Acceleration strategies for explicit finite element analysis of metal powder-based additive manufacturing processes using graphical processing units

Mozaffar, Mojtaba; Ndip-Agbor, Ebot; Lin, Stephen; Wagner, Gregory J.; Ehmann, Kornel F.; Cao, Jian

doi:10.1007/s00466-019-01685-4

Cited by 34 publications

(12 citation statements)

References 41 publications

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“…An attractive alternative to addressing this challenge is through numerical modeling and predictive simulations. Grid-based numerical methods, such as the finite element method (FEM), [20][21][22] finite volume method (FVM), [23][24][25][26][27][28][29] arbitrary Lagrangian-Eulerian method (ALE), [30][31][32] and the lattice Boltzmann method (LBM), [33][34][35][36] are popular numerical solutions for modeling metal PBF AM processes. 1 Most of the simulations consider the powder bed as a continuous media and predict the temperature distribution and residual stresses of the printed material.…”

Section: Introductionmentioning

confidence: 99%

“…1 Most of the simulations consider the powder bed as a continuous media and predict the temperature distribution and residual stresses of the printed material. 20,21,25,[37][38][39][40][41] Yuan and Gu, 25 for example, performed finite volume simulations of PBF-AM to study the effect of Marangoni convection on heat and mass transfer. Their results show that the thermodynamic flow is symmetric along the melt track and does not exhibit fluctuations due to a randomly packed powder particles.…”

Section: Introductionmentioning

confidence: 99%

“…Grid‐based numerical methods, such as the finite element method (FEM), 20‐22 finite volume method (FVM), 23‐29 arbitrary Lagrangian‐Eulerian method (ALE), 30‐32 and the lattice Boltzmann method (LBM), 33‐36 are popular numerical solutions for modeling metal PBF AM processes 1 . Most of the simulations consider the powder bed as a continuous media and predict the temperature distribution and residual stresses of the printed material 20,21,25,37‐41 . Yuan and Gu, 25 for example, performed finite volume simulations of PBF‐AM to study the effect of Marangoni convection on heat and mass transfer.…”

Section: Introductionmentioning

confidence: 99%

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A Lagrangian meshfree mesoscale simulation of powder bed fusion additive manufacturing of metals

Fan

Wang

Huang

et al. 2020

Numerical Meth Engineering

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We present a powder-scale computational framework to predict the microstructure evolution of metals in powder bed fusion additive manufacturing (PBF AM) processes based on the hot optimal transportation meshfree (HOTM) method. The powder bed is modeled as discrete and deformable three-dimensional bodies by integrating statistic information from experiments, including particle size and shape, and powder packing density. Tractions in Lagrangian framework are developed to model the recoil pressure and surface tension. The laser beam is applied to surfaces of particles and substrate dynamically as a heat flux with user-specified beam size, power, scanning speed, and path. The linear momentum and energy conservation equations are formulated in the Lagrangian configuration and solved simultaneously in a monolithic way by the HOTM method to predict the deformation, temperature, contact mechanisms, and fluid-structure interactions in the powder bed. The numerical results are validated against single track experiments. Various powder bed configurations, laser powers, and speed are investigated to understand the influence of dynamic contact and inelastic material behavior on the deformation, heat transfer, and phase transition of the powder bed. The formation of defects in the microstructure of 3D printed metals, including pores, partially, and unmelted particles, is predicted by the proposed computational scheme.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Lagrangian meshfree mesoscale simulation of powder bed fusion additive manufacturing of metals

Fan

Wang

Huang

et al. 2020

Numerical Meth Engineering

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“…In this work, we use Generalized Analysis for Multiscale Multi-Physics Application (GAMMA) [7], [8], a FEM based method for developing the database to train our model-based control system. GAMMA is used to solve the time-dependent heat equation and simulate the manufacturing DMD process at the part scale.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Iterative Machine Learning Approach for Temperature Profile Prediction in Additive Manufacturing Processes

Paul¹,

Mozaffar²,

Yang³

et al. 2019

2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from a computeraided design model by successively adding material layer by layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional parts with complex geometries using processes such as laser metal deposition that would be difficult to create using traditional machining. As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run. However, as the simulations are computationally costly and timeconsuming for predicting multiscale multi-physics phenomena in AM, physics-informed data-driven machine-learning systems for predicting the behavior of AM processes are immensely beneficial. Such models accelerate not only multiscale simulation tools but also empower real-time control systems using in-situ data. In this paper, we design and develop essential components of a scientific framework for developing a data-driven model-based real-time control system. Finite element methods are employed for solving time-dependent heat equations and developing the database. The proposed framework uses extremely randomized trees -an ensemble of bagged decision trees as the regression algorithm iteratively using temperatures of prior voxels and laser information as inputs to predict temperatures of subsequent voxels. The models achieve mean absolute percentage errors below 1% for predicting temperature profiles for AM processes. The code is made available for the research community at https://github.com/paularindam/ml-iter-additive.

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“…Each of these global matrix and vectors can be computed by assembling the local contributions of each element to them individually. Interested readers can find a more in-depth derivation of the finite element formulation in[Mozaffar et al, 2019]. To simulate AM processes, we need to dynamically keep track of active elements and surfaces at each time step and the local contributions of only active objects will be assembled in global matrices.…”

mentioning

confidence: 99%

Additive manufacturing process design with differentiable simulations

Mozaffar,

Cao

2021

Preprint

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We present a novel computational paradigm for process design in manufacturing processes that incorporates simulation responses to optimize manufacturing process parameters in high-dimensional temporal and spatial design spaces. We developed a differentiable finite element analysis framework using automatic differentiation which allows accurate optimization of challenging process parameters such as time-series laser power. We demonstrate the capability of our proposed method through three illustrative case studies in additive manufacturing for: (i) material and process parameter inference using partial observable data, (ii) controlling time-series thermal behavior, and (iii) stabilizing melt pool depth. This research opens new avenues for high-dimensional manufacturing design using solid mechanics simulation tools such as finite element methods. Our codes are made publicly available for the research community at https://github.com/mojtabamozaffar/differentiable-simulation-am.

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Acceleration strategies for explicit finite element analysis of metal powder-based additive manufacturing processes using graphical processing units

Cited by 34 publications

References 41 publications

A Lagrangian meshfree mesoscale simulation of powder bed fusion additive manufacturing of metals

A Lagrangian meshfree mesoscale simulation of powder bed fusion additive manufacturing of metals

A Real-Time Iterative Machine Learning Approach for Temperature Profile Prediction in Additive Manufacturing Processes

Additive manufacturing process design with differentiable simulations

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