4th World Congress on Integrated Computational Materials Engineering (ICME 2017): Additive Manufacturing - I
Program Organizers: Paul Mason, Thermo-Calc Software Inc.; Michele Manuel, University of Florida; Alejandro Strachan, Purdue University; Ryan Glamm, Boeing Research and Technology; Georg J. Schmitz, Micress/Aachen; Amarendra Singh, IIT Kanpur; Charles Fisher, Naval Surface Warfare Center
Monday 10:30 AM
May 22, 2017
Room: Salon II, III
Location: Ann Arbor Marriott Ypsilanti at Eagle Crest
Prediction of Microstructure, Residual Stress, and Deformation in Laser Powder Bed Fusion Process: Yu-Ping Yang1; Mahdi Jamshidinia1; Paul Boulware1; 1EWI
A transient thermal-metallurgical-mechanical analysis method has been developed to predict temperature, microstructure, hardness, stress, strain, and deformation for Laser powder bed fusion (L-PBF). The analysis method includes a pre-processing module, a powder deposition module, a thermal module, a metallurgical module, and a mechanical module. The pre-processing module is used to slice a solid geometry into layers and create laser heat lines for each layer based on a designed scan pattern. The power deposition module is used to model powder-to-solid transition by changing material properties based on the laser’s locations. The thermal module includes two heat-source models, a Goldak double-ellipsoidal model and a moving line heating model, which work with ABAQUS software to predict temperature by inputting laser power, travel speed, and a heat-line sequence. The metallurgical module is used to predict microstructure and hardness by inputting the predicted temperature history with the Goldak heat-source model. The mechanical module is used to predict stress and deformation by inputting the predicting temperature history with the line heat source model and modeling the melting effects. Microstructure and hardness of AISI 4140 steel built with L-PBF were predicted using the developed numerical modeling tool. Experimental measured hardness was used to validate the model prediction. It was found that tempering effect has to be modeled in order to predict the hardness correctly. Residual stress and deformation of Inconel 718 were predicted in a block sample built by L-PBF.
Enabling Component-scale Prediction in Additive Manufacturing by Integration of Physics-based and Data-driven Modeling: Jingran Li1; Ran Jin1; Hang Yu1; 1Virginia Tech
Additive manufacturing of materials often involves complex physical processes, such as light absorption and powder melting, dynamic melt flow, and rapid solidification, resulting in complex co-evolution of the thermal field, microstructure, and residual stresses. Owing to the multi-scale, multi-physics, and non-equilibrium nature of additive manufacturing, property prediction across the component has been challenging. For example, strategies remain elusive for identifying model discrepancies and unknown physical parameters, for reducing computation cost, and for quantifying modeling uncertainties. Here, we present a theoretical and experimental framework for component-scale prediction and uncertainty quantification, in which physics-based and data-driven models are integrated, and are validated by in situ thermal imaging and ex situ dimension measurement. 3D finite element modeling of the thermo-mechanical process captures the underlying physics; supervised machine learning-based surrogate models accelerate the computation speed; Bayesian calibration quantifies model discrepancies and parameter uncertainties. This framework enables property prediction on the component scale with different geometric designs, under different processing conditions, and at a low computation cost. These will be demonstrated in processes such as fused deposition modeling and directed energy deposition.
Transforming Additive Manufacturing through Exascale Simulation: James Belak1; Wayne King1; John Turner2; Lyle Levine3; Neil Henson4; Neil Carlson4; 1Lawrence Livermore National Laboratory; 2Oak Ridge National Laboratory; 3National Institute of Standards and Technology; 4Los Alamos National Laboratory
Additive manufacturing (AM) offers the prospect of revolutionizing the fabrication of components and parts with unique properties. Despite this enormous potential, the insertion of AM parts has been limited due to the difficulty in qualifying parts. Starting from a top-> down systems engineering approach, an integrated computational materials engineering (ICME) approach can be used to accelerate the qualification and adoption of newly additively manufactured parts by enabling up-front assessment of manufacturability and performance. Recently, we were awarded one of the USDOE Exascale Computing Project’s Application Development Centers to create a new modeling framework for additive manufacturing amenable to exascale computing. The project includes an integration of all the computational components of the AM process into a coupled-exascale modeling framework, where each simulation component itself is an exascale calculation. In order to expose the physics fidelity needed to enable part qualification, the project is driven by a series of demonstration problems that are amenable to experimental observation and validation. In particular, in situ experiments on instrumented AM machines will be used to measure local material properties during build that give rise to macroscopic properties such as residual stress. Here, we present our coupled-exascale simulation framework for additive manufacturing and its initial application to AM builds.Work was performed under the auspices of the U.S. Department of Energy by LLNL, ORNL and LANL under contract DEAC5207NA27344.
ICME Modules to Account for and Exploit Additive Manufacturing Process-Structure-Property Relationships in Component Design: Michael Groeber1; Edwin Schwalbach2; Michael Uchic2; Paul Shade2; Bill Musinski2; Sean Donegan2; Daniel Sparkman2; Jonathan Miller2; 1AFRL; 2Air Force Research Laboratory
Additive manufacturing presents both extreme potential and concern for component design. The ability to locally tailor processing path opens the door to sophisticated new designs with heterogeneous properties. However, accounting for this heterogeneity, before exploiting it, requires the ability to link local processing state to properties/performance of local material. A concern with current geometry-based design approaches, such as topology optimization, is not directly accounting for material property changes as geometry updates are made. Given current closed and fixed scanning strategies of most commercial systems, local processing paths are potentially altered significantly with seemingly minor macroscopic geometry changes and are unable to be avoided. This work intends to build ICME modules that predict microstructure (grain size, texture, void Vf, etc.) from processing history and predict performance (E, σys, hardening rate, εf, etc.) from microstructure. These modules will be designed to interface with topology optimization codes to dynamically account for material properties as geometry updates are made. The work is being demonstrated using a laser-based powder-bed fusion process on nickel superalloy IN625 for thin-walled structures. Highly-pedigreed data sets of in-situ monitoring data (beam path, thermal measurements), post-build characterization (CT, RUS, 3D Optical and SEM) and mechanical testing (milli-tensile, HEDM, notch and torsion testing) will be collected and provided to the open community. Challenges problems will be commissioned to benchmark the current modeling capabilities in process-structure and structure-properties. Finally, challenge results will be used in novel forecasting techniques akin to weather forecasting strategies of model aggregation.
Influence of Computational Grid and Deposit Volume on Residual Stress and Distortion Prediction Accuracy for Additive Manufacturing Modeling: Mustafa Megahed1; Joerg Willems1; Pierre-Adrien Pires1; Olivier Desmaison1; 1ESI Group
Powder Bed Additive Manufacturing offers unique advantages in terms of cost, lot size and complexity. The energy used however leads to distortions during the process. The distortion of single layers can be comparable with the powder layer thickness. The contact between the coater blade and the deposited material could terminate the build process. Furthermore, accumulated residual stresses can lead to deviations of the final shape from the design. Several residual stresses and distortion prediction models have been introduced and they have demonstrated qualitative agreement with expectations and experiments. All methods are based on assumptions and process simplifications – most of which were developed and confirmed for welding technology. The use of large deposits is justified by the acceleration of the computational procedure. This work focusses on the accuracy of quick residual stress and distortion models that will both provide layer by layer distortion data as well as the final work piece residual stress and shape. Different methods are utilized and compared in terms of accuracy and computation cost. The most promising model (instantaneous shrinkage approach) is further analyzed to determine conditions for accurate predictions. The residual stress and distortion models are implemented in an ICME platform that takes powder size distribution into account as well as the heat source powder interaction into account. Lower scale models are briefly introduced and data required for the residual stress analysis are documented prior to the analysis of some large components assessing manufacturability and final work piece shape.
12:10 PM Break