Quality Improvement: from Autos and Chips to Nano and Bio
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Transcript of Quality Improvement: from Autos and Chips to Nano and Bio
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C. F. Jeff WuSchool of Industrial and Systems Engineering
Georgia Institute of Technology
Quality Improvement: from Autos and Chips to Nano and Bio
• Legacies of Shewhart and Deming.• Quality improvement via robust parameter design: Taguchi’s origin in manufacturing. • Extensions of RPD: operating windows and
feedback control. • Incorporation of physical knowledge/data. • Advanced manufacturing: new concept/paradigm?
Sample
Sam
ple
Mean
151413121110987654321
25.0
22.5
20.0
17.5
15.0
__X
UCL
LCL
Toyota
Sample
Sam
ple
Mean
151413121110987654321
25.0
22.5
20.0
17.5
15.0
__X
UCL
LCL
GM
• Developed statistical process control (SPC) to quickly detect if a process is out of control. Classify process variability into two types.
• Common (chance) causes: natural variation, in control.
Shewhart’s Paradigm
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• Developed statistical process control (SPC) to quickly detect if a process is out of control. Classify process variability into two types.
• Common (chance) causes: natural variation, in control.• Special (assignable) causes: suggests process out of control.
Shewhart’s Paradigm
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Sample
Sam
ple
Mean
2321191715131197531
22
21
20
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__X
UCL
LCL
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Walter Shewhart
• American physicist, mathematician, statistician. Developed SPC while working for Western Electric (Bell Telephone). Original 1924 work in one page memo, 1/3 of which contains a control chart. Background: to tackle manufacturing variation.
• SPC should be viewed more as a scientific methodology, than a charting technique.
• Deming was introduced to Shewhart in 1927; was tremendously influenced by the SPC methodology; Deming’s key insight: Shewhart’s SPC can also be applied to enterprises; this led to his later work and big impact in quality management.
Deming’s Statistical Legacies
• As Shewhart, Deming was a physicist, mathematician, statistician. He studied statistics with Fisher and Neyman in 1936.
• He edited the book “Statistical Method from the Viewpoint of Quality Control” in 1939.
• He devised sampling techniques used in the 1940 Census, developed the Deming-Stephan algorithm, an early work on iterative proportional fitting in categorical data.
• His bigger impact in quality management started with his visits and lectures in Japan in 1950’s.
Design of Experiments (DOE)
• If a process is in control but with low process capability, use DOE to further reduce process variation. Pioneering work by Fisher, Yates, Finney, etc. before WWII.
• DOE in industries was widely used after the war; George Box’s work on Response Surface Methodology.
• Genichi Taguchi’s ( ) pioneering work on robust parameter design. Paradigm shift: use DOE for variation reduction, which is the major focus of my talk.
Robust Parameter Design• Statistical/engineering method for product/process
improvement (G. Taguchi), introduced to the US in mid-80s. Has made considerable impacts in manufacturing (autos and chips); later work in other industries.
• Two types of factors in a system:– control factors: once chosen, values remain fixed;– noise factors: hard-to-control during normal process
or usage.• Parameter design: choose control factor settings to
make response less sensitive (i.e. more robust) to noise variation; exploiting control-by-noise interactions.
Y=f(X,Z)
Noise Variation (Z) Response Variation (Y)
Control X=X1
Y=f(X,Z)
Noise Variation (Z)
Control X=X1
Response Variation (Y)
Traditional Variation Reduction
Y=f(X,Z)
Noise Variation (Z) Response Variation (Y)
X=X1
Robust Parameter Design
Y=f(X,Z)
Noise Variation (Z) Response Variation (Y)
X=X1→ X=X2
Robust Parameter Design
Variation Reduction through Robust Parameter Design
Shift from Traditional Strategy
• Emphasis shifts from location effect estimation to dispersion effect estimation and variation reduction.
• Control and noise factors treated differently: C×N interaction treated equally important as main effects C and N, which violates the effect hierarchy principle. This has led to a different/new design theory.
• Another emphasis: use of performance measure , including log variance or Taguchi’s idiosyncratic
signal-to-noise ratios, for system optimization. Has an impact on data analysis strategy.
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(b)
0 mV
-42
-21
-14
-28
-35
-7
020
µmµm
-42 mV
x y
z2 µm
(a)
RL
(c)
Robust optimization of the output voltage of nanogenerators
Nano Research 2010 (Stat-Material work at GT)
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Experimental Design
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Control factors Noise factor
New setting is more robust
0 10 20 30 40 500
10
20
30
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Number of Scans
Mea
n V
alue
of E
lect
rical
Pul
ses
(mV
)
120nN,30 m/s137nN,40 m/s
µµ
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Further Work Inspired by Robust Parameter Design
Two examples:• The method of operating windows to widen
the designer’s capability. • RPD combined with feedback control, both
offline and online adjustments.
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Method of Operating Window (OW)• Operating window is defined as the boundaries of a critical
parameter at which certain failure modes are excited. Originally developed by D. Clausing (1994 and earlier) at Xerox, Taguchi (1993).
• Approach:� Identify a critical parameter: low values of which lead to
one failure mode and high values lead to the other failure mode.
� Measure the operating window at different design settings.� Choose a design to maximize the operating window.
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Paper Feeder Example
Two failure modes• Misfeed : fails to feed a sheet• Multifeed: Feeds more than one sheet
Standard Approach
• Feed, say, 1000 sheets at a design setting; observe # of misfeeds and # of multifeeds; repeat for other settings; choose a design setting to minimize both.
• Problems: require large number of tests to achieve good statistical power; difficult to distinguish between different design settings; conflicting choice of levels (settings that minimize misfeeds tend to increase multifeeds).
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OW Approach in Paper Feeder ExampleStack force is a critical parameter and is easy to measure. A small force leads to misfeed and a large force leads to multifeed.
misfeed operating window multifeed
0 l u stack force
(l, u): operating windowStack force: operating window factor
• No clear boundaries separating the failure modes • Can be defined with respect to a threshold failure rate: l = force at which 50% misfeed occurs, u = force at which 50% multifeed occurs.
• 1. Find a control factor setting to maximize the signal-to-noise ratio
where N1, N2,… represent noise factor conditions.2. Adjust OW factor to the middle of the operating window.
• But the method lacks a sound justification.
N1:0
0
0
N2:
N3:
Operating window
𝑙1
𝑙2
𝑙3
𝑢1
𝑢2
𝑢3
Taguchi’s Two-Step Procedure
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A Rigorous Statistical Approach to OW
• Under some probability models for the failure modes and a specific loss function, Joseph-Wu (2002) showed that a rigorous two-step optimization leads to a performance measure similar to Taguchi’s SN ratio. The procedure also allows modeling and estimation, in addition to design optimization. See the illustration with paper feeder experiment.
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Factors and LevelsControl factors Notation Levels
1 2 3 Feed belt material A Type A Type B -
Speed B 288 240 192 Drop height C 3 2 1 Center roll D Absent Present -
Width E 10 20 30 Guidance angle F 0 14 28
Tip angle G 0 3.5 7 Turf H 0 1 2
Noise factors Stack quantity N Full Low -
Joseph-Wu, 2004, TechnometricsData, courtesy of Dr. K. Tatebayashi of Fuji-Xerox.
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Optimization
old
newmisfeed multifeed
Analysis led to new design with wider operating window.
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Examples of Operating Window FactorsProcess/ Product
Failure or defect type1 2
Operating windowfactor
Wave soldering
Voids Bridges Temperature
Resistance welding
Under weld
Expulsion Time
Image transfer Opens Shorts Exposure energy
Threading Loose Tight Depth of cut
Picture printing
Black Blur Water quantity
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Robust Parameter Design With Feedback Control
Dasgupta and Wu, 2006, Technometrics
• To develop a unified and integrated approach to obtain the best control strategy using parameter design. RPD with feedfoward control, Joseph (2003, Technometrics).
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Strategies for minimizing effect of noise on output
Robust parameter design Process Adjustment
Feedforward Control Feedback Control
(One-time activity;Limited applicability)
(Continuous activity;Wider applicability)
Offline and Online Reduction of Variation
Measure the noise Change adjustment factor
Measure the output Change adjustment factor
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Control factors:
Noise factors:
X1, X2, .., Xp
Adjustment Factor:
N1, N2, .., Nq
Ct
CONTROL EQUATION : Ct = f(et, et-1, …)
OUTPUT:
Yt = b(X,N,Ct-1 ,Ct-2 , …) + zt
Process dynamics
Process disturbance
Output error: et = Yt - target
Feedback Control with Control and Noise Factors
Functional form
FIND OPTIMAL SETTINGS OF :
X1, X2, .., Xp
PARAMATERS OF f
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An Example: the Packing Experiment
Target weight = 50 lbMain (course) feed = 38 lbDribble (fine) feed = 12 lb
C = 0
X (14 control factors)N (material composition)
Sampled bag weight (Y) = 49.5 lb
error = 49.5-50 = -0.5 lb
-C = kI (-0.5) = (0.1 ) (-0.5) = -0.05 lb
49.6
49.7
49.8
49.9
50
50.1
50.2
50.3
50.4
1 4 7 10
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40
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C = 0.05
38.05 lb
11.95 lb
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Results and Benefits
• Optimum combination selected using plots and fitted model.
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10
152025
30354045
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49.6 49.7 49.8 49.9 50 50.1 50.2 50.3 50.4 50.550.4250.3450.2650.1850.1050.0249.9449.8649.7849.70
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20
10
0
Fre
que
ncy
BEFORE ...
50.550.450.350.250.150.049.949.849.749.6
30
20
10
0
Fre
que
ncy
AFTER ...
Prior to experimentation
s = 0.121
What was achieveds = 0.031
(Dasgupta et al. 2002)
What could have been achieved
s = 0.0159
In-Process Quality Improvement (IPQI)
ManufacturingDesign Measurement End Product Shipping
ConceptEvaluation
QualityManagement
(Designed Experiments) (SPC Techniques)
(Deming’s QC Philosophy)
IPQIApproach developed by Jan Shi (GT), IPQI slides courtesy of Shi
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Example of IPQI: Knowledge-based Diagnosis for Auto Body Assembly
Z
XY
On-line monitoring
Data BaseKnowledge Base
Process Navigator
Human- Computer Interface
Diagnosis Reasoning
Variation Animation
Root Causes
1. Engineering: Hierarchical Structure Model of Assembly Product/Process2. Statistical: Correlation, clustering, hypothesis testing
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Manifestation of a single fault
M1M2
Z
Y
C1 C2
C3
M3
P2P1
X
Fault Fault Pattern
P1
P2
C1
C2
C3
O
O
O
O
OO
P: PinC: ClampM: Measurement point
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Engineering analysis by rigid body motion
Fusion of Knowledge and Data
Principal Component Analysis (PCA)
•
Relationship between PCA and Fixture Fault Pattern
•
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Other Engineering Examples of IPQI
Manufacturing Process Statistical methods Engineering knowledge
Tonnage signature analysis in forming
Wavelet analysis Time and frequency information due to press and die design
Wafer profile modeling and analysis in wire slicing
Gaussian Process model Dynamics model of wire slicing operations
On-line bleeds detection in continuous casting
Imaging feature extraction and design of experiments
Mechanism of bleeds formation in casting
Variation modeling and analysis for multistage wafer manufacturing
Data mining and probability network modeling
System layout and system design information
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From Knowledge to Data:Physical-Statistical Modeling
• Simulation experiments have been widely used in lieu of physical experiments. The latter are more expensive, time-consuming or only observed when events like flooding suddenly happen. SE can be an indispensible tool in quality improvement, especially for paucity of physical data or low failure rates.
• Example: validation of finite element experiment with limited physical data in fatigue life prediction of solder bumps in electronic packaging of chips.
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Convex Up (+) Concave Up (-)
Effects of Warpage on Solder Bump Fatigue
Tan-Ume-Hung-Wu, 2010, IEEE Tran. Advanced Packaging
• PWB samples can have different initial warpage or can be flat.
─ PWBA warpage can be either convex or concave as shown below:
• Two packages (27x27-mm, 35x35-mm) ─ Each package placed at three different locations:
Location 1 Location 2 Location 3
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Factors studied in Finite Element Method (FEM)
• Factors:
maxw
shapew
pd
pl
sm
fN = fatigue life estimation of solder bumps (cycles)
maximum initial PWB warpage at 25C (mm)
2105.3, 3076.6, 3824.0
warpage shape +1: Convex up; -1 Concave up
package dimension (mm) 27 by 27, 35 by 35
location of package (mm) Center, 60-30, Outmost
solder bump material Sn-Pb, Lead-free
84 FEM runs were conducted.
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Objective: To verify and correlate 3-D finite element simulation results.
PWB with 3535 mm PBGA at Location 2
PWB with 3535 mm PBGA at Location 4 Standard Thermal Cycling Profile
Accelerated Thermal Cycling Test
Experimental Study of Solder Bump Fatigue Reliability Affected by Initial PWB Warpage
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FEM Simulation vs Experimental Study
-4000 -2000 0 2000 4000
14
00
16
00
18
00
20
00
22
00
24
00
26
00
28
00
angle
y
maximum PWB warpage
Fat
igue
life
(C
ycle
)
FEM Simulations
Experimental Data
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Integration of FEM and Physical Data• Use kriging to model the FEM data:
• Calibrate the fitted model with experimental data, leading to
1ˆ ( ) 1101.6 ( ) ( 1101.6 ),Tk outN x x N I
where = FEM output data.
where = fatigue life prediction, = maximum initial PWB warpage at 25C.
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Validation of Kriging Model
Case
Max. Initial Warpage
across PWB at Room
Temp. (mm)
PBGADimensio
n(mm)
Distance from
PBGA Center to
Board Center (mm)
Fatigue Life from Prediction Model (Cycles)
Experimental Fatigue
Life (Cycles)
Difference
1 -1.833 27 27 67 2633 2750 -4.3 %
2 -2.013 27 27 30 2465 2625 -6.1 %
3 -2.171 35 35 67 2507 2400 4.4 %
4 -2.425 35 35 30 2277 2250 1.2 %
• Compare experimental fatigue life with kriging model prediction under four untried settings. Outperforms FEM prediction.
Challenges in Advanced Manufacturing
• Typical features: small volume, many varieties, high values. Recent example: additive manufacturing (3D printing). Parts made-on-demand as in battle fields. Situation more extreme than run-to-run control in semi-conductor industries.
• Scalable manufacturing process: from lab, to pilot, to mass scale production; bio-inspired materials (next slides).
• What new concepts and techniques are needed to tackle these problems? More use of comp/stat modeling and simulations. What else?
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Nanopowder Manufacturing Scale-up
Pump
Nanomiser®Flow Controller
Flame
Powder Collection & Dispersion System
Atomizing Gas
Solution
Filter
Atomizer
Control cost
Engineering knowledge
DataStatistical Model Calibration
Control & Evaluation
Quality Indices
Predictive Model Development
Jan Shi Lab
Challenges:• Nano-metrology analysis for
process control
• Variation propagation in multi-stage manufacturing process
• Process control capability
Goal: 1kg/day to 1000kg/day
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http://stemcelligert.gatech.edu
ApplicationsStem Cell Biology
Efficient, scalable &robust technologies
Reprogramming
Isolation
Manufacturing of diagnostic platforms & regenerative therapies
from stem cells
Pluripotent
Multipotent
Unipotent
Stem Cell Biomanufacturing
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Summary Remarks
• Quality management has made major economical and societal impacts. Quality engineering is the lesser known cousin. It has helped improve quality and reduce cost; witness the revival of US auto industries.
• Statistical design of experiments has a glorious history: agriculture, chemical, manufacturing, etc.
• Wider use of product/system simulations is expected in hi-tech applications. Further development requires new concepts and paradigm not found in traditional work.
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Geometrical interpretation of the failing P2
P2F
d(P2, P1)
1
d(P1,M1)
2
d(P1, M2 )
3
d(P1, M3)
M1M2
Z
Y
C1 C2
C3
M3
P2P1
X
The relationships of variations among sensing data due to locator P2 failure:
Where - STD at Mi
- STD of faults d(a,b) - distance
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