2015 Informs Annual Meeting

SB73

INFORMS Philadelphia – 2015

SB73 73-Room 203B, CC Data Analytics for Quality Control and Improvement II Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Xi Zhang, Assistant Professor, Peking University, 5 Yiheyuan Rd., Beijing, 100871, China, xi.zhang@pku.edu.cn Co-Chair: Kaibo Liu, Assitant Professor, UW-Madison, 1513 University Avenue, Madison, WI, 53706, United States of America, kliu8@wisc.edu 1 - Multistage Process Monitoring through Mewma Control Chart with Generalized Smoothing Parameters Sangahn Kim, Rutgers University, 96 Frelinghuysen Road CoRE Building, Room 201, Piscataway, NJ, 08854, United States of America, sk1389@scarletmail.rutgers.edu, Myong K (MK) Jeong, Elsayed Elsayed The multivariate exponentially weighted moving average (MEWMA) control chart is effective in monitoring a multistage process when the residual is applied to remove variance propagation effect. In this paper, we propose a generalized model of multivariate EWMA, which uses appropriate non-diagonal elements in the smoothing matrix based on the correlation between stages and within a stage, and suggest an optimal design for the proposed chart. 2 - Modeling In-Process Data of Machining Operations: Time Series vs. Spatial Point Cloud Mohammed Shafae, PhD Candidate, Virginia Tech, 112 Durham Traditional approaches for analyzing machining data revolve around representing them as time-series. The world of time-series analysis has provided several techniques for data analysis. However, they are implemented without asking, “Is time-series the most appropriate way to represent this data, and if not, how should the data be represented and what techniques need to be developed to analyze this alternate representation?” Exploring the answer to this question is the focus of this presentation. 3 - Separation and Prognostics of HRV Based on a Kernel-distance-based Multivariate Control Chart Lili Chen, Peking University, 5 Yiheyuan Rd., Beijing 100871, China, chenlili@coe.pku.edu.cn, Xi Zhang Isolating component signals directly from the observed heart rate variability signals can be a challenge. In this article, a signal separation based on integration of EMD and ICA is developed to separate the frequency components in HRV. A prognostics framework with Kernel-distance based multivariate control chart for disease detection has been developed to monitor the component signals. A real case study demonstrates the effectiveness of the proposed method. 4 - Data Fusion Approach for Degradation Modeling and Prognostics with Multiple Failure Modes Abdallah Chehade, UW-Madison, 1513 University Avenue, Madison, 53706, United States of America, chehade@wisc.edu, Xi Zhang, Kaibo Liu Operating units often suffer from multiple modes of failure in practice. This study proposes a data fusion approach to online classify the failure mode of the operating unit based on multiple degradation-based sensor data and then predict the remaining lifetime of the unit. A case study that involves a degradation dataset of aircraft gas turbine engines is used to numerically evaluate and compare the performance of the developed method with existing literature. Hall, Blacksburg, VA, 24061, United States of America, shafae1@vt.edu, Lee Wells, Jaime Camelio, Marco Ferreira QSR Refereed Research Session Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Haitao Liao, Associate Professor, University of Arizona, University of Arizona, Tucson, AZ, 85716, United States of America, hliao@email.arizona.edu Co- Chair: Associate Professor, Pennsylvania State University, 310 Leonhard Building, Industrial and Manufacturing Eng., State College PA 16801, United States of America, huy25@psu.edu Co-Chair: Tirthankar Dasgupta, Associate Professor, Harvard University, Department of Statistics, 1 Oxford Street, 7th Floor, Cambridge MA 02138, United States of America, dasgupta@stat.harvard.edu SB74 74-Room 204A, CC

1 - QSR Refereed Research Session Hui Yang, Associate Professor, Pennsylvania State University, 310 Leonhard Building, Industrial and Manufacturing Eng., State College, PA, 16801, United States of America, huy25@psu.edu The papers in this refereed session are selected after a rigorous peer-review process. Four finalists will make presentations in the QSR refereed session. The winner of best paper award will be announced at the QSR business meeting during the conference.

SB75 75-Room 204B, CC

Advanced Manufacturing I Cluster: Advanced Manufacturing Invited Session

Chair: Yuan-Shin Lee, Professor, North Carolina State University, 400 Daniels Hall, Department of Industri, Raleigh, NC, 27695, United States of America, yslee@ncsu.edu 1 - Supply Chain Design Implications of Modular Production Systems Satya Malladi, Graduate Student, Georgia Institute of Technology, 755 Ferst Drive NW, ISyE,, Atlanta, GA, 30332, United States of America, mss@gatech.edu, Alan Erera, Chelsea White Modular/mobile production systems allow production capacity to be transported closer to demand. We address the following questions: When should some or all production capacity be modular? When should spatial changes in customer demand result in logistics adjustments only and when should it also be met by relocating modular production capacity? 2 - Regenerative Medicine Manufacturing – Challenges and Tools for Scale-up and Scale-out Regenerative medicine technologies continue to be developed and successfully tested in labs, but their translation to commercial-scale viable production continues to be a significant challenge. This talk will discuss the current state of the art, hurdles in bench to bedside translation, and manufacturing and systems engineering tools and approaches that can be used to enable efficient scale-up and scale-out. 3 - A Simulation Model of the EBM Additive Manufacturing Process Richard Wysk, Dopaco Distinguished Professor, North Carolina State University, 400 Daniels Hall, 111 Lampe Dr, Raleigh, NC, 27606, United States of America, rawysk@ncsu.edu, Michael Blum, Ismail Lekorchi, Ola Harryson, Kali Drake, Christopher Kelly, Timothy Horn Electron beam melting is a 3D printing process that has deficiencies that need improvement. This paper defines, and analyzes each step in the EBM process. A simulation model of the process is created to determine time required to process a part. This model focuses on the processing steps required to bring about the production of mechanical metal parts. An investigation to improve the efficiency of each step has been created to model this additive process. This model and process is described. 4 - Smart Machining and Manufacturing Systems Yuan-Shin Lee, Professor, North Carolina State University, 400 Daniels Hall, Department of Industri, Raleigh, NC, 27695, United States of America, yslee@ncsu.edu With recent advancements in manufacturing process, information technology, sensing and automation technologies, it is envisioned a new kind of smart manufacturing that can locally deliver customized products with high quality bu with the cost structure of a mass manufactured product. One of the major building block is the new generation smart CNC machines. In this talk, we will discuss the new generation smart machines and manufacturing systems. Rohan Shirwaiker, North Carolina State University, 406 Daniels Hall, Raleigh NC 27607, United States of America,rashirwa@ncsu.edu, Binil Starly

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