A Journey of Learning from Statistics to Manufacturing, Logistics, Engineering Design and to Information Technology - PowerPoint PPT Presentation

Loading...

PPT – A Journey of Learning from Statistics to Manufacturing, Logistics, Engineering Design and to Information Technology PowerPoint presentation | free to download - id: 669802-YzJlZ



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

A Journey of Learning from Statistics to Manufacturing, Logistics, Engineering Design and to Information Technology

Description:

A Journey of Learning from Statistics to Manufacturing, Logistics, Engineering Design and to Information Technology Professor J.-C. Lu Industrial and Systems Engineering – PowerPoint PPT presentation

Number of Views:39
Avg rating:3.0/5.0

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: A Journey of Learning from Statistics to Manufacturing, Logistics, Engineering Design and to Information Technology


1
A Journey of Learning from Statistics to
Manufacturing, Logistics, Engineering Design and
to Information Technology
  • Professor J.-C. Lu
  • Industrial and Systems Engineering
  • Georgia Institute of Technology

2
Contents
  • Introduction
  • Statistics in Reliability
  • Quality Improvement in Manufacturing
  • Data Mining in Manufacturing
  • Product Design, Manufacturing and Service Chain
    Management System
  • Information Technology in Education

3
1. Introduction
  • Traditional Research Approach
  • Non-Traditional Research Methods

Application 1
Thesis Background
New Methods
Application 2
New Areas

Modifications
Extensions
Application k
4
Non-Traditional Research Approach
Best Practice
Real-life Problems
Practical Problem Solving
Business
Team-work
Application-oriented Literature
Cross-disciplines
Academic Problem Formulation
Discipline-focused
Literature Review
Academia
New Methods or New Areas in Research
Impact Analysis
Time
5
2. Statistics in Reliability
Traditional Research Approach Lu, J. C. (1989),
Weibull Extensions of the Freund and
Marshall-Olkin Bivariate Exponential, IEEE
Transaction on Reliability, 38, 5, 615- 619. Lu,
J. C. and Bhattacharyya, G. K. (1990), Some New
Constructions of Bivariate Weibull Models,
Annals of the Institute of Statistical
Mathematics, 42(3), 543-559. Lu, J. C. (1990),
Least Squares Estimation for the Multivariate
Weibull Model of Hougaard Based on Accelerated
Life Test of System and Component,
Communication in Statistics, 19(10),
3725-3739. Lu, J. C. and Bhattacharyya, G. K.
(1991), Inference Procedures for a Bivariate
Exponential Model of Gumbel Based on Life Test of
System and Components, Journal of Statistical
Planning and Inference, 27, 383-396. Lu, J. C.
and Bhattacharyya, G. K. (1991), Inference
Procedures for a Bivariate Exponential Model of
Gumbel, Statistics and Probability Letters, 12,
37-50.
6
Lu, J. C. (1997), A New Plan for Life-Testing
Two-Component Parallel Systems, Statistics and
Probability Letters, 34(1), 19-32.
x
x
x
x
x
(1)
(2)
(r)
(r1)
(n)

y
y

y
y
y


1
2


r
r1
.
n
x
The life-testing experiment was terminated at
(r),
x
and data with superscript are censored at
(r).
x
x
x
are ordered statistics,
,
lt
lt
(r)
(1)
(2)
y
y
y
are concomitant ordered statistics.
,
,
,
r
1
2
7
Sample Publications from the Traditional Research
Approach Chen, D., and Lu, J. C. (1998), The
Asymptotics of Maximum Likelihood Estimates of
Parameters Based on a Data Type Where Failure
and Censoring Times are Dependent, Statistics
and Probability Letters, 36, 379-391. Chen, D.,
Li. C. S., Lu, J. C., and Park, J. (2000),
Simple Parameter Estimation for Bivariate Shock
Models with Singular Distribution for Censored
Data with Concomitant Order Statistics,
Australian and New Zealand Journal of
Statistics, 42(3), 323-336.
Non-traditional Research Approaches
A. Start to work with Nortel in the printed
circuit board (PCB) manufacturing area in 1989.
Get the 1st Nortel grant in 1990. Publish the
1st paper (in JASA case study) in 1994.
B. Start to work with NCSUs Semiconductor Center
in 1990. Early publications appeared in 1991
(Proceedings), 1993 (engineering journal) and
1997 (statistics journal).
8
Reliability Degradation Studies (First example of
the Non-traditional Research Approach) Lu, J.
C., Park, J. and Yang, Q. (1997), Statistical
Inference of a Time-to-Failure Distribution
from Linear Degradation Data, Technometrics,
39(4), 391-400. Su, C., Lu, J. C., Chen, D., and
Hughes-Oliver, J. M. (1999), A Linear Random
Coefficient Degradation Model with Random Sample
Size, Lifetime Data Analysis, 5, 173-183. Chen,
D., Lu, J. C., X. Huo, and Ming, Y. (2001),
Optimum Percentile Estimating Equations for
Nonlinear Random Coefficient Models, Journal
of Statistical Planning and Inference,275-292. NS
F DMII-ORPS Program, Modeling Accelerated
Degradation Data for Product Reliability
Improvement and Warranty Analysis, 2001- 2003
(with Paul Kvam).
9
Linear Degradation Model (semiconductor manufactu
ring)
y
?
?
?

log(t
)

,

ij
ij
ij
1i
0i
i 1, 2, , k (replicates), j 1, 2, ,
n
(successive repeated measurements),
i
y

current, threshold voltage shift or
transconductance degradation,
ij
t
time.

ij
10
Linear Random Coefficient Model
?
?
have a bivariate normal distribution
Assume
and
0
1
2
2
with mean (?
, ?
and correlation ?.
), variance (
?
, ?
)
0
1
0
1
Define the failure time T as the time that the
degradation reaches a specified level y
, and set
?
?
y

T
.
f

f
0
1
?
?
( y

)/
The distribution of the failure time T
is
f
0
1
?
?
Pr( T ? t ) Pr( ( y

)/
lt t )
f
0
1
?
t ?

y
and
?
? A / B , where A
0
1
f
2
2
2
t
2 t ?
B sqrt(C), C
?
?

?
?
.

0
1
0
1
11
Non-linear Degradation Model (motivated from
both semiconductor and PCB manufacturing
studies)
, ?
) ?
?
? b
Y

f ( X
,
(random effects).
i
i
i
i
i
i
) ?
Note that E(
Y
f ( X
, E( ?
))

f ( X
, ?
).
i
i
i
i
Thus,
f ( X
, ?
)
is not the mean response of the population,
i
Y
and may not be the median of the distribution of
i
even when zero is the distribution mean of errors

?
.
i
By correcting the bias of the median regression,
estimates of ? were obtained from solving a
system of (optimum) unbiased percentile
estimating equations (PEE). The
asymptotic distribution of the estimates was
derived. Several examples of asymptotic
efficiency evaluations were given.
12
3. Quality Improvement in Manufacturing
Non-Traditional Research (examples) Mesenbrink,
P., Lu, J. C., McKenzie, R., and Taheri, J.
(1994), Characterization and Optimization of a
Wave Soldering Process, Journal of the American
Statistical Association (JASA), 89,
1209-1217. Gardner, M. M., Lu, J. C., et al.
(NCSU ECE and TI researchers) (1997), Equipment
Fault Detection using Spatial Signatures, IEEE
Trans. on Components, Hybrids and Manufacturing,
20(4), 295-304. Hughes-Oliver, J. M., Lu, J. C.,
Davis, J. C., and Gyurcsik, R. S. (1998),
Achieving Uniformity in a Semiconductor
Fabrication Process using Spatial Modeling,
JASA, 93, 36-45. Lu, J. C., et al. (SRC
(semiconductor research corporation) and NCSU ECE
people) (1998), A New Device Design
Methodology, IEEE Trans. on Electron Devices -
Special Issue on Process Integration and
Manufacturability, 45(3), 634-642. Li, C. S.,
Lu, J. C., Park, J., Kim, K. M., Brinkley, P. A.,
and Peterson, J. (1999), A Multivariate
Zero-inflated Poisson Distribution and its
Inferences, Technometrics, 41(1), 29-38.
13
4. Data Mining in Manufacturing
Rying, E. A. Bilbro, G. L. Ozturk, M. C., and Lu,
J. C. (2000), In Situ Selectivity and
Thickness Monitoring based on Quadrupole Mass
Spectroscopy during Selective Silicon Epitaxy,
Proceedings of the 197th Meetings of the
Electronchemical Society, 383-392. Lu, J. C.
(2001), Methodology of Mining Massive Data Set
for Improving Manufacturing Quality/Efficiency,
Chapter 11 (pp. 255-288) in Data Mining for
Design and Manufacturing edited by D. Braha,
Kluwer Academic Publishers New York. Lada, E.
K., Lu, J. C., and Wilson, J. R. (2002), A
Wavelet Based Procedure for Process Fault
Detection, IEEE Trans. on Semiconductor
Manufacturing, 15(1), 79-90. Rying, E. A.,
Bilbro, G. L., and Lu, J. C. (in press), Focused
Local Learning with Wavelet Neural Networks,
IEEE Trans. on Neural Networks. Porter, A. L.,
Kongthon, A., and Lu, J. C. (in press), Research
Profiling Improving the Literature Review
Illustrated for the Case of Data Mining of Large
Datasets, Scientometrics.
14
Data from Nortels Antenna Manufacturing Process
15
(No Transcript)
16
Discrete Wavelet Transform
17
Data Reduction Procedures
  • Linear and Nonlinear Approximation in Signal
    Processing
  • Information Metric Based Procedures
  • Data Denoising Procedures
  • Our Methods RRE_h and RRE_s
  • Comparisons
  • Testing Curves
  • Data without Noises
  • Data with Inherent Random Noises

18
Linear and Nonlinear Approximation in Signal
Processing
19
Information Metric Based Procedure
AMDL (Approximation Minimum Description Length)
Saitos (1994) method selects C to minimize

2
? (
y
.
AMDL(C) 1.5 C log
y
N 0.5 N log

)
N
i
2
2
i,C
i 1
Data De-noising Procedures
Donoho and Johnstone (1995) considered the
nonparametric regression model,
y
,
?
?
f
i 1, 2, , N, where
i
i
i
i
are i.i.d. normal variables with zero mean and
constant variance. The goal of the data
de-noising procedures is to find a
smooth estimate to minimize the mean square error
(MSE). Three methods,VisuShrink, RiskShrink and
SURE (Steins Unbiased Risk Estimate) were
compared in our studies.
20
Seven Testing Curves, Two Real-life Data Examples
21
Comparison Results (Data without Noise)
22
Comparison Results (Data with Inherent Random
Noises)
23
Decision Rules (based on the reduced-size data)
  • Chi-square tests
  • Multi-scale Statistical Process Control (SPC)
  • (Functional) Principal Component Analysis (PCA)
  • Bayesian Odds-ratio Probability-based
    Classification (and Canonical Variation Analysis)
  • Decision Tree (CART)
  • Scalogram (from Signal Processing Literature)
  • Integrated Energy Metrics

24
Scalogram
Challenges derive the distribution of the
energy,
2
E
I ( w
?
, where ? is decided from the

? ? ) w
j
jk
jk
k
is the wavelet coefficient.
data reduction method, and w
jk
25
Key Challenges in Data Mining Procedures in
Manufacturing Applications
The replication size in fault classes is small.
Proposal generating learning data
Example Rying (2001) conducted 25 runs of RTCVD
experiments with four induced fault cases.
Nominal Runs
26
Four Induced Fault Cases
27
Challenges in Learning-data Generations
1. Difficult to generate the data shifting
patterns (e.g., Ryings nominal data) at the
wavelet domain, which has a much smaller size
of data to deal with compared to the
original data domain with possible large size
data.
Idea Zoom-in the regions that fault
data patterns occurred, and generate the
shifted- data at the original data domain in
these focused regions.
28
Illustration Example
Zoom-in Procedure
29
Generate Replicates in the Wavelet Domain with
the following Patching Technique
30
5. Product Design, Manufacturing and
Service (PDMS) Chain Management System
Initiatives in iTimes (Information
Technology Integrated Manufacturing Enterprise
System)
31
Current Involvement in iTimes
(1) developing a collaborative game theory based
decision support system for structuring
interactions among partners in the ePDMS
chain, e.g., random coefficient based evolution
modeling of utility functions changing over the
co-developing periods) (2) extracting
design-relevant relationships from data
collected from various sources, e.g., past
designs, conditions of machines on the factory
floor at distributed sites, etc. (3)
monitoring and controlling resource (e.g.,
energy) utilization and environmental impact.
32
Challenges in Data Mining on Product Design
(1) Retrieving past design information
How to define similarity in 3-D geometric
objects with spatial relationships?
Is it possible to develop a multi-resolution pr
esentation of design models or data?
(2) Source of variation in design
(3) Relationship between design,
manufacturing and service activities.
33
Analysis Models of Varying Fidelity
34
(No Transcript)
35
6. Information Technology in Education
36
(No Transcript)
About PowerShow.com