1. Analytical Methods in Manufacturing

    41:01

    from ISR at UMD / Added

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    Peter Denno National Institute of Standards and Technology Abstract Modeling methodology is a vast subject, encompassing empirical and analytical means of discovery and numerical techniques. Design engineering and operations research are two areas where modeling can be key to success. In these two areas alone, the great variety of questions that could be explored, and methods applied, might make one wonder whether there could be, nonetheless, general means to integrate knowledge from models directly with other enterprise knowledge. A question that arises early is whether there might be value in treating equations as information objects, in the sense that system models, component data sheets, and production data are information objects, managed and interrelated by various software applications. Could exposing equations in this sense enhance knowledge refinement and reuse? Could it facilitate model verification? Agility? In this presentation I will discuss how NIST’s Modeling Methodology for Smart Manufacturing project is approaching these questions. We are using metamodels and semantic web technology to link equations into wider information infrastructure and support their independence. I will describe an investigation in which equations representing predictive models of a unit manufacturing process were developed and interrelated with production-oriented information. In on-going work, we are using mapping technology, templates, and trade-off analysis to formulate optimization problems, providing optimal process parameters. Biography Peter Denno is a computer scientist in the Systems Integration Division. He has 32 years’ experience in manufacturing research including 20 years at NIST and 10 years at Pratt & Whitney. His first encounter with metamodeling was 19 years ago, when he developed the Expresso EXPRESS validation system. He has remained involved with the technology since that time.

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    • Integrated Modeling and Analysis to Support Model-Based System Developments

      01:26:37

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      Hongman Kim Senior Research Engineer Phoenix Integration Abstract Model-based systems engineering (MBSE) is an approach to improve traditional document-based systems engineering approach through the use of a system model. In the current practice of system developments, there exists a large gap between systems engineering activities and engineering analyses, because systems engineers and engineering analysts are using different models, tools and terminology. The gap results in inefficiencies and quality issues that can be very expensive to fix. An integrated modeling and analysis capability was developed that bridges the gap. The technical approach is based on integrating SysML modeling tools with a process integration and design optimization framework. A capability was developed to translate SysML parametric diagrams to analytical models and execute them. The results of engineering analyses are used to automatically check system requirements in SysML models. When this capability is combined with simulation of SysML behavioral diagrams, it is possible to predict how a system would perform during the course of a mission. These capabilities will be demonstrated using a couple of examples: a car brake design problem and mission analysis of a CubeSat, a miniature satellite. The integrated modeling and analysis approach allows engineers to perform continuous design, analysis, and trade studies throughout the design process, and respond quickly to changes in requirements and design configurations. Biography Hongman Kim is a senior research engineer and MBSE (model-based systems engineering) technology lead at Phoenix Integration. Currently he is working on a DARPA SBIR project that is developing an integrated modeling and analysis capability to support MBSE. He has worked on a number of government funded research projects during his tenure at Phoenix Integration. He was the technical lead of a NASA NRA project that developed custom data modeling capabilities to support high fidelity analysis and optimization. Prior to that, he led a NASA SBIR project that successfully commercialized advanced design point cloud visualization and design trade-off tools, and also worked on a NASA SBIR that developed a Web-based grid computing and collaborative design optimization environment. Before joining Phoenix Integration, he performed research at the Multidisciplinary Analysis and Design Center of Virginia Tech and Systems Engineering Research Institute in South Korea. He holds a PhD degree in Aerospace Engineering from Virginia Tech, and M.S. and B.S. degrees in Aerospace Engineering from Seoul National University.

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      • Exploration, Analysis, and Decision-Making for Robust Building Design Optimization

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        from EETD Communications / Added

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        An EETD Seminar on Sept. 11, 2013 - Speaker: Jonathan Wright, Loughborough University, UK Model-based optimization and sensitivity analysis techniques provide designers optimized design solutions together with the sensitivity of the design goals to potential changes in a design solution. An analysis of the optimized trade-off between conflicting design goals (these often being energy use and capital investment), and the sensitivity of the goals to changes in the design solution, can result in the robust selection of a design solution for construction. This seminar gives an overview of the relationship between multi-objective design optimization methods and techniques for uncertainty and sensitivity analysis. Read more about this seminar, this speaker, and all EETD Seminars at: http://eetd.lbl.gov/news/seminars Follow EETD on Twitter, get a free subscription to the EETD Newsletter, and visit EETD’s Facebook page: Twitter: @AllanChen -- EETD News http://eetd.lbl.gov/newsletter/ -- Facebook: www.facebook.com/eet.div.lbnl

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        • On A Framework for Data and Specification-Driven Inverse Mulitscale Design of Materials

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          John G. Michopoulos Naval Research Laboratory Host John Baras Abstract We are presenting an overview of a framework for treating material behavior and material properties as design models and variables respectively, in the context of designing materials that need to perform such as they satisfy specific data- and specification-driven performance requirements. After an overview of the general principles and strategies two examples will be presented at the opposite ends of the length scale spectrum. In the atomistic scale level we demonstrate the determination of the Lenard-Jones (L-J) potential parameters that govern the dynamics of a problem of fracture such that a specific load history of the associated medium is satisfied. The problem is approached from an inverse problem perspective. A global Monte Carlo optimizer along with a legacy molecular dynamics code are implemented on Graphical Process Unit computational infrastructure, to compute the design variables of the problem that in this case are the L-J constants. In the macro scale we demonstrate the determination of a general set of material parameters that define the elastic and inelastic (with damage) constitutive response of an anisotropic medium made from fiber-epoxy composite material laminates. The specification requirements to be satisfied are those of a set of load-displacement histories in the full 6+6 dimensional kinematic space. The problem is also approached from an inverse problem perspective. Instead of arbitrarily specifying the load-displacement histories of a material system as the target performance of the system, we used our custom-developed recursive 6-DoF robotic loader to collect the actual kinematic responses of an existing material system. The desired material characterization problem is homomorphic to the problem of determining the material parameters when the behavior is known. The determination of the design (material) constants is achieved by minimizing objective functions such as the difference between experimentally measured and analytically computed system responses as described by strain fields and surface strain energy densities. Small and large strain formulations based on strain energy density decompositions are developed and utilized for determining the constitutive behavior of composite materials. Finally, examples based on both synthetic and actual data demonstrate the successful application of design optimization for constitutive characterization. Biography As a Research Scientist/Engineer and director of Computational Multiphysics Systems Lab (CMSL) of the Center of Computational Materials Sciences at the Naval Research Laboratory (NRL), Dr. Michopoulos oversees multi-physics and information technology (IT) research and development, operations and initiatives at CMSL. Current major initiatives include research and development of linking performance to material trough data and specification driven methodologies, electromagnetic launcher dissipative mechanism modeling and simulation, heterogeneous integrated computational, sensing and communication grids via data-driven multidisciplinary and holistic approaches and environments, engineering sciences research, development and management in areas of computational, theoretical and experimental multiphysics, platform/structure simulation based design, mechatronic/robotic data-driven characterization of continua, automation of research, distributed supercomputing, and multiphysics design optimization. Dr. Michopoulos also currently serves as the Chair of the Computers and Information in Engineering Division of the American Society of Mechanical Engineers. He is a member of the editorial board if the Journal of Computers and Information Science in Engineering and the Journal of Computational Sciences. He is a founding member of the International Science and Technology Outreach Society and prior to joining NRL he has been a senior research scientist for Geo-Centers Inc and prior to that director of the Image Processing Laboratory of the Institute of Fracture and Solid Mechanics at Lehigh University. He has also served as founding member and chief technology officer of three companies. He has authored and co-authored more than 230 publications and books and has been honored with 56 performance and leadership awards. Dr. Michopoulos holds an AS degree in Electronics and Telecommunications from the Capitol Radio Engineering Institute, a M.Sc. in Civil Engineering and a Ph.D in Applied Mathematics from the National Technical University of Athens, Greece, and has pursued post-doctoral studies at Lehigh University Bethlehem PA, on computational multi-field modeling of continua and Fracture Mechanics.

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          • On the development of tools for system design

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            Allessandro Pinto United Technologies Research Center Host John Baras Abstract The design of large systems is divided into vertical refinement steps. The system is also partitioned into sub-systems that are designed in isolation and integrated in the prototyping and testing phase. Many design projects, commercial and military, incur in cost and schedule overruns mainly caused by errors discovered during testing. The correction of such errors typically requires undergoing long re-design cycles. The use of an appropriate methodology supported by tools can help avoiding these problems. In this talk, we present a language for the contract-based specification of components and product families and we show how architectures can be checked for correctness. The language allows capturing static properties that are the ones typically considered in the preliminary design of systems. We then present methods to design and verify systems when dynamics and uncertainty are taken into account. Given the expressiveness required to capture realistic systems, analysis methods do not scale suggesting synthesis approaches as promising techniques to refine a system through abstraction layers. It turns out that the selection of such abstraction layers is hard, mainly due to lack of abstract yet “meaningful” models. Biography Alessandro Pinto is a researcher in the Systems Department at the United Technologies Research Center (UTRC) Inc., Berkeley, California. His research interests are in the area of computer aided design for cyber-physical systems with particular emphasis on autonomous systems. He received a Ph.D. degree in Electrical Engineering and Computer Sciences from the University of California at Berkeley in 2008, and a M.S. degree in Electrical Engineering in 2003 from the same University. He holds a Laurea degree from the University of Rome “La Sapienza”. From 1999 to 2001, he was a consultant at Ericsson Lab Italy in Rome, Italy, working on the design of system-on-chips and wireless access networks.

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