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The widespread application of metal additive manufacturing (AM) is limited by the ability to control the complex interactions between the energy source and the feedstock material. Here, we develop a generalizable process to introduce nanoscale grooves to the surface of metal powders which increases the powder absorptivity by up to 70% during laser powder bed fusion. Absorptivity enhancements in copper, copper-silver, and tungsten enable energy-efficient manufacturing, with printing of pure copper at relative densities up to 92% using laser energy densities as low as 83 joules per cubic millimeter. Simulations show that the enhanced powder absorptivity results from plasmon-enabled light concentration in nanoscale grooves combined with multiple scattering events. The approach taken here demonstrates a general method to enhance the absorptivity and printability of reflective and refractory metal powders by changing the surface morphology of the feedstock without altering its composition.

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In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM's intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

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The rapid collection and indexing of electron diffraction patterns as produced via electron backscatter diffraction (EBSD) has enabled crystallographic orientation and structural determination, as well as additional property-determining strain and dislocation density information with increasing speed, resolution, and efficiency. Pattern indexing quality is reliant on the noise of the collected electron diffraction patterns, which is often convoluted by sample preparation and data collection parameters. EBSD acquisition is sensitive to many factors and thus can result in low confidence index (CI), poor image quality (IQ), and improper minimization of fit, which can result in noisy datasets and misrepresent the microstructure. In an attempt to enable both higher speed EBSD data collection and enable greater orientation fit accuracy with noisy datasets, an image denoising autoencoder was implemented to improve pattern quality. We show that EBSD data processed through the autoencoder results in a higher CI, IQ, and a more accurate degree of fit. In addition, using denoised datasets in HR-EBSD cross correlative strain analysis can result in reduced phantom strain from erroneous calculations due to the increased indexing accuracy and improved correspondence between collected and simulated patterns.

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Laser powder bed fusion (LPBF) is a highly dynamic multi-physics process used for the additive manufacturing (AM) of metal components. Improving process understanding and validating predictive computational models require high-fidelity diagnostics capable of capturing data in challenging environments. Synchrotron x-ray techniques play a vital role in the validation process as they are the only in situ diagnostic capable of imaging sub-surface melt pool dynamics and microstructure evolution during LPBF-AM. In this article, a laboratory scale system designed to mimic LPBF process conditions while operating at a synchrotron facility is described. The system is implemented with process accurate atmospheric conditions, including an air knife for active vapor plume removal. Significantly, the chamber also incorporates a diagnostic sensor suite that monitors emitted optical, acoustic, and electronic signals during laser processing with coincident x-ray imaging. The addition of the sensor suite enables validation of these industrially compatible single point sensors by detecting pore formation and spatter events and directly correlating the events with changes in the detected signal. Experiments in the Ti-6Al-4V alloy performed at the Stanford Synchrotron Radiation Lightsource using the system are detailed with sufficient sampling rates to probe melt pool dynamics. X-ray imaging captures melt pool dynamics at frame rates of 20 kHz with a 2 µm pixel resolution, and the coincident diagnostic sensor data are recorded at 470 kHz. This work shows that the current system enables the in situ detection of defects during the LPBF process and permits direct correlation of diagnostic signatures at the exact time of defect formation.

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High thermal gradients and complex melt pool instabilities involved in powder bed fusion­based metal additive manufacturing using focused Gaussian-shaped beams often lead to high porosity, poor morphological quality, and degraded mechanical performance. We show here that Bessel beams offer unprecedented control over the spatiotemporal evolution of the melt pool in stainless steel (SS 316L) in comparison to Gaussian beams. Notably, the nondiffractive nature of Bessel beams enables greater tolerance for focal plane positioning during 3D printing. We also demonstrate that Bessel beams significantly reduce the propensity for keyhole formation across a broad scan parameter space. High-speed imaging of the melt pool evolution and solidification dynamics reveals a unique mechanism where Bessel beams stabilize the melt pool turbulence and increase the time for melt pool solidification, owing to reduced thermal gradients. Consequently, we observe a distinctively improved combination of high density, reduced surface roughness, and robust tensile properties in 3D-printed test structures.

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State-of-the-art metal 3D printers promise to revolutionize manufacturing, yet they have not reached optimal operational reliability. The challenge is to control complex laser-powder-melt pool interdependency (dependent upon each other) dynamics. We used high-fidelity simulations, coupled with synchrotron experiments, to capture fast multitransient dynamics at the meso-nanosecond scale and discovered new spatter-induced defect formation mechanisms that depend on the scan strategy and a competition between laser shadowing and expulsion. We derived criteria to stabilize the melt pool dynamics and minimize defects. This will help improve build reliability.

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Laser powder bed fusion (LPBF) is a method of additive manufacturing characterized by the rapid scanning of a high powered laser over a thin bed of metallic powder to create a single layer, which may then be built upon to form larger structures. Much of the melting, resolidification, and subsequent cooling take place at much higher rates and with much higher thermal gradients than in traditional metallurgical processes, with much of this occurring below the surface. We have used in situ high speed X-ray diffraction to extract subsurface cooling rates following resolidification from the melt and above the ß-transus in titanium alloy Ti-6Al-4V. We observe an inverse relationship with laser power and bulk cooling rates. The measured cooling rates are seen to correlate to the level of residual strain borne by the minority ß-Ti phase with increased strain at slower cooling rates. The α-Ti phase shows a lattice contraction which is invariant with cooling rate. We also observe a broadening of the diffraction peaks which is greater for the ß-Ti phase at slower cooling rates and a change in the relative phase fraction following LPBF. These results provide a direct measure of the subsurface thermal history and demonstrate its importance to the ultimate quality of additively manufactured materials.

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A key challenge for metasurface research is locally controlling at will the nanoscale geometric features on meter-scale apertures. Such a technology is expected to enable large aperture meta-optics and revolutionize fields such as long-range imaging, lasers, laser detection and ranging (LADAR), and optical communications. Furthermore, these applications are often more sensitive to light-induced and environmental degradation, which constrains the possible materials and fabrication process. Here, we present a relatively simple and scalable method to fabricate a substrate-engraved metasurface with locally printed index determined by induced illumination, which, therefore, addresses both the challenges of scalability and durability. In this process, a thin metal film is deposited onto a substrate and transformed into a mask via local laser-induced dewetting into nanoparticles. The substrate is then dry-etched through this mask, and selective mask removal finally reveals the metasurface. We show that masking by the local nanoparticle distribution, and, therefore, the local index, is dependent on the local light-induced dewetting temperature. We demonstrate printing of a free-form pattern engraved into a fused silica glass substrate using a laser raster scan. Large-scale spatially controlled engraving of metasurfaces has implications on other technological fields beyond optics, such as surface fluidics, acoustics, and thermomechanics.

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Laser powder bed fusion additive manufacturing is an emerging 3D printing technique for the fabrication of advanced metal components. Widespread adoption of it and similar additive technologies is hampered by poor understanding of laser-metal interactions under such extreme thermal regimes. Here, we elucidate the mechanism of pore formation and liquid-solid interface dynamics during typical laser powder bed fusion conditions using in situ X-ray imaging and multi-physics simulations. Pores are revealed to form during changes in laser scan velocity due to the rapid formation then collapse of deep keyhole depressions in the surface which traps inert shielding gas in the solidifying metal. We develop a universal mitigation strategy which eliminates this pore formation process and improves the geometric quality of melt tracks. Our results provide insight into the physics of laser-metal interaction and demonstrate the potential for science-based approaches to improve confidence in components produced by laser powder bed fusion.

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In situ X-ray-based measurements of the laser powder bed fusion (LPBF) additive manufacturing process produce unique data for model validation and improved process understanding. Synchrotron X-ray imaging and diffraction provide high resolution, bulk sensitive information with sufficient sampling rates to probe melt pool dynamics as well as phase and microstructure evolution. Here, we describe a laboratory-scale LPBF test bed designed to accommodate diffraction and imaging experiments at a synchrotron X-ray source during LPBF operation. We also present experimental results using Ti-6Al-4V, a widely used aerospace alloy, as a model system. Both imaging and diffraction experiments were carried out at the Stanford Synchrotron Radiation Lightsource. Melt pool dynamics were imaged at frame rates up to 4 kHz with a ∼1.1 µm effective pixel size and revealed the formation of keyhole pores along the melt track due to vapor recoil forces. Diffraction experiments at sampling rates of 1 kHz captured phase evolution and lattice contraction during the rapid cooling present in LPBF within a ∼50 × 100 µm area. We also discuss the utility of these measurements for model validation and process improvement.

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Many traditional approaches for strengthening steels typically come at the expense of useful ductility, a dilemma known as strength-ductility trade-off. New metallurgical processing might offer the possibility of overcoming this. Here we report that austenitic 316L stainless steels additively manufactured via a laser powder-bed-fusion technique exhibit a combination of yield strength and tensile ductility that surpasses that of conventional 316L steels. High strength is attributed to solidification-enabled cellular structures, low-angle grain boundaries, and dislocations formed during manufacturing, while high uniform elongation correlates to a steady and progressive work-hardening mechanism regulated by a hierarchically heterogeneous microstructure, with length scales spanning nearly six orders of magnitude. In addition, solute segregation along cellular walls and low-angle grain boundaries can enhance dislocation pinning and promote twinning. This work demonstrates the potential of additive manufacturing to create alloys with unique microstructures and high performance for structural applications.

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We present two strategies to minimize laser damage in transparent conductive films. The first consists of improving heat dissipation by selection of substrates with high thermal diffusivity or by addition of capping layer heatsinks. The second is reduction of bulk energy absorption by lowering free carrier density and increasing mobility, while maintaining film conductance with thicker films. Multi-pulse laser damage tests were performed on tin-doped indium oxide (ITO) films configured to improve optical lifetime damage performance. Conditions where improvements were not observed are also described. When bulk heating is not the dominant damage process, discrete defect-induced damage limits damage behavior.

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Selective Laser Melting (SLM) of metal powder bed layers, whereby 3D metal objects can be printed from a digital file with unprecedented design flexibility, is spurring manufacturing innovations in medical, automotive, aerospace and textile industries. Because SLM is based on raster-scanning a laser beam over each layer, the process is relatively slow compared to most traditional manufacturing methods (hours to days), thus limiting wider spread use. Here we demonstrate the use of a large area, photolithographic method for 3D metal printing, using an optically-addressable light valve (OALV) as the photomask, to print entire layers of metal powder at once. An optical sheet of multiplexed ~5 kW, 20 ms laser diode and ~1 MW, 7 ns Q-switched laser pulses are used to selectively melt each layer. The patterning of near infrared light is accomplished by imaging 470 nm light onto the transmissive OALV, which consists of polarization-selective nematic liquid crystal sandwiched between a photoconductor and transparent conductor for switching.

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The results of detailed experiments and finite element modeling of metal micro-droplet motion associated with metal additive manufacturing (AM) processes are presented. Ultra high speed imaging of melt pool dynamics reveals that the dominant mechanism leading to micro-droplet ejection in a laser powder bed fusion AM is not from laser induced recoil pressure as is widely believed and found in laser welding processes, but rather from vapor driven entrainment of micro-particles by an ambient gas flow. The physics of droplet ejection under strong evaporative flow is described using simulations of the laser powder bed interactions to elucidate the experimental results. Hydrodynamic drag analysis is used to augment the single phase flow model and explain the entrainment phenomenon for 316 L stainless steel and Ti-6Al-4V powder layers. The relevance of vapor driven entrainment of metal micro-particles to similar fluid dynamic studies in other fields of science will be discussed.

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The light-matter interaction of an optical beam and metal micro-particulates at the vicinity of an optical substrate surface is critical to the many fields of applied optics. Examples of impacted fields are laser-induced damage in high power laser systems, sub-wavelength laser machining of transmissive materials, and laser-target interaction in directed energy applications. We present a full-wave-based model that predicts the laser-induced plasma pressure exerted on a substrate surface as a result of light absorption in surface-bound micron-scale metal particles. The model predictions agree with experimental observation of laser-induced shallow pits, formed by plasma emission and etching from surface-bound metal micro-particulates. It provides an explanation for the prototypical side lobes observed along the pit profile, as well as for the dependence of the pit shape on the incident laser and particle parameters. Furthermore, the model highlights the significance of the interference of the incident light in the open cavity geometry formed between the micro-particle and the substrate in the resulting pit shape.

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Laser-induced Hertzian fractures on the exit surface of silica glass are found to result from metal surface-bound micro particles. Two types of metal micro-spheres are studied (stainless-steel and Al) using ultraviolet laser light. The fracture initiation probability curve as a function of fluence is obtained, resulting in an initiation threshold fluence of 11.1 ± 4.7 J/cm2 and 16.5 ± 4.5 J/cm2 for the SS and Al particles, accordingly. The modified damage density curve is calculated based on the fracture probability. The calculated momentum coupling coefficient linking incident laser fluence to the resulting plasma pressure is found to be similar for both particles: 32.6 ± 15.4 KN/J and 28.1 ± 10.4 KN/J for the SS and Al cases accordingly.

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The interaction of nanosecond laser pulses at 1064- and 355-nm with micro-scale, nominally spherical metallic particles is investigated in order to elucidate the governing interaction mechanisms as a function of material and laser parameters. The experimental model used involves the irradiation of metal particles located on the surface of transparent plates combined with time-resolved imaging capable of capturing the dynamics of particle ejection, plume formation and expansion along with the kinetics of the dispersed material from the liquefied layer of the particle. The mechanisms investigated in this work are informative and relevant across a multitude of materials and irradiation geometries suitable for the description of a wide range of specific applications. The experimental results were interpreted using physical models incorporating specific processes to assess their contribution to the overall observed behaviors. Analysis of the experimental results suggests that the induced kinetic properties of the particle can be adequately described using the concept of momentum coupling introduced to explain the interaction of plane metal targets to large-aperture laser beams. The results also suggest that laser energy deposition on the formed plasma affects the energy partitioning and the material modifications to the substrate.

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High peak power laser systems are vulnerable to performance degradation due to particulate contamination on optical surfaces. In this work, we show using model contaminant particles that their optical properties decisively determine the nature of the optical damage. Borosilicate particles with low intrinsic optical absorption undergo ablation initiating in their sub-surface, leading to brittle fragmentation, distributed plasma formation, material dispersal and ultimately can lead to micro-fractures in the substrate optical surface. In contrast, energy coupling into metallic particles is highly localized near the particle-substrate interface leading to the formation of a confined plasma and subsequent etching of the substrate surface, accompanied by particle ejection driven by the recoil momentum of the ablation plume. While the tendency to create fractured surface pitting from borosilicate is stochastic, the smooth ablation pits created by metal particles is deterministic, with pit depths scaling linearly with laser fluence. A simple model is employed which predicts ~3x electric field intensity enhancement from surface-bound fragments. In addition, our results suggest that the amount of energy deposited in metal particles is at least twice that in transparent particles.

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This paper demonstrates the use of dynamic laser speckle autocorrelation spectroscopy in conjunction with the photothermal treatment of nanoporous gold (np-Au) thin films to probe nanoscale morphology changes during the photothermal treatment. Utilizing this spectroscopy method, backscattered speckle from the incident laser is tracked during photothermal treatment and both the characteristic feature size and annealing time of the film are determined. These results demonstrate that this method can successfully be used to monitor laser-based surface modification processes without the use of ex-situ characterization.

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Libraries of nanostructured materials on a single chip are a promising platform for high throughput and combinatorial studies of structure-property relationships in the fields of physics and biology. Nanoporous gold (np-Au), produced by an alloy corrosion process, is a nanostructured material specifically suited for such studies because of its self-similar thermally induced coarsening behavior. However, traditional heat application techniques for the modification of np-Au are bulk processes that cannot be used to generate a library of different pore sizes on a single chip. Here, laser micro-processing offers an attractive solution to this problem by providing a means to apply energy with high spatial and temporal resolution. In the present study we use finite element multiphysics simulations to predict the effects of laser mode (continuous-wave vs. pulsed) and thermal conductivity of the supporting substrate on the local np-Au film temperatures during photothermal annealing. Based on these results we discuss the mechanisms by which the np-Au network is coarsened. Thermal transport simulations predict that continuous-wave mode laser irradiation of np-Au thin films on a silicon substrate supports the widest range of morphologies that can be created through photothermal annealing of np-Au. Using the guidance provided by simulations, we successfully fabricate an on-chip material library consisting of 81 np-Au samples of 9 different morphologies for use in the parallel study of structure-property relationships.

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