Posts by Collection

High-fidelity coupled CFD-FEM simulation on DED and LPBF

– corporation with ANSYS

Linked the high-fidelity molten pool dynamics model with the finite element solver for thermal stress simulation.

High-density dislocations in additively manufactured metals

– corporation with Shanghai Jiaotong University

Demonstrated that thermal stresses are the origin of high-density dislocations in additively manufactured metals using the coupled CFD-FEM simulation.

Physically informed data-driven prognostic model for the temperature prediction in AM

– corporation with Singapore A*STAR

High accuracy and significantly faster computation speed for the track-scale AM modeling based on the data-driven prediction (CNN, GPR, QR) of the isotherms and the isotherm-reconstructed temperature attribution on FEM model.

Pattern-oriented Strain Attribution in Part-scale Modeling of Additive Manufacturing

– corporation with Singapore A*STAR

Efficient large-scale AM modeling approaches with sound physical basis, where the layer-wise element progressive activation is applied with base strain pattern attributed onto the sample layers.

Hybrid Autonomous Manufacturing, Moving from Evolution to Revolution (HAMMER)

– corporation with The OHIO State University and so on.

High-fidelity English wheel simulation: distortion analysis of the sheet metal under rolling of two wheel of different size and curvature.

Micro-Casting of Titanium Alloy in Self-boiling Molds

Heat transfer analysis of liquid materials in multiple self-boiling molds for micro-casting.

DED on sheet metal forming

– corporation with Nissan

Deformation prediction using bulking analysis under the residual stress pattern achieved by track-scale DED simulation.

(AID4Greenest) - AI powered characterization and modelling for green steel technology

Funded by European Union

– corporation with Fraunhofer, IWM and IMDEA et al.

Fortran-based self-developed Lagamine coding platform for implementation of H. Morch visco-plasticity law & Development of the mean field creep model on Gitlab using Python.

The origin of dense dislocations in many additively manufactured metals remains a mystery. We here employed pure Cu as a prototype and fabricated the very challenging high-purity (>99.9%) bulk Cu by laser powder-bed-fusion (L-PBF) technique. We found that high-density dislocations were present in the as-built samples and these high-density dislocations were introduced on the fly during the L-PBF process. A newly developed multi-physics modeling was further employed to interpret the origin of these pre-existing dislocations, demonstrating that the compression-tension cycles rendered by the localized heating/cooling heterogeneity upon laser scanning are responsible for the residual high-density dislocations.

Doi:https://doi.org/10.1080/21663831.2020.1751739

The prediction of thermal stress and distortion is a prerequisite for high-quality additive manufacturing (AM). The widely applied thermo-mechanical model using the finite element method (FEM) leaves much to be improved due to their oversimplifications on material deposition, molten pool flow, etc. In this study, a high-fidelity modelling approach by linking the thermal-fluid (computational fluid dynamics, CFD) and mechanical models (named as CFD-FEM model) is developed to predict the thermal stress for AM taking into account the influences of thermal-fluid flow. Profiting from the precise temperature profiles and melt track geometry extracted from the thermal-fluid model as well as the remarkable flexibility of the quiet element method of FEM, this work aims at simulating the thermal stress distribution by involving physical changes in the AM process, e.g., melting and solidification of powder particles, molten pool evolution and inter-track inter-layer re-melting. Unlike the conventional thermo-mechanical analysis, in this approach, thermal stress calculation is purely based on a mechanical model where the thermal loads are applied by using a linear interpolation function to spatially and temporally map the temperature values from the thermal-fluid model’s cell centres into the FEM element nodes. With the proposed approach, the thermal stress evolution in the AM process of single track, multiple tracks and multiple layers are simulated, where the rough surfaces and internal voids can be well incorporated. Moreover, a conventional thermo-mechanical simulation of two tracks with predefined track geometry is conducted for cross comparison. Finally, the simulated thermal stress distribution can rationally explain the crack distribution observed in the experiments.

Doi:https://doi.org/10.1016/j.matdes.2020.109185

The advent of additive manufacturing (AM) offers the possibility of creating high-performance metallic materials with unique microstructure. Ultrafine dislocation cell structure in AM metals is believed to play a critical role in strengthening and hardening. However, its behavior is typically considered to be associated with alloying elements. Here we report that dislocations in AM metallic materials are self-stabilized even without the alloying effect. The heating–cooling cycles that are inherent to laser power-bed-fusion processes can stabilize dislocation network in situ by forming Lomer locks and a complex dislocation network. This unique dislocation assembly blocks and accumulates dislocations for strengthening and steady strain hardening, thereby rendering better material strength but several folds improvements in uniform tensile elongation compared to those made by traditional methods. The principles of dislocation manipulation and self-assembly are applicable to metals/alloys obtained by conventional routes in turn, through a simple post-cyclic deformation processing that mimics the micromechanics of AM. This work demonstrates the capability of AM to locally tune dislocation structures and achieve high-performance metallic materials.

Doi:https://doi.org/10.1016/j.mattod.2021.06.002

Selective laser melting is receiving increasing interest as an additive manufacturing technique. Residual stresses induced by the large temperature gradients and inhomogeneous cooling process can favour the generation of cracks. In this work, a crystal plasticity finite element model is developed to simulate the formation of residual stresses and to understand the correlation between plastic deformation, grain orientation and residual stresses in the additive manufacturing process. The temperature profile and grain structure from thermal-fluid flow and grain growth simulations are implemented into the crystal plasticity model. An element elimination and reactivation method is proposed to model the melting and solidification and to reinitialize state variables, such as the plastic deformation, in the reactivated elements. The accuracy of this method is judged against previous method based on the stiffness degradation of liquid regions by comparing the plastic deformation as a function of time induced by thermal stresses. The method is used to investigate residual stresses parallel and perpendicular to the laser scan direction, and the correlation with the maximum Schmid factor of the grains along those directions. The magnitude of the residual stress can be predicted as a function of the depth, grain orientation and position with respect to the molten pool. The simulation results are directly comparable to X-ray diffraction experiments and stress–strain curves.

Doi:https://doi.org/10.1007/s00466-021-02116-z

The process-structure–property relationship for additive manufacturing (AM) is typically derived starting from the temperature profile which can be achieved by the meso-scale thermal-fluid flow simulation with huge computational cost. We propose a data-driven prognostic approach with specialized physical constraints to rapidly predict the temperature profiles. The dataset is constructed from the physics-based thermal-fluid flow simulation results under different manufacturing parameters. The temperature field around the molten pool region is statistically characterized by the function parameters of the individual isotherms, which are essentially the output of the data-driven model based on the input manufacturing parameters, while the temperature field is reconstructed using the interpolation approach based on the predicted isotherms. The data-driven predicted temperature profiles are validated against those from the thermal-fluid flow simulations, and then further applied in the thermal stress and grain growth simulations, of which the results are compared with those using the temperature profile directly from thermal-fluid flow simulations. The results demonstrate that our data-driven approach is highly feasible in predicting the geometry features of the isotherms and temperature profiles around the molten pool regions.

Doi:https://doi.org/10.1016/j.cma.2022.114652

4D printing of metallic shape-morphing systems can be applied in many fields, including aerospace, smart manufacturing, naval equipment, and biomedical engineering. The existing forming materials for metallic 4D printing are still very limited except shape memory alloys. Herein, a 4D printing method to endow non-shape-memory metallic materials with active properties is presented, which could overcome the shape-forming limitation of traditional material processing technologies. The thermal stress spatial control of 316L stainless steel forming parts is achieved by programming the processing parameters during a laser powder bed fusion (LPBF) process. The printed parts can realize the shape changing of selected areas during or after forming process owing to stress release generated. It is demonstrated that complex metallic shape-morphing structures can be manufactured by this method. The principles of printing parameters programmed and thermal stress pre-set are also applicable to other thermoforming materials and additive manufacturing processes, which can expand not only the materials used for 4D printing but also the applications of 4D printing technologies.

Doi:https://doi.org/10.1002/advs.202206486

A bottleneck in Laser Powder Bed Fusion (L-PBF) metal additive manufacturing (AM) is the quality inconsistency of its products. To address this issue without costly experimentation, computational multi-physics modeling has been used, but the effectiveness is limited by parameter uncertainties and their interactions. We propose a full factorial design and variable selection approach for the analytics of main and interaction effects arising from material parameter uncertainties in multi-physics models. Data is collected from high-fidelity thermal-fluid simulations based on a 2-level full factorial design for 5 selected material parameters. Crucial physical phenomena of the L-PBF process are analyzed to extract physics-based domain knowledge, which are used to establish a validation checkpoint for our study. Initial data visualization with half-normal probability plots, interaction plots and standard deviation plots, is used to assess if the checkpoint is being met. We then apply the combination of best subset selection and the LASSO method on multiple linear regression models for comprehensive variable selection. Analytics yield statistically and phyiscally validated findings with practical implications, emphasizing the importance of parameter interactions under uncertainty, and their relation to the underlying physics of L-PBF.

Doi:https://doi.org/10.1038/s41524-023-01004-9

4D printing technologies are currently suffering from the inability to produce rapid motions, which limit their applications that require fast shape transformation such as rapid unlocking and deployment of aerospace equipment. Herein, inspired by the shooting mechanisms of Viola verecunda fruit for seed dispersal, the 4D-printed biomimetic catapult is developed. Based on the structure change characteristics of gradient fan-shaped cells of the fruit pods during seed ejection, the biomimetic smart catapult is processed via the programming of spatial distribution of heterogeneous materials with various storage modulus enabled by additive manufacturing. This catapult can achieve high-speed ejection with the logically stimuli of external force, temperature, light, humidity, or electricity. The proposed biomimetic 4D printing strategy has broken through the limitations in motion speed, which helps fully unleash the potential of 4D printing.

Doi:https://doi.org/10.1002/adfm.202301286

The defect formation is closely related to molten pool and keyhole features in metal additive manufacturing. Experimentation and physics-based simulation methods to capture the molten pool and keyhole features are expensive and time-consuming. A data-driven method is proposed in this work to efficiently predict the molten pool and keyhole features characterized by a series of fitting curves under given manufacturing parameters, instead of simply predicting the molten pool and keyhole sizes. The database consists of simulation cases with the high-fidelity thermal-fluid flow model. Molten pool melting regime, keyhole stability and keyhole type are recognized with the neural net pattern recognition. With the Gaussian process regression model, the keyhole dimensions are predicted and the keyhole contour is reconstructed. The comparison between predicted data and physics-based simulation results demonstrates the feasibility and accuracy of our data-driven model. Meanwhile, the predicted results can guide the selection of manufacturing parameters in actual production, and are also helpful to the further study of pores in additive manufacturing in academic research.

Doi:https://doi.org/10.1007/s10845-023-02157-6

At high temperature, metallic material creep is difficult to avoid. The understanding of the creep mechanisms helps metallurgists to design optimal alloy compositions and the prediction of a component life is a key input to manage safe maintenance and to define operational cost of any production line (solar plant parts, heat exchanger, any turbine parts loaded at high temperature…).

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The English wheel is a highly flexible traditional metalworking tool that allows skilled craftsmen to form compound curves on sheet metal panels. Historically, geometric accuracy and repeatability of formed panels using the English wheel have been tied to the operator leading to limited industrial adoption. This paper presents a novel framework for an integrated English wheeling system that leverages robot forming with a newly developed adaptable gripper/end-effector, metrology for deformed geometry tracking and tolerance measurements, integrated sensors for real-time forming force measurements and control, computational modeling for tracking pattern/toolpath generation, and virtual reality (VR) for seamless integration. Sample panels are formed using the integrated system revealing new insights on the forming forces during the process – highlighting why an integrated system is desirable. Concepts from the proposed framework can be applied to other robotic forming processes and its merit is discussed under current digital manufacturing and industry 4.0 literature.

Doi:https://doi.org/10.1016/j.jmsy.2024.04.022

In finite element models (FEMs), two- or three-dimensional Representative Volume Elements (RVEs) based on a statistical distribution of particles in a matrix can predict mechanical material properties. This article studies an alternative to 3D RVEs with a 2.5D RVE approach defined by a one-plane layer of 3D elements to model the material behavior. This 2.5D RVE relies on springs applied in the out-of-plane direction to constrain the two lateral deformations to be compatible, with the goal of achieving the isotropy of the studied material. The method is experimentally validated by the prediction of the tensile stress–strain curve of a bi-phasic microstructure of the AlSi10Mg alloy. Produced by additive manufacturing, the sample material becomes isotropic after friction stir processing post treatment. If a classical plane strain 2D RVE simulation is clearly too stiff compared to the experiment, the predictions of the stress–strain curves based on 2.5D RVE, 2D RVE with no transversal constraint (called 2D free RVE), and 3D RVE simulations are close to the experiments. The local stress fields within a 2.5D RVE present an interesting similarity with 3D RVE local fields, but differences with the 2D free RVE local results. Since a 2.5D RVE simplifies one spatial dimension, the simulations with this model are faster than the 3D RVE (factor 2580 in CPU or taking into account an optimal parallel computation, a factor 417 in real time). Such a discrepancy can affect the FEM2 multi-scale simulations or the time required to train a neural network, enhancing the interest in a 2.5D RVE model.

Doi:https://doi.org/10.3390/met14111244

Inconel 718 (IN718) is a polycrystalline nickel-based superalloy and one of the most widely used materials in the aerospace industry owing to its excellent mechanical performances at high temperatures, including creep resistance. Interest in additively manufactured components in aerospace is greatly increasing due to their ability to reduce material consumption, to manufacture complex parts, and to produce out-of-equilibrium microstructures, which can be beneficial for mechanical behavior. IN718’s properties are, however, very sensitive to microstructural features, which strongly depend on the manufacturing process and subsequent heat treatments. Additive manufacturing and, more specifically, Laser Powder Bed Fusion (LPBF) induces very high thermal gradients and anisotropic features due to its inherently directional nature, which largely defines the microstructure of the alloy. Hence, defining appropriate manufacturing parameters and heat treatments is critical to obtain appropriate mechanical behavior. This review aims to present the main microstructural features of IN718 produced by LPBF, the creep mechanisms taking place, the optimal microstructure for creep strength, and the most efficient heat treatments to yield such an optimized microstructure.

Doi:https://doi.org/10.3390/ma18020276

Part-scale modeling of the temperature field in metal powder bed additive manufacturing (AM) is critical for predicting mechanical properties of the AM-ed parts. Track-by-track heat transfer analysis is impractical due to the extensive number of layers and the intricate design of scan strategies for the heat source, particularly in the fabrication of specimen clusters or parts with complex geometry, where multiple regions in the powder bed are manufactured simultaneously. Many part-scale modeling approaches only focus on the thermal behavior of a single part without considering the thermal interaction from the surrounding parts to reduce computational cost. However, experimental observations have revealed that the temperature distribution along the building direction can vary among samples with identical local geometries. This discrepancy can be attributed to the heating effects from neighboring samples. In this study, we propose an integrated part-scale modeling framework that combines layer-wise equivalent heat flux attribution with layer-wise element activation. Before the layer-wise attribution, we justify the equivalent heat flux of individual layers through high-fidelity track-scale simulations. Unlike traditional heat transfer analysis for single parts, our analysis incorporates heat conduction effects through the powder bed between different fusion zones. The temperature data obtained from each equivalent layer using our approach shows consistency when compared to the experimental observations. This research presents an efficient, physically grounded method for modeling the thermal behavior of large AM specimen clusters, enhancing our understanding of temperature field evolution in AM and supporting the design of optimized scanning path strategies for large samples.

Doi:https://doi.org/10.1016/j.jmapro.2024.12.057

Additive manufacturing (AM) of high-strength metallic alloys frequently encounters detrimental distortion and cracking, attributed to the accumulation of thermal stresses. These issues significantly impede the practical application of as-printed components. This study examines the Mg-15Gd-1Zn-0.4Zr (GZ151K, wt.%) alloy, a prototypical high-strength casting Mg-RE alloy, fabricated through laser powder bed fusion (LPBF). Despite achieving ultra-high strength, the GZ151K alloy concurrently exhibits a pronounced cold-cracking susceptibility. The as-printed GZ151K alloy consists of almost fully fine equiaxed grains with an average grain size of merely 2.87 µm. Subsequent direct aging (T5) heat treatment induces the formation of dense prismatic β’ precipitates. Consequently, the LPBF-T5 GZ151K alloy manifests an ultra-high yield strength of 405 MPa, surpassing all previously reported yield strengths for Mg alloys fabricated via LPBF and even exceeding that of its extrusion-T5 counterpart. Interestingly, as-printed GZ151K samples with a build height of 2 mm exhibit no cracking, whereas samples with build heights ranging from 4 to 18 mm demonstrate severe cold cracking. Thermal stress simulation also suggests that the cold cracking susceptibility increases significantly with increasing build height. The combination of high thermal stress and low ductility in the as-printed GZ151K alloy culminates in a high cold cracking susceptibility. This study offers novel insights into the intricate issue of cold cracking in the LPBF process of high-strength Mg alloys, highlighting the critical balance between achieving high strength and mitigating cold cracking susceptibility.

Doi:https://doi.org/10.1016/j.jmst.2024.07.005

While incremental forming processes can inexpensively create complex geometries from sheet metal, they struggle with adding sharp out of plane features for stiffness enhancement. With the implementation of directed-energy deposition (DED), an additive manufacturing process that locally deposits metal onto metallic substrates, reinforcement structures can be formed on the sheet metal. Furthermore, a design engineer may take advantage of the high residual stresses of DED to directly alter shapes in the substrate metal sheet. This hybrid forming-deposition process, as well as the application of local reinforcement, requires a good understanding of the process mechanism to predict expected shapes and minimize undesired deformations. In this work, numerical approaches are applied to evaluate heat transfer, thermal stress, and buckling of thin sheets under the stresses of deposition. These results are compared to analogous experiments conducted on an open-architecture laser-powder DED machine. The results of the thermal-mechanical analysis resemble the deformation trends observed in the experiments. However, the small-displacement formulation in the simulation used for ease of convergence does not fully capture the magnitude of the observed deformations. Nevertheless, the simulations effectively illustrate the effect of different scan strategies on the final deformed shape of the sheet metal.

Doi:https://doi.org/10.1016/j.jmapro.2025.03.120

Free vibration characteristics analysis of rectangular plate with central opening used in arbitrary boundary conditions

More information here

High-fidelity Modelling of Thermal Stress for Additive Manufacturing by Linking Thermal-fluid and Mechanical Models

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Data-driven prognostic model for temperature field in additive manufacturing based on the high-fidelity thermal-fluid flow simulation (slides below)

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Enhanced Large-Scale Modeling of Additive Manufacturing: Layer-wise Equivalent Heat Flux Attribution for Thermal Interaction Analysis across Multiple Fabrications (slides below)

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2018-2022, Department of Mechanical Engineering, National University of Singapore:

-ME1102 - Engineering Principles and Practice I

-ME2112 - Strength of Materials

-ME2115 - Mechanics of Machines: Vibration Lab

2018-2022, Department of Mechanical Engineering, National University of Singapore:

-Paper, software and coding supervision assistance for NUS Ph.D, Master and undergraduate students

2022-2023, Department of Mechanical Engineering, Northwestern University, USA:

-Software and coding supervision assistance for Ph.D students