The Experts below are selected from a list of 679719 Experts worldwide ranked by ideXlab platform
Farrokh Mistree - One of the best experts on this subject based on the ideXlab platform.
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Robust Design of Materials—Design Under Uncertainty
Integrated Design of Multiscale Multifunctional Materials and Products, 2010Co-Authors: David L. Mcdowell, Hae-jin Choi, Carolyn Conner Seepersad, Janet K. Allen, Jitesh H. Panchal, Farrokh MistreeAbstract:This chapter discusses the fundamentals of robust Design and uncertainty management in simulation-based Design. In engineering Design, the concept of robustness is used to mitigate loss of functionality or performance due to reliance on information that is uncertain or difficult to model or compute. Management of uncertainty is crucial in Materials Design. The degree of uncertainty is often quite substantial in experiments, processing methods, material structure, and model parameters that support concurrent Design of Materials and products/systems. The chapter defines different characteristic types of uncertainty associated with material Design. Several examples of sources of uncertainty are also illustrated. Further, the requirements for new approaches for the management of uncertainty in Materials Design are addressed. The chapter discusses the Taguchi's robust Design approach and also describes the robust concept exploration method (RCEM), which is an extension to Taguchi's approach. These existing robust Design approaches are evaluated against the challenges in Materials Design to identify the requirements for robust Design methods in multilevel Design.
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Robust Design of Materials—Design Under Uncertainty
Integrated Design of Multiscale Multifunctional Materials and Products, 2010Co-Authors: David L. Mcdowell, Hae-jin Choi, Carolyn Conner Seepersad, Janet K. Allen, Jitesh H. Panchal, Farrokh MistreeAbstract:This chapter discusses the fundamentals of robust Design and uncertainty management in simulation-based Design. In engineering Design, the concept of robustness is used to mitigate loss of functionality or performance due to reliance on information that is uncertain or difficult to model or compute. Management of uncertainty is crucial in Materials Design. The degree of uncertainty is often quite substantial in experiments, processing methods, material structure, and model parameters that support concurrent Design of Materials and products/systems. The chapter defines different characteristic types of uncertainty associated with material Design. Several examples of sources of uncertainty are also illustrated. Further, the requirements for new approaches for the management of uncertainty in Materials Design are addressed. The chapter discusses the Taguchi's robust Design approach and also describes the robust concept exploration method (RCEM), which is an extension to Taguchi's approach. These existing robust Design approaches are evaluated against the challenges in Materials Design to identify the requirements for robust Design methods in multilevel Design.
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Critical Path Issues in Materials Design
Integrated Design of Multiscale Multifunctional Materials and Products, 2010Co-Authors: David L. Mcdowell, Hae-jin Choi, Carolyn Conner Seepersad, Janet K. Allen, Jitesh H. Panchal, Farrokh MistreeAbstract:The purpose of this chapter is to review the existing efforts related to Materials Design. Concurrent Design of Materials and products is a compelling, transformative technology for 21st-century competitiveness. It also serves as an interdisciplinary platform for instruction of new generations of Materials scientists and engineers. Product Design and Materials development are not mutually exclusive and independent activities but synergistic components of an integrated product, process, and Materials Design endeavor. This challenge involves a philosophical and cultural shift toward inductive, goal-oriented synthesis of products and their constituent Materials and processing paths, and for this, a systems-based strategy is essential. With an emphasis on the limitations of current capabilities and the associated research and development opportunities, the chapter outlines several critical path issues, such as adequate models and experimental data on different length and time scales for a diverse set of functions that material systems must deliver; techniques for characterizing and managing uncertainty in material models applied to processing paths and structure–property relations, as well as resulting Design specifications; tools for linking diverse modeling and simulation tools and methods and related data across length and time scales, functional domains, and material classes; and systems Design methods and tools that bridge or integrate the Design of Materials, manufacturing processes, and products/components.
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Designing Embodiment Design Processes Using a Value-of-Information-Based Approach With Applications for Integrated Product and Materials Design
Volume 1: 34th Design Automation Conference Parts A and B, 2008Co-Authors: Matthias Messer, Janet K. Allen, Farrokh Mistree, Jitesh H. Panchal, Vivek Krishnamurthy, Benjamin Klein, Paul D. YoderAbstract:Designers are continuously challenged to manage complexity in embodiment Design processes (EDPs), in the context of integrated product and Materials Design. In order to manage complexity in Design processes, a systematic strategy to embodiment Design process generation and selection is presented in this paper. The strategy is based on a value-of-information-based Process Performance Indicator (PPI). The approach is particularly well-suited for integrated product and Materials Design, and all other scenarios where knowledge of a truthful, i.e., perfect, Design process and bounds of error are not available in the entire Design space. The proposed strategy is applied to Designing embodiment Design processes for photonic crystal waveguides in the context of a next-generation optoelectronic communication system. In this paper, it is shown that the proposed strategy based on the Process Performance Indicator is useful for evaluating the performance of embodiment Design processes particularly when accuracy of the prediction or the associated error bounds are not known.Copyright © 2008 by ASME
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exploring the advantages of Materials Design in a product Design process
Design Automation Conference, 2006Co-Authors: Hannah Muchnick, Janet K. Allen, Stephanie C Thompson, Emad Samadiani, Yogendra Joshi, Farrokh MistreeAbstract:In this paper, we explore the benefits of Materials Design in a product Design process. We also compare the methods of material selection and Materials Design by demonstrating two examples—the Design of a cantilever beam for minimum weight and the Design of a fan blade for minimum weight. The Design of the cantilever beam is carried out using Ashby’s material selection method as well as a proposed method for Materials Design. The Design of the fan blade and its material is completed using computational tools. Our goal in this paper is to demonstrate the benefits of Materials Design over material selection methods and to illustrate the flexibility inherent in Materials Design processes. We are more interested in revealing the possibilities of Materials Design, rather than the specific results from the example problems. The investigation of Materials Design presented in this paper moves us one step closer towards the realization of a systematic, inductive method for the concurrent Design of products and Materials.Copyright © 2006 by ASME
Sergei V Kalinin - One of the best experts on this subject based on the ideXlab platform.
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Big–deep–smart data in imaging for guiding Materials Design
Nature Materials, 2015Co-Authors: Sergei V Kalinin, Bobby G. Sumpter, Richard K. ArchibaldAbstract:Advanced microscopy techniques provide unique insight into a material's structure. This Progress Article discusses how the application of big, deep and smart data to image analysis might permit the Design of Materials with advanced functionality. Harnessing big data, deep data, and smart data from state-of-the-art imaging might accelerate the Design and realization of advanced functional Materials. Here we discuss new opportunities in Materials Design enabled by the availability of big data in imaging and data analytics approaches, including their limitations, in material systems of practical interest. We specifically focus on how these tools might help realize new discoveries in a timely manner. Such methodologies are particularly appropriate to explore in light of continued improvements in atomistic imaging, modelling and data analytics methods.
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big deep smart data in imaging for guiding Materials Design
Nature Materials, 2015Co-Authors: Sergei V Kalinin, Bobby G. Sumpter, Rick ArchibaldAbstract:Advanced microscopy techniques provide unique insight into a material's structure. This Progress Article discusses how the application of big, deep and smart data to image analysis might permit the Design of Materials with advanced functionality. Harnessing big data, deep data, and smart data from state-of-the-art imaging might accelerate the Design and realization of advanced functional Materials. Here we discuss new opportunities in Materials Design enabled by the availability of big data in imaging and data analytics approaches, including their limitations, in material systems of practical interest. We specifically focus on how these tools might help realize new discoveries in a timely manner. Such methodologies are particularly appropriate to explore in light of continued improvements in atomistic imaging, modelling and data analytics methods.
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Big-deep-smart data in imaging for guiding Materials Design
Nature Materials, 2015Co-Authors: Sergei V Kalinin, Bobby G. Sumpter, Richard K. ArchibaldAbstract:Harnessing big data, deep data, and smart data from state-of-the-art imaging might accelerate the Design and realization of advanced functional Materials. Here we discuss new opportunities in Materials Design enabled by the availability of big data in imaging and data analytics approaches, including their limitations, in material systems of practical interest. We specifically focus on how these tools might help realize new discoveries in a timely manner. Such methodologies are particularly appropriate to explore in light of continued improvements in atomistic imaging, modelling and data analytics methods.
Richard K. Archibald - One of the best experts on this subject based on the ideXlab platform.
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Big–deep–smart data in imaging for guiding Materials Design
Nature Materials, 2015Co-Authors: Sergei V Kalinin, Bobby G. Sumpter, Richard K. ArchibaldAbstract:Advanced microscopy techniques provide unique insight into a material's structure. This Progress Article discusses how the application of big, deep and smart data to image analysis might permit the Design of Materials with advanced functionality. Harnessing big data, deep data, and smart data from state-of-the-art imaging might accelerate the Design and realization of advanced functional Materials. Here we discuss new opportunities in Materials Design enabled by the availability of big data in imaging and data analytics approaches, including their limitations, in material systems of practical interest. We specifically focus on how these tools might help realize new discoveries in a timely manner. Such methodologies are particularly appropriate to explore in light of continued improvements in atomistic imaging, modelling and data analytics methods.
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Big-deep-smart data in imaging for guiding Materials Design
Nature Materials, 2015Co-Authors: Sergei V Kalinin, Bobby G. Sumpter, Richard K. ArchibaldAbstract:Harnessing big data, deep data, and smart data from state-of-the-art imaging might accelerate the Design and realization of advanced functional Materials. Here we discuss new opportunities in Materials Design enabled by the availability of big data in imaging and data analytics approaches, including their limitations, in material systems of practical interest. We specifically focus on how these tools might help realize new discoveries in a timely manner. Such methodologies are particularly appropriate to explore in light of continued improvements in atomistic imaging, modelling and data analytics methods.
David L. Mcdowell - One of the best experts on this subject based on the ideXlab platform.
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Systems Approaches to Materials Design: Past, Present, and Future
Annual Review of Materials Research, 2019Co-Authors: Raymundo Arroyave, David L. McdowellAbstract:There is increasing awareness of the imperative to accelerate Materials discovery, Design, development, and deployment. Materials Design is essentially a goal-oriented activity that views the mater...
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Robust Design of Materials—Design Under Uncertainty
Integrated Design of Multiscale Multifunctional Materials and Products, 2010Co-Authors: David L. Mcdowell, Hae-jin Choi, Carolyn Conner Seepersad, Janet K. Allen, Jitesh H. Panchal, Farrokh MistreeAbstract:This chapter discusses the fundamentals of robust Design and uncertainty management in simulation-based Design. In engineering Design, the concept of robustness is used to mitigate loss of functionality or performance due to reliance on information that is uncertain or difficult to model or compute. Management of uncertainty is crucial in Materials Design. The degree of uncertainty is often quite substantial in experiments, processing methods, material structure, and model parameters that support concurrent Design of Materials and products/systems. The chapter defines different characteristic types of uncertainty associated with material Design. Several examples of sources of uncertainty are also illustrated. Further, the requirements for new approaches for the management of uncertainty in Materials Design are addressed. The chapter discusses the Taguchi's robust Design approach and also describes the robust concept exploration method (RCEM), which is an extension to Taguchi's approach. These existing robust Design approaches are evaluated against the challenges in Materials Design to identify the requirements for robust Design methods in multilevel Design.
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Robust Design of Materials—Design Under Uncertainty
Integrated Design of Multiscale Multifunctional Materials and Products, 2010Co-Authors: David L. Mcdowell, Hae-jin Choi, Carolyn Conner Seepersad, Janet K. Allen, Jitesh H. Panchal, Farrokh MistreeAbstract:This chapter discusses the fundamentals of robust Design and uncertainty management in simulation-based Design. In engineering Design, the concept of robustness is used to mitigate loss of functionality or performance due to reliance on information that is uncertain or difficult to model or compute. Management of uncertainty is crucial in Materials Design. The degree of uncertainty is often quite substantial in experiments, processing methods, material structure, and model parameters that support concurrent Design of Materials and products/systems. The chapter defines different characteristic types of uncertainty associated with material Design. Several examples of sources of uncertainty are also illustrated. Further, the requirements for new approaches for the management of uncertainty in Materials Design are addressed. The chapter discusses the Taguchi's robust Design approach and also describes the robust concept exploration method (RCEM), which is an extension to Taguchi's approach. These existing robust Design approaches are evaluated against the challenges in Materials Design to identify the requirements for robust Design methods in multilevel Design.
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Critical Path Issues in Materials Design
Integrated Design of Multiscale Multifunctional Materials and Products, 2010Co-Authors: David L. Mcdowell, Hae-jin Choi, Carolyn Conner Seepersad, Janet K. Allen, Jitesh H. Panchal, Farrokh MistreeAbstract:The purpose of this chapter is to review the existing efforts related to Materials Design. Concurrent Design of Materials and products is a compelling, transformative technology for 21st-century competitiveness. It also serves as an interdisciplinary platform for instruction of new generations of Materials scientists and engineers. Product Design and Materials development are not mutually exclusive and independent activities but synergistic components of an integrated product, process, and Materials Design endeavor. This challenge involves a philosophical and cultural shift toward inductive, goal-oriented synthesis of products and their constituent Materials and processing paths, and for this, a systems-based strategy is essential. With an emphasis on the limitations of current capabilities and the associated research and development opportunities, the chapter outlines several critical path issues, such as adequate models and experimental data on different length and time scales for a diverse set of functions that material systems must deliver; techniques for characterizing and managing uncertainty in material models applied to processing paths and structure–property relations, as well as resulting Design specifications; tools for linking diverse modeling and simulation tools and methods and related data across length and time scales, functional domains, and material classes; and systems Design methods and tools that bridge or integrate the Design of Materials, manufacturing processes, and products/components.
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Foundations for a systems-based approach for Materials Design
10th AIAA ISSMO Multidisciplinary Analysis and Optimization Conference, 2004Co-Authors: Carolyn Conner Seepersad, Hae-jin Choi, David L. Mcdowell, Janet K. Allen, Jitesh H. Panchal, Marco Gero Fernández, Farrokh MistreeAbstract:Establishing systems-based Materials Design methods is an important step towards enabling rapid, concurrent Design of Materials and products with the potential for significant technological innovations. Materials Design involves tailoring material structures and processing paths to achieve properties and performance levels that are customized for a particular application. It is a complex, non-deterministic, multi-scale, multifunctional activity that requires multiple collaborating Designers and distributed, heterogeneous computing resources. Accordingly, a systems-based Design approach is required with which to manage information flows, embed performance-property-structure-processing relations, interrogate models, explore variability, and engage collaborative decision-support protocols. In this paper, we discuss some of the intellectual and computing foundations of our systemsbased approach for Materials Design. It has three primary facets: (1) a decision support framework for modeling and supporting a complex, collaborative Design process, (2) robust Design methods for modeling uncertainty and managing or minimizing its impact on Design specifications, and (3) a computational infrastructure for integrating and sharing heterogeneous information and computing and software resources. Some of the key aspects of our approach are illustrated via Design of multifunctional cellular Materials for a structural heat exchanger application.
Hiroshi Katayamayoshida - One of the best experts on this subject based on the ideXlab platform.
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computational Materials Design of defect induced ferrimagnetic mno
Journal of Physics: Condensed Matter, 2014Co-Authors: Masayoshi Seike, Tetsuya Fukushima, Kazunori Sato, Hiroshi KatayamayoshidaAbstract:We present a computational Materials Design for defect-induced ferrimagnetic MnO. The magnetic properties of MnO containing Mn vacancies were investigated using first-principle calculations. For these electronic structure calculations, we employed a pseudo-self-interaction-corrected local density approximation (PSIC-LDA). We used the Korringa–Kohn–Rostoker coherent potential approximation (KKR-CPA) to create a random distribution of atoms at the assigned sites. Having described the magnetic properties with a classic Heisenberg model, we calculated the effective exchange coupling constants by applying the magnetic force theorem to two magnetic sites embedded in the CPA medium. We estimated the Curie temperatures from the calculated exchange interactions. This study found that the Mn vacancies induced ferrimagnetic ground states in MnO, and that the Curie temperature could reach room temperature at Mn vacancy concentrations above 20%. These findings suggest a new route for Designing ferrimagnetic Materials from anti-ferromagnetic host Materials.
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Materials Design for semiconductor spintronics by ab initio electronic structure calculation
Physica B-condensed Matter, 2003Co-Authors: Hiroshi Katayamayoshida, Kazunori SatoAbstract:Abstract A systematic study for the Materials Design of III–V and II–VI compound-based ferromagnetic diluted magnetic semiconductors is given based on ab initio calculations within the local spin density approximation. The electronic structures of 3d-transition-metal-atom-doped GaN and Mn-doped InN, InP, InAs, InSb, GaN, GaP, GaAs, GaSb, AlN, AlP, AlAs and AlSb were calculated by the Korringa–Kohn–Rostoker method combined with the coherent potential approximation. It is found that the ferromagnetic ground states are readily achievable in V-, Cr- or Mn-doped GaN without any additional carrier doping treatments, and that InN is the most promising candidate for high-TC ferromagnet. A simple explanation of the systematic behavior of the magnetic states in III–V and II–VI compound-based diluted magnetic semiconductors is also given. It is also shown that V or Cr-doped ZnS, ZnSe, and ZnTe are ferromagnetic without p- or n-type doping treatment. However, Mn-, Fe-, Co- or Ni-doped ZnS, ZnSe and ZnTe are spin-glass states. V-, Cr-, Fe-, Co-, Ni-doped ZnO without any doping and Mn-doped ZnO with p-type hole doping all shows half-metallic transparent ferromagnetism.
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first principles Materials Design for semiconductor spintronics
Semiconductor Science and Technology, 2002Co-Authors: Kazunori Sato, Hiroshi KatayamayoshidaAbstract:Materials Design of new functional diluted magnetic semiconductors (DMSs) is presented based on first principles calculations. The stability of the ferromagnetic state in ZnO-, ZnS-, ZnSe-, ZnTe-, GaAs- and GaN-based DMSs is investigated systematically and it is suggested that V- or Cr-doped ZnO, ZnS, ZnSe and ZnTe are candidates for high-TC ferromagnetic DMSs. V-, Cr- or Mn-doped GaAs and GaN are also candidates for high-TC ferromagnets. It is also shown that Fe-, Co- or Ni-doped ZnO is ferromagnetic. In particular, the carrier-induced ferromagnetism in ZnO-based DMSs is investigated and it is found that their magnetic states are controllable by changing the carrier density. The origin of the ferromagnetism in the DMSs is also discussed.
