arXiv subjects

Anthony D. Rollett

Publications and source records attributed to Anthony D. Rollett.

8 recordsLinked to original sources

arXiv2026

Aeroacoustic signatures reveal fast transient dynamics of vapor-jet-driven cavity oscillations in metallic additive manufacturing

Aeroacoustic emissions from intense evaporation are widely measured yet often treated as noisy byproducts and used mainly in empirical monitoring. Here, we show that airborne sound encodes physics-governed sub-millisecond fingerprints of vapor-jet dynamics in excessive vaporization, exemplified by vapor keyholes in laser metal processing. From first principles, we develop a vapor-jet-cavity oscillation framework and incorporate it into an aeroacoustic formulation, thereby coupling measured sound to transient cavity depth and oscillation frequency. Reconciled with synchronized multimodal in-situ data, airborne acoustics enable accurate tracking of vapor-cavity properties within tens to hundreds of microseconds. Combined with newly discovered correlations, cavity-jet-acoustic theory recasts the transition from steady, pore-free to pore-shedding vaporizations as a critical-frequency event. Aeroacoustic emissions thus become scalable, physics-guided, and cost-efficient probes of rapidly evolving liquid-vapor systems.

physics.app-ph↗

arXiv2025

Airborne acoustic emission enables sub-scanline keyhole porosity quantification and effective process characterization for metallic laser powder bed fusion

Keyhole-induced (KH) porosity, which arises from unstable vapor cavity dynamics under excessive laser energy input, remains a significant challenge in laser powder bed fusion (LPBF). This study presents an integrated experimental and data-driven framework using airborne acoustic emission (AE) to achieve high-resolution quantification of KH porosity. Experiments conducted on an LPBF system involved in situ acquisition of airborne AE and ex situ porosity imaging via X-ray computed tomography (XCT), synchronized spatiotemporally through photodiode signals with submillisecond precision. We introduce KHLineNum, a spatially resolved porosity metric defined as the number of KH pores per unit scan length, which serves as a physically meaningful indicator of the severity of KH porosity in geometries and scanning strategies. Using AE scalogram data and scan speed, we trained a lightweight convolutional neural network to predict KHLineNum with millisecond-scale temporal resolution, achieving an R-squared value exceeding 0.8. Subsequent analysis identified the 35-45 kHz frequency band of AE as particularly informative, consistent with known KH oscillations. Beyond defect quantification, the framework also enables AE-driven direct inference of KH regime boundaries on the power-velocity process map, offering a noninvasive and scalable component to labor-intensive post-process techniques such as XCT. We believe this framework advances AE-based monitoring in LPBF, providing a pathway toward improved quantifiable defect detection and process control.

physics.app-ph↗

arXiv2025

Accurate and efficient predictions of keyhole dynamics in laser materials processing using machine learning-aided simulations

The keyhole phenomenon has been widely observed in laser materials processing, including laser welding, remelting, cladding, drilling, and additive manufacturing. Keyhole-induced defects, primarily pores, dramatically affect the performance of final products, impeding the broad use of these laser-based technologies. The formation of these pores is typically associated with the dynamic behavior of the keyhole. So far, the accurate characterization and prediction of keyhole features, particularly keyhole depth, as a function of time, has been a challenging task. In situ characterization of keyhole dynamic behavior using the synchrotron X-ray technique is informative but complicated and expensive. Current simulations are generally hindered by their poor accuracy and generalization abilities in predicting keyhole depths due to the lack of accurate laser absorptance data. In this study, we develop a machine learning-aided simulation method that accurately predicts keyhole dynamics, especially in keyhole depth fluctuations, over a wide range of processing parameters. In two case studies involving titanium and aluminum alloys, we achieve keyhole depth prediction with a mean absolute percentage error of 10%, surpassing those simulated using the ray-tracing method with an error margin of 30%, while also reducing computational time. This exceptional fidelity and efficiency empower our model to serve as a cost-effective alternative to synchrotron experiments. Our machine learning-aided simulation method is affordable and readily deployable for a large variety of materials, opening new doors to eliminate or reduce defects for a wide range of laser materials processing techniques.

cond-mat.mtrl-sci↗

arXiv2025

Equilibrium Conserving Neural Operators for Super-Resolution Learning

Neural surrogate solvers can estimate solutions to partial differential equations in physical problems more efficiently than standard numerical methods, but require extensive high-resolution training data. In this paper, we break this limitation; we introduce a framework for super-resolution learning in solid mechanics problems. Our approach allows one to train a high-resolution neural network using only low-resolution data. Our Equilibrium Conserving Operator (ECO) architecture embeds known physics directly into the network to make up for missing high-resolution information during training. We evaluate this ECO-based super-resolution framework that strongly enforces conservation-laws in the predicted solutions on two working examples: embedded pores in a homogenized matrix and randomly textured polycrystalline materials. ECO eliminates the reliance on high-fidelity data and reduces the upfront cost of data collection by two orders of magnitude, offering a robust pathway for resource-efficient surrogate modeling in materials modeling. ECO is readily generalizable to other physics-based problems.

cs.LG↗

arXiv2023

Inference of highly time-resolved melt pool visual characteristics and spatially-dependent lack-of-fusion defects in laser powder bed fusion using acoustic and thermal emission data

With a growing demand for high-quality fabrication, the interest in real-time process and defect monitoring of laser powder bed fusion (LPBF) has increased, leading manufacturers to incorporate a variety of online sensing methods including acoustic sensing, photodiode sensing, and high-speed imaging. However, real-time acquisition of high-resolution melt pool images in particular remains computationally demanding in practice due to the high variability of melt pool morphologies and the limitation of data caching and transfer, making it challenging to detect the local lack-of-fusion (LOF) defect occurrences. In this work, we propose a new acoustic and thermal information-based monitoring method that can robustly infer critical LPBF melt pool morphological features in image forms and detect spatially-dependent LOF defects within a short period. We utilize wavelet scalogram matrices of acoustic and photodiode clip data to identify and predict highly time-resolved (within a 1.0 ms window) visual melt pool characteristics via a well-trained data-driven pipeline. With merely the acoustic and photodiode-collected thermal emission data as the input, the proposed pipeline enables data-driven inference and tracking of highly variable melt pool visual characteristics with $R^2=0.8$. We subsequently validate our proposed approach to infer local LOF defects between two adjacent scanlines, showing that our proposed approach can outperform our selected baseline theoretical model based on previous literature. Revealing the physical correlation between airborne acoustic emission, thermal emission, and melt pool morphology, our work demonstrates the feasibility of creating an efficient and cost-effective acoustic- and thermal-based approach to facilitate online visual melt pool characterization and LOF defect detection.

physics.app-ph↗

arXiv2020

Three-Dimensional In Situ Texture Development and Plasticity Accumulation in the Cyclic Loading of an alpha-Ti Alloy

High-energy synchrotron x-rays are used to track grain rotations and the micromechanical evolution of a hexagonal Ti-7Al microstructure as it is cyclically loaded below its macroscopic yield stress. The evolution of the grains through 200 cycles reveals a continual change in von Mises stress and orientation across the entire specimen indicating a slow accumulation of plasticity even though the sample was cycled below its macroscopic yield. Grain reorientation is consistent with the development of a tension texture despite the negligible magnitude of (macroscopic) plastic strain. It is observed that grains in a "hard" orientation (c-axis close to the loading direction) retain more residual stress on their slip systems upon unloading than the grains in a "soft" orientation.

cond-mat.mtrl-sci↗

arXiv2020

Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials

Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-based generation algorithm (DREAM.3D). Importantly, simulations of electrochemical performance, using a locally resolved finite element model, demonstrate that the GAN generated microstructures closely match the performance distribution of the original, while DREAM.3D leads to significant differences. The ability of the generative machine learning model to recreate microstructures with high fidelity suggests that the essence of complex microstructures may be captured and represented in a compact and manipulatable form.

eess.IV↗

arXiv2020

Grain-Resolved Temperature-Dependent Anisotropy in Hexagonal Ti-7Al Revealed by Synchrotron X-Ray Diffraction

Hexagonal metals have anisotropic coefficients of thermal expansion causing grain-level internal stresses during heating. High energy x-ray diffraction microscopy, a non-destructive, in situ, micromechanical and microstructural characterization technique, has been used to determine the anisotropic coefficients of thermal expansion (CTEs) for Ti-7Al. Two samples of polycrystalline $α$-phase Ti-7Al were continuously heated from room temperature to 850 $^\circ$C while far-field HEDM scans were collected. The results showed a change in the ratio of the CTEs in the 'a' and 'c' directions which explains discrepancies found in the literature. The CTE additionally appears to be affected by the dissolution of $α_2$ precipitates. Analysis of the grain-resolved micromechanical data also shows reconfiguration of the grain scale stresses likely due to anisotropic expansion driving crystallographic slip.

cond-mat.mtrl-sci↗