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Estimating Crystallographic Texture and Orientation Statistics
Published in Jeffrey P. Simmons, Lawrence F. Drummy, Charles A. Bouman, Marc De Graef, Statistical Methods for Materials Science, 2019
The author believes that in the near future the non-parametric approaches described above will mature and a host of new tools and techniques will be made available. This is likely to be driven by the continued development of integrated computational materials engineering (ICME) and related activities. ICME is predicated on the replacement of expensive experiment and testing paradigms with computational modeling and simulation. This necessitates the incorporation of uncertainty quantification and quantitative comparison between simulations and across simulation and experiment. It is telling that despite the ubiquity of texture analysis there is no standard way of reporting error or uncertainty in a texture measurement or plot. We don’t have a framework for reporting error bars on a pole figure, for example. The adoption of parametric approaches will allow materials to leverage the recent developments in the area of UQ being developed by the statistics, applied mathematics, design, and machine learning communities.
Alloy design
Published in Gregory N. Haidemenopoulos, Physical Metallurgy, 2018
Computational alloy design has its origins on the pioneering work of Prof. G.B. Olson at MIT and Northwestern University in the USA, where the concept of materials by design was first developed. This work led to the announcement, in 2011, by the White House, of the Materials Genome Initiative (MGI). This in turn initiated the global framework of Integrated Computational Materials Engineering (ICME), which encompasses computational materials modeling for engineering applications. Integrated Computational Materials Design (ICMD) is a framework based on ICME and MGI and aims at the accelerated knowledge-based design of new materials. The origins and the ICMD methodologies are described in a review paper by Xiong and Olson (see suggested reading at the end of the chapter).
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Published in Adedeji B. Badiru, Vhance V. Valencia, David Liu, Additive Manufacturing Handbook, 2017
The development and implementation of new materials and manufacturing processes for aerospace application are often hindered by the high cost and long time span associated with current qualification procedures. The data requirements necessary for material and process qualification are extensive and often require millions of dollars and multiple years to complete. This burden is a serious impediment to the pursuit of revolutionary new materials and more affordable processing methods for aerospace components. The application of integrated computational materials engineering (ICME) methods to this problem can help to reduce the barriers to rapid insertion of new materials and processes. By establishing predictive capability for the development of process parameters, microstructural features and mechanical properties, a streamlined approach to qualification is possible.
Recent developments in the application of machine-learning towards accelerated predictive multiscale design and additive manufacturing
Published in Virtual and Physical Prototyping, 2023
Sandeep Suresh Babu, Abdel-Hamid I. Mourad, Khalifa H. Harib, Sanjairaj Vijayavenkataraman
The new ‘materials by design’ approach has multi-scale modelling and simulation as a core component. Multi-scale modelling enables a holistic integration of engineering and scientific methodologies and knowledge, including the unification of material information to performance analysis and process simulation of product manufacturing. A multiscale model is one in which information across various scales, as shown in Figure 11, is considered to comprehend the system better, thereby reducing the overall computational cost. A successful multiscale model must overcome several challenges (LLorca et al. 2013), primarily the balance between accuracy and computational cost. Multiscale models are the most vital component of the so-called Integrated Computational Materials Engineering (ICME) paradigm. It is based on the Process-Structure-Properties Performance principle and encompasses a study of several aspects of a material design cycle, including processing, arrangement of internal structure, properties and performance. Computational techniques such as molecular modelling and ML are creating revolutions in the material design paradigm. It is now possible to obtain a microstructure according to the user’s desired properties.
Recent advances in light metals and manufacturing for automotive applications
Published in CIM Journal, 2021
The challenge of designing the next generation of lighter weight vehicles is putting increased pressure on the automotive industry to speed up the implementation of new lightweight materials and processes. Integrated Computational Materials Engineering (ICME) is defined as the integration of materials information, captured in computational tools, with engineering product performance analysis and manufacturing-process simulation (Pollock et al., 2008). Figure 9 is an ICME framework for new application development proposed by Luo (2015). Among the three major components in this framework, system/product design ICME using finite element analysis (FEA) methods (Kurowski, 2017) is well established, and material design ICME based on CALPHAD and kinetics modeling (Shi & Luo, 2018) is relatively mature, manufacturing ICME needs significantly more research and development efforts. It is critically important to develop location-specific microstructure models for location-specific property/performance simulation of lightweight applications by combining material models and manufacturing process models.
Toward an In-Depth Material Model for Cermet Nuclear Thermal Rocket Fuel Elements
Published in Nuclear Technology, 2021
William C. Tucker, Piyas Chowdhury, Lauren J. Abbott, Justin B. Haskins
Many of the material issues faced by the early NTP development efforts could have been more quickly identified and remedied by integrating the in-depth material modeling capabilities available today, which would have reduced the costly trial-and-error development with experimental fabrication and testing in hot hydrogen. Namely, integrated computational materials engineering (ICME) techniques, which span the atomistic scale (e.g., ab initio and molecular dynamics), mesoscale/microscale (e.g., discrete dislocation dynamics), and macroscale (e.g., finite element), have shown promise for characterizing materials in extreme conditions.12–16 For ceramic uranium materials, ab initio [specifically density functional theory (DFT)] computations have been used to predict crystal structural characteristics, vibrational spectra, mechanical properties, and thermodynamic properties at a range of temperatures with comparable accuracy to experiments.12,13 For refractory metals like tungsten, discrete dislocation dynamics have shown the ability to provide experimentally comparable measures of mechanical response and plasticity in single crystals.15,16 The success of such methods implies possible applications to other aspects of fuel element material behavior, such as the susceptibility of materials to hydrogen reactions and vaporization, intercalation and mobility of hydrogen or fission species into coatings and substrates, creep and hydrogen embrittlement effects on mechanical performance, and fracture in fuel materials.