Informs Annual Meeting 2017

MC63

INFORMS Houston – 2017

MC62

MC63

370C Smart Manufacturing Sponsored: Quality, Statistics and Reliability Sponsored Session Chair: Ran Jin, Virginia Tech, Blacksburg, VA, 24061, United States, jran5@vt.edu 1 - 3D Freeze Assembling Printing of Graphene Aerogel Dong Lin, Assistant Professor, Kansas State University, 2080 Rathbone Hall, 1701 D Platt St., Manhattan, KS, 66506, United States, dongl@ksu.edu The surging field of graphene aerogels (GA) provides a promising methodology for transferring inherent properties of graphene into macroscopic applications for composite materials, energy storage, stress sensor, thermal insulator, and shock damping. However, fabrication of GA with tailored macrostructures by scalable and controllable methods remains a significant challenge. The vision of tailoring macroarchitecture of GA for specific applications, including separation, all-solid- state batteries, micro pressure sensors, flexible electrodes, and electrochemical catalyst templates, has stimulated the research on 3D printing of GA. Here, we propose a printing methodology that contines multinozzle drop-on-demand inkjet printing of pure graphene oxide (GO) suspension with freeze casting for rapid printing of 3D GA architectures to achieve several key qualities, namely, pure, continuous, boundary free, controlled microstructure, and truly 3D architectures (e.g., 3D truss with overhang structures). To date, we have successfully printed world’s lightest material via 3D printing through the proposed technique. 2 - Manufacturing Quality Modeling with Smooth Variable Selection Based on Spatial Predictors Yifu Li, Virginia Tech, Blacksburg, VA, 24060, United States, liyifu@vt.edu Spatial data are widely collected in manufacturing to reflect the product quality information. It is challenging to transform these spatial data into useful information for product quality improvements due to the increasing spatial resolution and defect complexity. Motivated by an aerosol jet® printing process, we propose a smooth variable selection estimator using spatial predictors from high-resolution microscopic images to model printed electronics’ resistances as responses. We demonstrated the effectiveness of the proposed method for prediction and variable selection with simulation studies and a case study. 3 - Data Challenges from Modern Manufacturing System Shan Ba, The Procter & Gamble Company, Cincinnati, OH, United States, ba.s@pg.com Modern manufacturing systems are often equipped with numerous sensors and high-speed cameras, which constantly measure hundreds or thousands of quality and process variables throughout the production process. The high volume and velocity of the incoming sensor/image data streams pose major challenges in data storage, data transfer, and data analysis. Bigger data should bring bigger insights. In this talk, we will discuss several challenges in handling and analyzing manufacturing big data in order to boost quality, minimize waste and gain process insight in a timely manner. 4 - A Statistical Model for Computer Experiments in Smart Additive Manufacturing Xinwei Deng, Department of Statistics, Virginia Tech, 406 Hutcheson Hall, Blacksburg, VA, 24061, United States, xdeng@vt.edu, Jingran Li, Ran Jin For computer experiments in the additive manufacturing, the scanning path in the process often contains valuable information that links the time and space domains. A novel Gaussian process (GP) model is proposed to quantify the surface characteristics, which integrates the distinct effects of the time evolution and spatial distance at the printing process. This proposed GP model is examined on several geometrical designs to evaluate the performance of model prediction and model interpretation. 5 - Smart Manufacturing Ran Jin, Virginia Tech, 1145 Perry Street, Durham Hall, Room 250 (0118), Blacksburg, VA, 24061, United States, jran5@vt.edu Modern manufacturing needs to optimize the entire product lifecycle to satisfy the highly diverse customer needs. With the deployment of Internet of Things, data-driven decision making is expected to enable smart manufacturing to achieve high level of adaptability and flexibility. This talk discusses the recent advancement of methodologies, and the corresponding sensing, analysis and decision making to enable smart manufacturing.

370D Biophysical and Economic Modeling – Assumptions, Metrics, and Decisions Invited: Energy and Climate Invited Session Chair: Carey King, The University of Texas at Austin, careyking@energy.utexas.edu 1 - A Long-term Macroscale Model Linking Biophysical and Economic States Carey King, Energy Institute at The University of Texas at Austin, 2304 Whitis Ave., C2400, Austin, TX, 78712, United States, careyking@mail.utexas.edu New macroeconomic frameworks are needed to properly interpret the post-2008 macroeconomic situation of high debt, low interest rates, and aging populations (primarily in advanced economies). Monetary models of finance and debt often assume that natural resources (energy, food, materials) and technology are not constraints on the economy. Energy scenario models often assume that economic growth, finance and debt will not be constraints on investment in resources. This presentation discusses a new modeling framework that eliminates aforementioned assumptions by integrating states of resources, economy (employment, debt, wages, capital), and population at a fundamental level. 2 - Changing Resource Productivity under Resource Constraints Eric Kemp-Benedict, Senior Scientist, Stockholm Environment Institute, 11 Curtis Avenue, Somerville, MA, 02144, United States, eric.kemp-benedict@sei-international.org Resource constraints are of growing economic importance. Nonrenewable resources have historically been so abundant that productive capacity has mattered more than the total stock, but now that the most accessible resources have been exhausted, extraction is increasingly resource-intensive. Renewable resources, if treated well, can produce indefinitely into the future, but productive potential, once it has been reached, can only expand slowly. Future economic prospects thus increasingly depend on the pace of resource productivity growth. This presentation shows how productivity and output might evolve under resource constraints in a model of cost share-driven technological change. 3 - Beyond GDP: National Accounting in the Age of Resource Depletion Michael Carbajales-Dale, Clemson University, madale@clemson.edu Energy-economy-environment systems analysis (E3SA) is becoming increasingly important in the drive for sustainable development and to better understand the imminent future energy transition. E3SA necessarily spans a multitude of scales and disciplines. This talk will give an overview of the work being done by the E3SA group at Clemson University at three different scales: life cycle assessment of electricity generation technologies, network analysis of the infrastructure and industries, and a physical input-output framework for national or global economies. 4 - How Should we Learn from Models of Complex, Open Systems? Michael Gerst, University of Maryland, 1337 Meridian Place NW, Washington, DC, 20010, United States, mgerst@umd.edu Biophysical and economic models represent complex, open systems. Such systems are difficult to understand; consequently models built to learn about them are necessarily incomplete. We introduce a novel framework to gauge model incompleteness using expert elicitation that links learning about model behavior to expert judgment on target system behavior.

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