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Daniel W. Apley Dept. of Industrial Engineering and
Management Sciences McCormick
School of Engineering and Applied Science 2145 Sheridan Road Evanston IL 60208-3119 office: Technological Institute
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Research and Teaching Overview I am an Associate Professor of Industrial Engineering and Management Sciences
at Northwestern University, Evanston, IL. I obtained B.S., M.S., and Ph.D.
degrees in Mechanical Engineering and an M.S. degree in Electrical
Engineering from the University of Michigan. Prior to joining Northwestern
University in 2003, I served on the faculty of Texas A&M University for
five years. My research interests lie at the interface of engineering
modeling, statistical analysis, and data mining, with applications that
include quantitative six sigma process improvement and manufacturing variation
reduction. Much of my work in these application areas is geared towards
processes in which one is inundated with data, in particular those that are
pervaded by measurement and automation technology. I received an NSF CAREER
award for my research and teaching in this area in 2001. I am a past chair of
the Quality, Statistics & Reliability Section of INFORMS, a past Director
of the Manufacturing and Design Engineering Program at Northwestern, and I
currently serve as an Associate Editor for Technometrics and on the Editorial Board of the Journal of Quality Technology. RESEARCH AND TEACHING OVERVIEW My broad areas of research and teaching interest are: · Engineering statistics · Data mining and statistical learning · Statistical quality control and six sigma variation reduction · Manufacturing process diagnosis and automatic control Much of my research addresses the pervasive problem of how to transform large amounts of data into useful information, solutions to which are often referred to generically as "data mining". One data mining application domain in which I am particularly interested is manufacturing process improvement. Extracting information from manufacturing process data often involves using engineering models, knowledge and information within a statistical framework. I am also interested in representing and quantifying model uncertainty in model-based methods, for the purpose of creating tools that are effective and robust in complex, difficult-to-model processes. In typical six sigma problems on which I work, a manufacturer might measure thousands of different variables related to product quality and the state of each process, including machine signatures and other functional data. These high-dimensional data are collected over time and recorded in massive databases. Often, the research objective is to develop a methodology for discovering and visualizing pieces of information in these large databases that help process engineers systematically identify and eliminate root causes of variation, inconsistency, and poor quality. My research in this area boils down to developing six sigma tools that are designed for modern manufacturing processes in which one is inundated with high-volume, high-dimensional data. My recent research projects have developed and applied these tools to automobile manufacturing, electronics assembly, aerospace structure manufacturing, and integrated circuit fabrication. I also conduct research on the interface between statistical and engineering modeling and data mining in other application domains, such as business intelligence and product design optimization. Recent projects in these areas include developing predictive models for strategic management of credit risk, based on large-scale customer databases; and developing Bayesian statistical methods for design optimization with deterministic computer simulations, such as finite element simulation of vehicle crashworthiness, sheet metal stamping processes, etc. At Northwestern, I teach undergraduate courses in Statistical Methods for Quality Improvement (IEMS 305), Introductory Statistics (IEMS 303), and Intermediate Statistics (IEMS 304), the latter course being an introduction to the statistical tools of data mining. I also teach graduate courses in Quality Engineering (IEMS 428) and Engineering Applications of Data Mining (IEMS 490). Shan, X. and
Apley, D. W., "Blind Identification of Manufacturing Variation Patterns
by Combining Source Separation Criteria," Technometrics, to
appear. Apley, D. W. and
Lee, H. C., "Robustness Comparison of Exponentially Weighted Moving
Average Charts on Autocorrelated Data and on Residuals," Journal of
Quality Technology, to appear. Jiang, W., Shu,
L., and Apley, D. W. "Adaptive CUSUM Procedures with EWMA-based Shift
Estimators," IIE Transactions, to appear. Chin, C. H. and
Apley, D. W., "Performance
and Robustness of Control Charting Methods for Autocorrelated Data,"
Journal of the Korean Institute of Industrial Engineers, 34(2), pp.
122—139, June, 2008. Ding, Y. and
Apley, D. W., "Guidelines
for Placing Additional Sensors to Improve Variation Diagnosis in Assembly
Processes," International Journal of Production Research,
45(23), pp. 5485-5507, December, 2007. Xiong, Y., Chen,
W., Apley, D. W., and Ding, X. "A
Nonstationary Covariance Based Kriging Method for Metamodeling in Engineering
Design," International Journal for Numerical Methods in
Engineering, 71(6), pp. 733-756, August, 2007. Apley, D. W., and
Zhang, F., "Identifying
and Visualizing Nonlinear Variation Patterns in Multivariate Manufacturing
Data", IIE Transactions, 39(6), pp. 691-701, June,
2007. Apley, D. W. and
Chin, C. H., "An
Optimal Filter Design Approach to Statistical Process Control," Journal
of Quality Technology, 39(2), pp. 93-117, April, 2007. Chin, C. H. and
Apley, D. W. "Optimal
Design of Second-Order Linear Filters for Control Charting," Technometrics,
48(3), pp. 337-348, August, 2006. Apley, D. W.,
Liu, J. and Chen, W. "Understanding
the Effects of Model Uncertainty in Robust Design With Computer Experiments,"
ASME Journal of Mechanical Design, 128(4), pp. 945-958, July, 2006. Apley, D. W. and
Ding, Y., "A
Characterization of Diagnosability Conditions for Variance Components
Analysis in Assembly Operations," IEEE Transactions on Automation
Science and Engineering, 2(2), pp. 101-110, April, 2005. Lee, H. Y. and
Apley, D. W. "Diagnosing
Manufacturing Variation Using Second-Order and Fourth-Order Statistics,"
International Journal of Flexible Manufacturing Systems, 16, pp.
45-64, 2004. Apley, D. W. and
Kim, J.B., "Cautious
Control of Industrial Process Variability with Uncertain Input and
Disturbance Model Parameters," Technometrics, 46(2), pp.
188-199, 2004. Ding, Y., Gupta,
A., and Apley, D. W., "Singularity
Issues in Fixture Fault Diagnosis for Multi-Station Assembly Processes,"
ASME Journal of Manufacturing Science and Engineering, 126(1), pp.
200-210, 2004. Apley, D. W.,
"A
Cautious Minimum Variance Controller with ARIMA Disturbances," IIE
Transactions, 36(5), pp. 417-432, 2004. Shiu, B. W.,
Apley, D. W., Ceglarek, D., and Shi, J., "Tolerance
Allocation for Compliant Beam Structure Assemblies," IIE
Transactions, 35(4), pp. 329-342, 2003. Apley, D. W. and
Lee, H. Y., "Identifying
Spatial Variation Patterns in Multivariate Manufacturing Processes: A Blind Separation Approach," Technometrics,
45(3), pp. 220-234, 2003. Apley, D. W. and
Lee, H. C., "Design
of Exponentially Weighted Moving Average Control Charts for Autocorrelated
Processes with Model Uncertainty," Technometrics, 45(3), pp.
187-198, 2003. Tsung, F. and Apley,
D. W., "The
Dynamic T2 Chart for Monitoring Feedback-Controlled
Processes," IIE Transactions, 34(12), pp. 1043-1053,
2002. Received the 2002-2003 IIE Transactions Best Paper
Award for Quality and Reliability. Apley, D. W.,
"Time
Series Control Charts in the Presence of Model Uncertainty," ASME
Journal of Manufacturing Science and Engineering, 124(4), pp. 891-898,
2002. Shu, L., Apley,
D.W., and Tsung, F. "Autocorrelated
Process Monitoring Using Triggered Cuscore Charts," Quality and
Reliability Engineering International, 18(5), pp. 411-421, 2002. Apley, D. W. and
Tsung, F., "The
Autoregressive T2 Chart for Monitoring
Univariate Autocorrelated Processes," Journal of Quality
Technology, 34(1), pp. 80-96, 2002. Apley, D. W. and
Shi, J., "A
Factor Analysis Method for Diagnosing Variability in Multivariate
Manufacturing Processes," Technometrics, 43(1), pp. 84-95,
2001. Apley, D. W. and
Shi, J., "The
GLRT for Statistical Process Control of Autocorrelated Processes," IIE
Transactions, 31(12), pp. 1123-1134, 1999. Apley, D. W. and
Shi, J., "An
Order Downdating Algorithm for Tracking System Order and Parameters in
Recursive Least Squares Identification," IEEE Transactions on Signal
Processing, 47(11), pp. 3134-3137, 1999. Apley, D. W. and
Shi, J., "Diagnosis
of Multiple Fixture Faults in Panel Assembly," ASME Journal of
Manufacturing Science and Engineering, 120(4), pp. 793-801, 1998. Shi, J. and
Apley, D. W., "A
Suboptimal N-step-ahead Cautious Controller for Adaptive Control Applications,"
ASME Journal of Dynamic Systems, Measurement, and Control, 120(3), pp.
419-423, 1998. Apley, D. W.,
Seliger, G., Voit, L., and Shi, J., "Diagnostics
in Disassembly Unscrewing Operations," International Journal of
Flexible Manufacturing Systems, 10(2), pp. 111-128, 1998. |