410 p.
ISBN: 978-0-12-396989-7The prominent grand challenge in materials engineering for the twenty-first century is to effect a reversal of the paradigm by which new materials are developed, especially for highly constrained design (HCD) applications. Traditional methodologies for new materials development are driven mainly by innovations in processing; it follows that only a limited number of readily accessible microstructures are considered,1 with attention focused on a small number of properties or performance objectives. For HCD applications, the designer faces increasingly complex requirements with multiple property objectives/constraints and material anisotropy affecting system performance. It is evident that the time- and resource-consumptive empiricism that has dominated materials development during the past century must give way to a greater dependence on modeling and simulation.2 We need to invert the current paradigm in new materials development from the present (deductive) cause-and-effect approach to a much more powerful and responsive (inductive) goal–means approach (Olson, 1997). This shift could substantially reduce system development time and cost for materialssensitive HCD problems. There has existed a fundamental incompatibility during the past two decades between materials science and engineering and the engineered product design cycle. Current methodology for introducing new materials into engineered components requires up to 10 years of development time. This compares with design optimization methodologies that are presently capable of introducing sophisticated design evolvements (excluding materials considerations) in a matter of days or weeks. One consequence of this incompatibility is a fundamental weakness in the nexus that links materials science and engineering to the design enterprise, where the goal is to tailor a material’s microstructure to meet the stringent properties and performance requirements of complex components and systems. Addressing this gap is the primary motivation for this book.
To the best of the authors’ knowledge, this book presents the first mathematically rigorous framework for addressing the inverse problems of materials design and process design, while using a comprehensive set of hierarchical measures of the microstructure statistics and composite theories that are based on the same description of the microstructure. The framework presented in the book utilizes highly efficient spectral representations to arrive at invertible linkages between material structure, its properties, and the processing paths used to alter the material structure. Several recent high-profile reports (Integrated Computational Materials Engineering (ICME), The National Academies Press, 2008; Materials Genome Initiative for Global Competitiveness, National Science and Technology Council, 2011; A National Strategic Plan for Advanced Manufacturing, National Science and Technology Council, 2012) have all called for the creation of a new materials innovation infrastructure to facilitate the design, manufacture, and deployment of new highperformance materials at a dramatically accelerated pace in emerging advanced technologies. We believe that the framework presented in this book can serve as the core enabler for these strategic initiatives.
1It is known that the space of potential microstructures is vastly larger than the set that is typically characterized. This position has been strongly articulated in the report of findings of a National Science Foundation (NSF)-sponsored workshop entitled New Directions in Materials Design Science and Engineering, edited by McDowell and Story (1998). This book is primarily intended as a reference for specialists engaged in the emerging field of materials design. It can also be used as a textbook for a sequence of two courses offered for a high-level undergraduate class or a graduate class. Chapters 1 through 3 serve as background material and can be skipped (or assigned as self-reading) if students have familiarity with this material. Chapters 4 through 11 introduce the basic concepts of the first-order theories and illustrate their usage in first-order inverse solutions to materials and process design problems. These chapters could be the focus of a first course in MSDPO (microstructure-sensitive design for performance optimization). In a second follow-up course, the focus can be on the more difficult concepts associated with second-order theories presented in Chapters 12 through
15. Chapter 16 provides useful background material on microscopy techniques (with a strong focus on electron backscatter diffraction) that can be used in either course to give the student a solid introduction to at least one characterization technique that complements the computational
approaches found in the rest of the text. Our past experience indicates that these courses are
highly amenable to the incorporation of team projects by small groups of students as an integral part of the course.
