provides a quantitative description of the humping formation tendency but also serves as a valuable tool for optimizing the laser welding process.
Pulse Strengthened and Laser Edge Sealed Vacuum Insulation Glazing
A scalable, low-cost processing strategy for glass strengthening and sealing is proposed to improve the thermal performance of insulating glass units and develop innovative window technologies. In this project, a combination of large area photonic processing, additive manufacturing, and laser encapsulation techniques is reported to realize a vacuum glazing technology to meet the cost, performance, reliability, and throughput demands. A modeling framework for evaluating the mechanical and thermal response was established to verify the structural deformation and thermal conductance. Micro-size glass frits in printing ink and a continuous-wave laser curing were employed to allow the formation of a hermetic bonding layer and led to improvement of glass edge sealing. Various sealing parameters, including laser traveling speed, spot diameter, and laser power were optimized. The water seepage and mechanical strength of the resulting glass-to-glass bonding were examined as well. These results showcase the development of a practical vacuum insulating glazing with several unique features through a combination of photonic processing, laser encapsulation, and additive manufacturing.
High-throughput, Ultra-fast Laser Sintering of Ceramics and AI Based Prediction on Processing-Microstructure-Property Relationships
We report high-throughput, ultra-fast laser sintering of alumina sample array and characterization of sample units’ microstructure and hardness, as a fast exploration of laser processing parameters, microstructure, and property. These experimental data were used to train machine-learning (ML) models. Accurate ML predictions were demonstrated for the processing-microstructure-property relationship, specifically in (1) prediction of the microstructure of alumina under arbitrary laser power and (2) prediction of the expected microstructure from the desired hardness. An independent neural network was developed and showed that ML-predicted microstructure had less than 10% error from real ones, in terms of projected hardness. To monitor the microstructure during laser sintering, we demonstrated an ML model that can instantaneously predict ceramic’s microstructure at the laser spot, based on the laser spot brightness. The ML model can generate more than 10 predictions per second, and the error in average grain size was less than 5% from the experimental observations.
The origin and formation of oxygen inclusions in austenitic stainless steels manufactured by laser powder bed fusion
The origins of nano-scale oxide inclusions in 316L austenitic stainless steel (SS) manufactured by laser powder bed fusion (L-PBF) was investigated by quantifying the possible intrusion pathways of oxygen contained in the precursor powder, extraneous oxygen from the process environment during laser processing, and moisture contamination during powder handling and storage. When processing the fresh, as-received powder in a wellcontrolled environment, the oxide inclusions contained in the precursor powder were the primary contributors to the formation of nano-scale oxides in the final additive manufactured (AM) product. These oxide inclusions were found to be enriched with oxygen getter elements like Si and Mn. By controlling the extraneous oxygen level in the process environment, the oxygen level in AM produced parts was found to increase with the extraneous oxygen level. The intrusion pathway of this extra oxygen was found to be dominated by the incorporation of spatter particles into the build during processing. Moisture induced oxidation during powder storage was also found to result in a higher oxide density in the AM produced parts. SS 316L powder free of Si and Mn oxygen getters was processed in a well-controlled environment and resulted in a similar level of oxygen intrusion. Finally, microhardness testing indicated that the oxide volume fraction increase from extraneous oxygen did not influence hardness values. However, a marked decrease in hardness was found for the humidified and Si-Mn free AM processed parts.
Process-structure relationships in laser directed energy deposition of molybdenum powder within a Ti–6Al–4V matrix
Not Available
Deformation mechanisms of multiphase microstructures in laser powder bed fusion processed stainless steels
Not Available
A Bezier Curve Informed Melt Pool Geometry to Model Additive Manufacturing Microstructures Using SPPARKS
Additive manufacturing is a transformative technology with the potential to manufacture designs which traditional subtractive machining methods cannot. Additive manufacturing offers fast builds at near final desired geometry; however, material properties and variability from part to part remain a challenge for certification and qualification of metallic components. AM induced metallic microstructures are spatially heterogeneous and highly process dependent. Engineering properties such as strength and toughness are significantly affected by microstructure morphologies resulting from the manufacturing process Linking process parameters to microstructures and ultimately to the dynamic response of AM materials is critical to certifying and qualifying AM built parts and components and improving the performance of AM materials. The AM fabrication process is characterized by building parts layer by layer using a selective laser melt process guided by a computer. A laser selectively scans and melts metal according to a designated geometry. As the laser scans, metal melts, fuses, and solidifies forming the final geometry in a layerwise fashion. As the laser heat source moves away, the metal cools and solidifies forming metallic microstructures. This work describes a microstructure modeling application implemented in the SPPARKS kinetic Monte Carlo computational framework for simulating the resulting microstructures. The application uses Bzier curves and surfaces to model the melt pool surface and spatial temperature profile induced by moving the laser heat source; it simulates the melting and fusing of metal at the laser hot spot and microstructure formation and evolution when the laser moves away. The geometry of the melt pool is quite flexible and we explore effects of variances in model parameters on simulated microstructures.
Enhancement of Gas-Atomized 304L Stainless Steel Powder by Plasma Spheroidization for use in the Laser Powder Bed Fusion Process
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Laser Induced Breakdown Spectroscopy for In-Situ Monitoring of Laser Powder Bed Fusion Processing
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Towards tractable, fully time-resolved simulations of engineering scale parts built using the laser powder bed fusion process.
Abstract not provided.
Laser Induced Breakdown Spectroscopy for Chemistry and Composition Monitoring of Laser Powder Bed Fusion Processing
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Laser Speckle Photometry for Residual Stress Diagnosis of Laser Powder Bed Fusion Processing
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Uncertainty quantification for quantitative validation of laser powder bed fusion process outcomes
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