arXiv subjects

Abdullah Al Amin

Publications and source records attributed to Abdullah Al Amin.

4 recordsLinked to original sources

arXiv2026

Laser Powder Bed Fusion Melt Pool Dynamics for Different Geometric Variations and Powder Layer Heights: High-Fidelity Multiphysics Modeling vs 2025 NIST Experiments

Metal Laser Powder Bed Fusion (PBF-LB/M) is a leading additive manufacturing technique in which part quality and grain morphology are highly dependent on process parameters. Numerous studies of process variations, such as laser power, scan speed, and spot diameter, have demonstrated that they strongly influence melt pool dynamics; however, the effects of powder layer height and geometric variations remain less well understood. In this article, we focus on variations in powder layer height and part geometry to study their influence on melt pool dynamics. We employed a high-fidelity multiphysics simulation framework based on the open source finite volume method (FVM) solver package `LaserBeamFoam' built on `OpenFOAM' to study the variations in different melt pool metrics -- melt pool depth, width, bead height, overlap depth, overlap width, solidified area, and dilution area. The solver captures coupled phenomena of heat transfer, fluid flow, vaporization, recoil pressure, Marangoni convection, and realistic laser reflection behavior to accurately model the melt pool dynamics. Simulations are performed for different powder layer heights and geometric dimensions for direct comparison with benchmark experiments conducted at the National Institute of Standards and Technology (NIST) in 2025. Quantitative validation against NIST experiment demonstrates excellent agreement in all the melt pool metrics. These results highlight the predictive capability of physics-based PBF-LB models, paving the way for process optimization, defect mitigation, and the integration of simulation into digital twin frameworks for additive manufacturing.

physics.app-ph↗

arXiv2023

Statistical Parameterized Physics-Based Machine Learning Digital Twin Models for Laser Powder Bed Fusion Process

A digital twin (DT) is a virtual representation of physical process, products and/or systems that requires a high-fidelity computational model for continuous update through the integration of sensor data and user input. In the context of laser powder bed fusion (LPBF) additive manufacturing, a digital twin of the manufacturing process can offer predictions for the produced parts, diagnostics for manufacturing defects, as well as control capabilities. This paper introduces a parameterized physics-based digital twin (PPB-DT) for the statistical predictions of LPBF metal additive manufacturing process. We accomplish this by creating a high-fidelity computational model that accurately represents the melt pool phenomena and subsequently calibrating and validating it through controlled experiments. In PPB-DT, a mechanistic reduced-order method-driven stochastic calibration process is introduced, which enables the statistical predictions of the melt pool geometries and the identification of defects such as lack-of-fusion porosity and surface roughness, specifically for diagnostic applications. Leveraging data derived from this physics-based model and experiments, we have trained a machine learning-based digital twin (PPB-ML-DT) model for predicting, monitoring, and controlling melt pool geometries. These proposed digital twin models can be employed for predictions, control, optimization, and quality assurance within the LPBF process, ultimately expediting product development and certification in LPBF-based metal additive manufacturing.

cs.LG↗

arXiv2022

Stochastic additive manufacturing simulation: from experiment to surface roughness and porosity prediction

Deterministic computational modeling of laser powder bed fusion (LPBF) process fails to capture irregularities and roughness of the scan track, unless expensive powder-scale analysis is used. In this work we developed a stochastic computational modeling framework based on Markov Chain Monte Carlo (MCMC) capable of capturing the irregularities of LPBF scan. The model is calibrated against AFRL single track scan data using a specially designed tensor decomposition method, i.e., Higher-Order Proper Generalized Decomposition (HOPGD) that relies on non-intrusive data learning and construction of reduced order surrogate models. Once calibrated, the stochastic model can be used to predict the roughness and porosity at part scale at a significantly reduced computational cost compared to detailed powder-scale deterministic simulations. The stochastic simulation predictions are validated against AFRL multi-layer and multitrack experiments and reported as more accurate when compared with regular deterministic simulation results.

cs.CE↗

arXiv2020

Mechanical and Vibrational Characteristics of Functionally Graded Cu-Ni Nanowire: A Molecular Dynamics Study

Functionally graded material (FGM) is a class of advanced materials, consisting of two (or more) different constituents, that possesses a continuously varying composition profile. With the advancement of nanotechnology, applications of FGMs have shifted from their conventional usage towards sophisticated micro and nanoscale electronics and energy conversion devices. Therefore, the study of mechanical and vibrational properties of different FGM nanostructures is crucial in exploring their feasibility for different applications. In this study, for the first time, we employed molecular dynamics (MD) simulations to investigate the mechanical and vibrational properties of radially graded Cu-Ni FGM nanowires (NW). Distribution of Cu and Ni along the radial direction follows power-law, exponential and sigmoid functions for FGM NWs under consideration. Our results demonstrate that, distribution function parameters play an important role in modulating the mechanical (elastic modulus and ultimate tensile strength) and vibrational (natural frequency and quality factor) properties of FGM NWs. The study also suggests that, elastic moduli of FGM NWs can be predicted with relatively good accuracy using Tamura and Reuss micromechanical models, regardless of NW diameter. We found that, Euler-Bernoulli beam theory under-predicts the natural frequencies of FGM NWs, whereas He-Lilley model closely approximates the MD results. Interestingly, FGM NWs are always found to exhibit beat vibration because of their asymmetrical cross sections. Finally, this is the first atomistic scale study of FGMs that directly compares MD simulations with continuum theories and micromechanical models to understand the underlying mechanisms that govern the mechanical and vibrational properties of FGM NWs in nanoscale.

cond-mat.mtrl-sci↗