ASM Training Course Information
(Planning note for potential AIAA Conference event – January, 2014; Maryland)
This course has been assembled to provide an overview of computational materials engineering. It will provide a primer on the various types of models and simulation methods. It is aimed at providing a general understanding of the critical issues relative to computational materials engineering, with a goal to provide students with greater knowledge of materials and process modeling capabilities and limitations. The important aspects of linking materials models with process models and subsequently to component design and behavior analysis models will be reviewed. This course is aimed at providing critical information to materials, mechanical design and manufacturing engineers, program managers and engineering management looking to introduce or apply computational materials methods in the future as point solutions or as systematic changes to how they perform their engineering functions. This course will not provide practical hands-on training of specific models or codes, but rather will provide an appreciation for the types of models available, their benefit and how various model outputs should be interpreted.
Introduction to Computational Materials Engineering, D. Furrer, Pratt & Whitney
Computational materials engineering is a vast and complicated field of engineering. The complexity of this emerging technology area is due in part to the challenges in developing appropriate, accurate and efficient models for the range of materials and process applications. This complexity, however, is often overshadowed by the challenges presented by the multi-disciplinary aspects of development and deployment within industrial environments. Successful integration of computational materials engineering tools for industrial applications requires consideration of many specific aspects.
This section of the course will review to topic of computational materials engineering at a highlevel, and will introduce critical topics for materials engineers, manufacturing engineers, designers, and program managers, including information on types of models, model assumptions, input data requirements, interpretation of model outputs, and aspects of computational system quality control.
These topics will be further discussed in the specific sections on materials models, manufacturing process models, and linkage of materials and process models with design functions that follow.
Atomistic Modeling, W. Hamm, Materials Design
A brief, high level overview of major atomistic modeling techniques (Quantum Mechanics and
Molecular Dynamics) will be given, together with a brief description of other methods (kinetic Monte
Carlo, molecular static, etc.) followed by examples of practical use in industry. Examples will include how atomistic techniques can inform longer length scale models and be integrated with designs. Emphasis will be placed on practical applications.
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Review of Microstructure and Property Models, J. Sebastian, Questek, LLC
A key component to the application of computational materials engineering is the development, validation and use of structure and property models for the materials systems of interest. Model development and selection is dependent on the intended use and addresses issues of accuracy, extrapolative potential, computational efficiency, and data requirements for calibration, validation and verification. Furthermore, integration of materials structure and property models with process simulation and component level performance is essential for full implementation of an integrated framework that delivers the full benefits of computational materials engineering.
This section of the course will develop an understanding of the critical aspects of process-structure and structure-property models in a hierarchical approach that builds from basic fundamental parameters to full system level simulation tools. Typical modeling frameworks and approaches for computational materials models will be introduced with comparison of the benefits and shortcomings of the approaches explored. Examples of applications of computational materials models in use today will be detailed. Implications and key challenges to integration of computational materials models will be explored.
Summary of Thermo-Mechanical Process Modeling and Design Integration, R. Shankar, SFTC
Summary of Casting and Solidification Process Modeling and Design Integration, J.Guo, ESI
Process modeling has been applied to industrial applications for nearly three decades. Aerospace and defense applications have always been on the leading edge of technology. Automotive manufacturers have been dominant in the business case. Industrial application is the best measure of success in the development of a process model.
In the 1970’s, computers were developed to allow technically advanced companies to deploy finite element (FEM) models to a range of structural analysis for linear elastic cases. In the 1980’s forming and casting models were deployed under government sponsorship. These models grew to commercial programs through the end of the 20th century, with many companies applying them to daily production.
These and other simulation tools, provided information about product geometry variables such as strain or temperature. The results were generally based on simplified material models. For example in cold forming, a simple power law was commonly used to describe the flow stress of a workpiece.
As applications increased, the requirements grew from simulating a single unit process to modeling a chain of processes that may or may not be homogeneous. With this in mind, the simplified material models were frequently the weak link in the chain. On the other hand, significant progress has been made in simulating unit processes and process chains over the past 30 years. Today, the vast majority of manufacturers use some form of process simulation in forging, cold forming, stamping, casting, billet conversion and more. In this course an overview of existing and practical capabilities will be presented.
The goal in critical system component manufacturing is to optimize the life in service. Local optimums do not necessarily achieve that goal. The vision of future models includes the ability to optimize a process for cost or weight, with an eye on the component service life. Material modeling is the key to this objective. A range of capabilities are being developed to support this strategic objective.
A summary of these models will be presented. New tools and approaches for commercial models will be discussed, such as design of experiments and optimization. Limitations in predictive material models, such as heat treat cracking will also be presented.
This section will provide some history and an overview of mature industrial modeling applications. It will review advanced cases that may be practical under ideal conditions. A view of current and future developments will put the current ‘state of the art’ in perspective.
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Overview of Linking Materials Models with Design Models, R. Pulikollu, Sentient Science
Multi-objective aerospace structure capabilities require structure-material design, utilizing conventional and advanced engineered materials, like metals and alloys, polymer-matrix composites, etc.. Multiscale materials modeling develops improved descriptions of structure and material behavior through the incorporation of information about material structures, evolution process and deformation mechanisms from various length and time scales. In the spatial domain, scales range from the atomic scales of nano-scale defects, to the micro-scale regions of polycrystals, meso-scale of matrix with distributions of voids or inclusions to the macroscopic scale of components. Temporal domains can range from femto-second atomic oscillations to longer time scales of structural dynamics. Bridging the scales involves implicit inclusion of smaller scales through reduced-order descriptions, e.g. constitutive laws, and explicit inclusion of smaller length scales through coupling of models at different scales.
Robust multi-scaling methodologies often involve two-way coupling (bottom-up and top-down), which can overcome limitations imposed by homogenization techniques used in micro-mechanical analyses to track the evolution of instabilities causing rupture.
This 2-hour short course presentation will outline robust approaches to multi-scale modeling both in the time and spatial domains, for two classes of problems, viz. (i) ductile fracture of multi-phase alloys, e.g. cast aluminum alloys and (ii) fatigue failure of polycrystalline alloys such as Ti and Ni alloys. The first part of this presentation will introduce spatial multi-scaling using an adaptive concurrent multi-scale framework that involves both bottom-up (homogenization) and top-down (localization). Four important ingredients of multi-scale modeling of ductile failure in heterogeneous cast aluminum alloys include: (i) a multi-scale characterization based preprocessor for multi-scale models; (ii) microstructural analysis module for ductile fracture; (iii) a homogenization based continuum damage model for ductile materials that can be used in macroscopic analysis modules and (iv) multi-scale framework for ductile crack propagation.
The second part of this presentation will discuss the development of microstructure based models for dwell fatigue crack initiation in polycrystalline alloy Ti and Ni alloys. Systematic development of an image-based crystal plasticity finite element model, and fatigue crack nucleation model under dwell loading will be discussed. The presentation will also discuss multi-time scaling methods with 3D crystal plasticity finite element (CPFE) simulations for fatigue life prediction, accommodating large number of cycles to failure. A wavelet transformation based multi-time scaling (WATMUS) algorithm for accelerated crystal plasticity finite element simulations will be introduced in particular.
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