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Layer-to-layer height measurements of additively manufactured 316L stainless steel using high speed spectral-domain optical coherence tomography (SD-OCT) are presented. Layers are built up using an open architecture laser powder bed fusion machine while height measurements are made in-line along the process laser path following each layer print. Printed cubes, with and without an internal ‘overhang’ channel, were built to investigate the effect of scanning parameters on surface structure. Layer-to-layer scan rotation strategy significantly impacts surface roughness between layers which in turn can influence porosity. Spatter particles, which have been correlated with numerous defect modalities, generate high points in the powder bed and can persist on a melted surface for many layers. Laser power significantly affects overhang morphology, as measured by SD-OCT. Large dross occurs in the high energy density regime, while balling and a capillary-driven coalescence of unstable melt pools perpendicular to the scanning direction occurs in the low energy density regime. High fidelity powder-scale simulations of deep powder layers were used to further elucidate the underlying physics revealed by SD-OCT measurements and high speed imaging, yielding insight to defect formation mechanisms which can lead to improved process parameters. [ABSTRACT FROM AUTHOR]

Detailed understanding of the complex melt pool physics plays a vital role in predicting optimal processing regimes in laser powder bed fusion additive manufacturing. In this work, we use high framerate video recording of Selective Laser Melting (SLM) to provide useful insight on the laser-powder interaction and melt pool evolution of 316 L powder layers, while also serving as a novel instrument to quantify cooling rates of the melt pool. The experiment was performed using two powder types – one gas- and one water-atomized – to further clarify how morphological and chemical differences between these two feedstock materials influence the laser melting process. Finally, experimentally determined cooling rates are compared with values obtained through computer simulation, and the relationship between cooling rate and grain cell size is compared with data previously published in the literature. [ABSTRACT FROM AUTHOR]

Understanding laser interaction with metal powder beds is critical in predicting optimum processing regimes in laser powder bed fusion additive manufacturing of metals. In this work, we study the denudation of metal powders that is observed near the laser scan path as a function of laser parameters and ambient gas pressure. We show that the observed depletion of metal powder particles in the zone immediately surrounding the solidified track is due to a competition between outward metal vapor flux directed away from the laser spot and entrainment of powder particles in a shear flow of gas driven by a metal vapor jet at the melt track. Between atmospheric pressure and ∼10 Torr of Ar gas, the denuded zone width increases with decreasing ambient gas pressure and is dominated by entrainment from inward gas flow. The denuded zone then decreases from 10 to 2.2 Torr reaching a minimum before increasing again from 2.2 to 0.5 Torr where metal vapor flux and expansion from the melt pool dominates. The dynamics of the denudation process were captured using high-speed imaging, revealing that the particle movement is a complex interplay among melt pool geometry, metal vapor flow, and ambient gas pressure. The experimental results are rationalized through finite element simulations of the melt track formation and resulting vapor flow patterns. The results presented here represent new insights to denudation and melt track formation that can be important for the prediction and minimization of void defects and surface roughness in additively manufactured metal components. [ABSTRACT FROM AUTHOR]

The results of detailed experiments and high fidelity modeling of melt pool dynamics, droplet ejections and hole drilling produced by periodic modulation of laser intensity are presented. Ultra-high speed imaging revealed that melt pool oscillations can drive large removal of material when excited at the natural oscillation frequency. The physics of capillary surface wave excitation is discussed and simulation is provided to elucidate the experimental results. The removal rates and drill through times as a function of driving frequency is investigated. The resonant removal mechanism is driven by both recoil momentum and thermocapillary force but the key observation is the latter effect does not require evaporation of material, which can significantly enhance the efficiency for laser drilling process. We compared the drilling of holes through a 2 mm-thick Al plate at modulation frequencies up to 20 kHz. At the optimal frequency of 8 kHz, near the resonant response of the melt pool, the drilling efficiency is greater than 10x with aspect ratio of 12:1, and without the collateral damage that is observed in unmodulated CW drilling. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We present a machine learning workflow to discover signatures in acoustic measurements that can be utilized to create a low-dimensional model to accurately predict the location of keyhole pores formed during additive manufacturing processes. Acoustic measurements were sampled at 100 kHz during single-layer laser powder bed fusion (LPBF) experiments, and spatio-temporal registration of pore locations was obtained from post-build radiography. Power spectral density (PSD) estimates of the acoustic data were then decomposed using non-negative matrix factorization with custom k -means clustering (NMF k ) to learn the underlying spectral patterns associated with pore formation. NMF k returned a library of basis signals and matching coefficients to blindly construct a feature space based on the PSD estimates in an optimized fashion. Moreover, the NMF k decomposition led to the development of computationally inexpensive machine learning models which are capable of quickly and accurately identifying pore formation with classification accuracy of supervised and unsupervised label learning greater than 95% and 90%, respectively. The intrinsic data compression of NMFk, the relatively light computational cost of the machine learning workflow, and the high classification accuracy makes the proposed workflow an attractive candidate for edge computing toward in-situ keyhole pore prediction in LPBF. [ABSTRACT FROM AUTHOR]

A semi-analytical model for deterministic surface smoothing of fused silica below the evaporation temperature threshold is presented. Using this model, a method for calculating deterministic track depths for laser polishing fused silica is derived and validated with experiments. Model predictions were in good agreement with experiment across varying laser preheating temperature and power. With the semi-analytical method presented here, deterministic laser smoothing can be achieved in the densification regime to close the process loop of laser polishing. [ABSTRACT FROM AUTHOR]

This study investigates the physical processes involved in picosecond pulse (20-28 ps FWHM) laser ablation of Al 6061, 316L stainless steel, and undoped crystalline Si (〈100〉) over a range of laser wavelength (355 nm and 1064 nm) and fluence (0.1-40 J/cm2). Experimental measurements of material ablation rate show enhanced removal at the 355 nm wavelength, primarily due to laser-plasma interaction (LPI) within the ablative plume that approaches an order of magnitude increase over the measured removal at 1064 nm. A transition in the ablation rate at 355 nm is identified around ∼10 J/cm2 above which the removal efficiency increases by a factor of two to three. Multi-physics radiation hydrodynamic simulations, considering LPI effects and utilizing a novel mixed-phase equation of state model, show that the transition in ablation efficiency is due to the onset of melt ejection through cavitation, where laser-driven shock heating sets the depth of melt penetration and the ensuing release wave from the ablation surface drives cavitation through the imposition of tensile strain within the melt. High-speed pump-probe imaging of the ejecta and ejecta collection studies, as well as scanning electron microscopy of the ablation craters, support the proposed cavitation mechanism in the higher fluence range. The ablation process is critically influenced by LPI effects and the thermophysical properties of the material. [ABSTRACT FROM AUTHOR]

The article discusses the use of Bessel beams in metal additive manufacturing, which provides better control over the spatiotemporal evolution of the melt pool compared to traditional Gaussian beams. Bessel beams reduce issues like porosity, keyhole formation, and spatter generation during the 3D printing process, resulting in 3D-printed structures with improved density, reduced surface roughness, and better mechanical properties.

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. [ABSTRACT FROM AUTHOR]

We describe a damage testing system and its use in investigating laser-induced optical damage initiated by both intrinsic and extrinsic precursors on multilayer dielectric coatings suitable for use in high-energy, large-aperture petawatt-class lasers. We employ small-area damage test methodologies to evaluate the intrinsic damage resistance of various coatings as a function of deposition methods and coating materials under simulated use conditions. In addition, we demonstrate that damage initiation by raster scanning at lower fluences and growth threshold testing are required to probe the density of extrinsic defects, which will limit large-aperture optics performance. [ABSTRACT FROM AUTHOR]

In-situ process monitoring as an aid to part qualification for laser powder-bed fusion (L -PBF) technology is a topic of increasing interest to the additive manufacturing community. In this work, airborne acoustic and inline pyrometry measurements were recorded simultaneously with the laser position. X-ray radiography imaging was used to spatio-temporally register keyhole pore locations to the pyrometry and acoustic signals, enabling binary labeling of data partitions based on pore formation. These labeled partitions were subsequently featurized using a highly comparative time-series analysis toolbox. Acoustic data was found to be much more effective than the pyrometry data for keyhole pore identification. However, when the information contained in both sensing modalities was combined in a sensor fusion strategy, the error rates of the top performing models were significantly reduced. [ABSTRACT FROM AUTHOR]

Gaussian laser intensity profiles are standard in laser-based metal additive manufacturing, although recent work in single-layer melt tracks showed that beam shaping could offer a feasible route towards microstructural control. Since thermal cycling and grain orientation templating in multilayer builds can alter microstructures, we compare three-dimensional 316 L stainless steel cubes built using Gaussian and elliptical laser intensity profiles. Microstructural characterization confirms that elliptical beams result in a modified and improved microstructure compared to Gaussian beams. This assessment favoring the elliptical beam is based on: (1) the observed refinement of the columnar and equiaxed grains; (2) more importantly, the volume fraction occupied by equiaxed grains increases dramatically such that the average grain area is reduced by nearly 50%; (3) reduced texture in cubes built using an elliptical beam. The random orientation of small equiaxed grains in samples built using an elliptical beam also suggests a higher nucleation frequency. High-fidelity finite element simulations that deliver accurate thermal profiles by incorporating laser ray tracing and fluid dynamics were performed. Using a time-dependent solidification map based on local thermal gradients (G) and growth rates (R), our simulation results confirm the experimentally observed trend that an elliptical beam results in a favorable thermal profile. Unlabelled Image • Starkly different 316 L microstructures were built by laser AM using circular Gaussian and elliptical laser beams • With a higher area fraction occupied by equiaxed grains, the average grain area decreased by ~50% using the elliptical beam • Randomly oriented small grains formed using the elliptical beam suggest a higher nucleation frequency • Overall crystallographic texture was reduced using the elliptical beam [ABSTRACT FROM AUTHOR]