4th World Congress on Integrated Computational Materials Engineering (ICME 2017): Integration Framework and Usage - IB
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 2:00 PM
May 22, 2017
Room: Salon I
Location: Ann Arbor Marriott Ypsilanti at Eagle Crest
2:00 PM Invited
Center for Hierarchical Materials Design: Data and Databases for ICME: Emine Gulsoy1; Laura Bartolo1; Juan De Pablo2; Gregory Olson1; Peter Voorhees1; 1Northwestern University; 2University of Chicago
Center for Hierarchical Materials Design (CHiMaD) is a NIST-sponsored Center of Excellence for Advanced Materials Research, focusing on developing the next generation of computational tools, databases and experimental techniques in order to enable the accelerated design of novel materials and their integration to industry. Integrated computational materials engineering tools are vital for realization of the Materials Genome Initiative and CHiMaD is leading several fronts in data and proto-data development for both organic and inorganic materials. CHiMaD’s prototype-data development will be discussed as well as its efforts in database development for organic and inorganic materials along with community initiatives including Data Workshops and Materials Data Facility which aims to serve as a community resource for sharing, storing and mining data.
Integration of Experiments and Simulations to Build Material Big-Data: Gunjin Yun1; 1Seoul National University
In this paper, a method for extracting stress-strain databases from material test measurements is introduced as one of the potential Integrated Computational Materials Engineering (ICME) tools. Measuring spatially heterogeneous stress and strain evolutionary data during material tests is a challenging and costly task. The proposed method can extract a large volume of spatially heterogeneous stress and strain evolutionary data from experimental boundary measurements such as tractions and displacements. For the purpose, nonlinear finite element models are intrusively implemented with artificial neural network (ANN)-based material constitutive models. Then a specialized algorithm that can auto-progressively train ANN material models guided by experimental measurements is executed. Any complex constitutive law is not presumed. From the algorithm, ANN gradually learns complex material constitutive behavior. The training databases are gradually accumulated with self-corrected stress and strain data predicted by the ANN. Finally, material databases are obtained. For an example, visco-elastoplastic material databases are obtained by the proposed method.
ICME Based Hierarchical Design Using Composite Materials for Automotive Structures: Azeez Shaik1; Yagnik Kalariya1; Rizwan Pathan1; Amit Salvi1; 1TCS Research, Tata Consultancy Services
Composite materials are increasingly being used in automotive structures due to their higher specific stiffness and specific strength. Composite material characterization is a complicated task due to micro-scale non-homogeneity and its resulting anisotropy and is generally accomplished with expensive physical tests at coupon level. High fidelity computational models are increasingly being used to accurately establish the elastic material behaviour that also provides detailed information about nonlinear behaviour due damage and fracture. The fiber architecture or composite microstructure can be altered to provide a maximum performance for a given application under certain loads. Thus, material selection from existing materials, and material design for a given component needs to be integrated in the existing design cycle.In this paper, ICME based hierarchical design process integrated with composite material selection and microstructure based material design will be presented. An automotive car door assembly will be designed using this approach. Material selection from given list of composite materials will be carried out using stiffness based approach. Individual components will be checked for damage and failure and a fiber reinforced composite material is designed specifically to suit the requirement keeping the overall stiffness very close to the global requirement. This framework for design decisions is integrated using a TCS PREMAP framework developed in house.
Hot-tearing Modeling of Multicomponent Al-Cu Alloys in an Integrated Computational Materials Engineering Approach: Adrian Sabau1; Seyed Mirmiran2; Christopher Glaspie2; Shimin Li3; Diran Apelian3; Amit Shyam1; J. Haynes1; Andres Rodriguez4; 1Oak Ridge National Laboratory; 2Fiat Chrysler Automobiles North America; 3Worcester Polytechnic Institute; 4Nemak Monterrey
Hot-tearing is a major casting defect that is not a material property but rather a result of the combined effects of thermodynamic phenomena leading to phase precipitation at grain boundaries, solidification microstructure, inter-dendritic feeding, and stress evolution during solidification. The availability of constitutive models for the simulation of hot-tearing and the much longer computational times required for process simulations poses a challenge to the ICME models. The susceptibility of Al-Cu multicomponent alloys to hot-tearing during permanent mold casting was investigated using a constrained permanent mold in which the load and displacement was measured. The experimental results for hot tearing susceptibility are compared with those obtained from numerical simulations. The Cu composition was varied from approximately 5 to 8 pct. (weight). The data for the measured load and displacement during casting were compared with those obtained from numerical simulations to assess the current-state-of-the-art for hot-tearing modeling.
3:30 PM Break
An Integrated Computational Model for Keyhole Laser Welding Process and Residual Stress Prediction: Lili Zheng1; Jiye Wang1; Wei Yuan1; 1Hitachi America Ltd
An integrated computational modeling framework is being developed to simulate keyhole laser welding process and residual stress distribution in welded stainless steels. A conical Gaussian type heat source model is established to predict the temperature profile. As a prerequisite, extensive laser welding experiments are carried out to determine the heat input parameters in the model. For evaluation, the weld bead profile predicted from the heat source model is compared with that from experimental observation. The temperature profile from the heat source model is then passed onto the thermal-mechanical sub-model as thermal load in order to analyze the residual stress distribution in the welds. X-ray diffraction method is adopted to measure the residual stress and verify the model accuracy.
Reduced-order Models for Microscale Plastic Strain Rate Fields in Two-phase Composites Subjected to an Arbitrary Macroscale Plastic Strain Rate using Data Science Approaches: David Montes de Oca Zapiain1; Evdokia Popova1; Surya R. Kalidindi1; 1Georgia Institute of Technology
We will present a reduced-order (computationally fast) model that predicts the microscale spatial distribution of plastic strain rate in a two-phase composite subjected to an arbitrary macroscopically imposed strain rate tensor (using periodic boundary conditions). This model was developed using a recently developed framework of localization linkages called the Material Knowledge Systems (MKS). In prior work, the framework was successfully used to predict local strain rate fields in multiphase composites subjected to a given macroscale plastic strain rate. In the present work, the framework was extended to allow building these reduced-order models for the complete set of all macroscale plastic strain rates that could be applied at the macroscale. These reduced-order models (also called localization linkages) were calibrated and validated to results from microscale finite element models.
Continuum Dislocation Dynamics Model of Heterogeneous Deformation in a Multimodal Particle Nickel Based Superalloy: Joseph Rangel1; Gavin Yearwood1; James Little1; Jonathan Benson1; Hector Basoalto1; 1University of Birmingham
A continuum dislocation field plasticity framework has been developed to study heterogeneous slip development under cyclic loading conditions, accounting for multimodal particle distribution. The model simulates a continuum dislocation density (CDD) evolution through a gamma prime (γ’) type dispersion on a 2D cross section of a single grain, and takes into account dislocation mobility and interaction. A non-local theory is used to represent the influence of long-range combined dislocation stress fields which influence the dislocation motion and behaviour at obstacles, observed through events such as climb and pile-up. The model strives to replicate the heterogeneous nature of plastic deformation. Simulations are carried out on a representative microstructure of high volume fraction disc Superalloy, which includes grain boundaries and multi modal distribution of gamma prime precipitate particles (γ’). The strain maps generated by the model are compared with experimental in-situ results.It is shown that large concentrations of localised deformation are identified as potential sites for fatigue crack initiation, once surpassing a critical value. This critical value is determined by the amount of shear that would indicate the opening of a micro-crack. The volume fraction particle size distribution of the gamma prime the microstructure is varied to see the influence on stress distribution and cyclic loading response. These results are analysed to see which precipitate particle distributions promote resistance to fatigue crack initiation.