Skills (57)
Research experience
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Jan 2008
Research: Oklahoma State University - Stillwater
Oklahoma State University - StillwaterStillwater · USA -
Jan 2003–
Dec 2008Research: Georgia Institute of Technology
Georgia Institute of Technology · School of Electrical & Computer EngineeringAtlanta · USA
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Teaching: Team 201 - FEDS
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Teaching: 2003-2010 FIRST Robotics Competition Mentor
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Jul 2007
Research: GM/NASA Robonaut 2
General Motors · Advanceed RoboticsAerospace/Automotive Manufacturing
Education
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Aug 1999–
May 2003Georgia Institute of Technology
Controls Systems & DSP · Ph.D. Electrical & Computer EngineeringUnited States of America (USA) · Atlanta, GA
Awards & achievements
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May 2010Award: 2010 IEEE Robotics & Automation Society Early Career Award
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Sep 2007Award: NSF GOALI Award No. 0729552
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Jul 2007Award: Elected IEEE Senior Member
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Mar 2007Award: SME Kuo K. Wang Outstanding Young Manufacturing Engineer Award
Other
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LanguagesEnglish, Spanish, Basic German & French
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Scientific MembershipsIEEE, SME, SAE, SigmaXI, PMI
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Journal RefereesCIRP Journal of Manufacturing Science and Technology, - American Control Conference (ACC), Journal of Stored Products Research, - IEEE Conference on Automation Science and Engineering (CASE), - IEEE Robotics & Automation Magazine (RAM), - IEEE Transactions on Automation Science and Engineering (T-ASE), IRE Transactions on Industrial Electronics, - Journal of Universal Computer Science (JUCS), - Journal on Multi-Sensor/Multi-Source Information Fusion, - Prognostics & Health Management Conference/Journal (PHM)
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Other InterestsFlying, hiking, basketball
Publications (6) View all
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Article: Local recurrence based performance prediction and prognostics in the nonlinear and nonstationary systems.
Pattern Recognition. 01/2011; 44:1834-1840. -
Conference Proceeding: Dynamics and performance modeling of multi-stage manufacturing systems using nonlinear stochastic differential equations
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ABSTRACT: Modern manufacturing enterprises have invested in a variety of sensors and IT infrastructure to increase plant floor information visibility. This offers an unprecedented opportunity to track performances of manufacturing systems from a dynamic, as opposed to static, sense. Conventional static models are inadequate to model manufacturing system performance variations in real-time from these large non-stationary data sources. This paper addresses a physics-based approach to model the performance outputs (e.g., throughputs, uptimes, and yield rates) from a multi-stage manufacturing system. Unlike previous methods, degradation and repair dynamics that influence downtime distributions in such manufacturing systems are explicitly considered. Sigmoid function theory is used to remove discontinuities in the models. The resulting model is validated using real-world datasets acquired from the General Motorpsilas assembly lines, and it is found to capture dynamics of downtime better than traditional exponential distribution based simulation models.Automation Science and Engineering, 2008. CASE 2008. IEEE International Conference on; 09/2008 -
Article: Process control in high-noise environments using a limited number of measurements
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ABSTRACT: Ph.D. Magnus Egerstedt -
Article: Stencil Printing Process Modeling and Control Using Statistical Neural Networks
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ABSTRACT: This paper presents a neural network model for the stencil printing process (SPP) in surface-mount technology (SMT) manufacturing of printed circuit boards (PCBs). A practical model description that decomposes the overall steady-state process in independently modeled subspaces is provided. The neural network model can be updated in real-time procuring a method to control the process by dynamically searching the optimal set point of the control variables. The optimization is performed by minimizing the weighted mean squared error with respect to the desired solder brick height or volume; furthermore, in the case when multiple solutions exist, the set point that yields the lowest variance is used. The process simulator is mainly suitable for offline testing and debugging of more complex closed-loop control algorithms for the SPP optimization providing a common and realistic framework for algorithm performance evaluation. An important consideration in this paper is based on the fact that the estimation of the sampled moments of the probability distributions is made using a statistically significant number of data samples from each board, for each component type, for each printing direction, and for each pad orientation.IEEE Transactions on Electronics Packaging Manufacturing 02/2008; · 1.16 Impact Factor -
Conference Proceeding: Process control in a high-noise environment using a limited number of measurements
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ABSTRACT: In this paper, we develop a hybrid control algorithm that produces control values for processes where only a limited number of function evaluations are available for the control law generation. This situation arises, for example, in stencil printing processes in printed circuit board manufacturing, where the cost associated with multiple function evaluations is prohibitive: The proposed control algorithm is given by a modified version of a constrained conjugated-gradient method, transitioned into a windowed-smoothed block-form of the least-squares affine estimator.American Control Conference, 2003. Proceedings of the 2003; 07/2003
