Accelerated Testing in Product D l t (加速试验在产Development (加速试验在产
品开发中的运用)
Dr. Loon Ching Tang (董润楨博士士)
©2011 ASQ & Presentation TangPresented live on Oct 12th, 2011
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© 2011 LC Tang. All rights reserved
Accelerated Testing in Product Development
加速试验在产品开发中的运用
Loon-Ching TANG (董润楨), Ph.DProfessor and Head, Department of Industrial and Systems Engineering
National University of [email protected]
© 2011 LC Tang. All rights reserved
Paradigm of Reliability Program
Past• Prediction reliability based
on part-count• Test and Fix cycle• Accelerated tests through
intensive usage and higher temperature
Present• New materials and devices
pushing the technology frontiers
• HAST and HASS; ALT• Short development cycle• Design for Six Sigma• Accelerated Degradation
Testing
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DFSS Roadmap
• System analysis– QFD, FMEA, Design selection, Product Architecture
• Robust Design– Statistical Design of Experiments– Taguchi loss function concept
• Product Optimization– Accelerated reliability testing and analysis– Tolerancing and sensitivity analysis– Process capability
© 2011 LC Tang. All rights reserved
How to tailor accelerated reliability testing under DFSS framework?
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3 Cases
• Accelerated testing of smallHard disk Drive (HDD)
• Design verification for ODM in split unit air-conditioning unit.
• Design release test for system iron
• Sophisticated user; Early design selection (Science Park, Singapore)
• New in reliability (Shenzhen, China)
• Mature product (moving from Singapore to Shanghai, China)
© 2011 LC Tang. All rights reserved
Background
• Sustained rapid evolution of HDD over the past decade.
• Rapid HDD product development requires timely and accurate predictions of HDD reliability. – Comparison of HDD designs– Tracking of reliability improvements
Analytical detailed can be found from "A Reliability Modeling Framework for the Hard Disk Drive Development Process“, IIE Transactions, 2010.
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Project Focus
• Test Setup and Data Collection Framework• Preliminary Analysis: ALT and Data Analysis• Reliability prediction framework based on Cumulative
Particle Counts (CPC)
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Test Setup Schematic
•Time to Failure •Failure Code
Particle Size Distribution
Proc
ess
Flow
Dat
a O
utpu
t
Particle Generator
Particle Distributor
Particle Throughput Counts
Particle Throughput Counter
Particle Classifier
HDDs Testing and Failure Detection
© 2011 LC Tang. All rights reserved
Reliability Assessment Framework
Key ideas:• Use Cumulative Particle Counts (CPC) as
“surrogate measure” for time Establish CPC-to-failure distribution (CTF)
• Transform CTF distribution to TTF distribution– Requires a means to transform CPC measure to proper
time measures
© 2011 LC Tang. All rights reserved
Reliability Assessment Framework
0
5
10
15
20
25
30
35
40
45
50
0 2000 4000 6000 8000 10000 12000 14000 16000
Time (sec)
Cum
ulat
ive
Part
icle
Cou
nts
(CPC
)
Fitted Model - MLE Upper 95% Confidence Limits - MLEFitted Model - LS Upper 95% Confidence Limits - LSRaw CPC Counts Cumulative particle count
Per
cent
Fai
lure
10000000100000010000010000
99
95
90
80
7060504030
20
10
5
1
Table of Statistics
Median 411288IQR 692065Failure 22Censor 14AD* 10.209
Loc
Correlation 0.978
12.9270Scale 1.13363Mean 782001StDev 1264597
Lognormal Plot for Cumulative Particle Countwith 95% confidence band Establish CPC-
to-failure (CTF)distribution
Establish CPC growth model
over time
Translate CTF distribution to
TTF distribution
Establish CTF distribution and lower
confidence limit to control risk
© 2011 LC Tang. All rights reserved
CPC growth model-empirical data
( )2 1 1 lnα α β τ= +
• Empirical CPC growth:
Non-linear trends
Non-linear trends
Two phases of particle growthTwo phases of particle growth
Run-in phase
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Cumulative Particle Counts (CPC)
• CPC data collected (CPC-to-failure data)• CPC-to-failure failure data appears to follow lognormal
distribution
Cumulative particle count
Per
cent
Fai
lure
10000000100000010000010000
99
95
90
80
7060504030
20
10
5
1
Table of Statistics
Median 411288IQR 692065Failure 22Censor 14AD* 10.209
Loc
Correlation 0.978
12.9270Scale 1.13363Mean 782001StDev 1264597
Lognormal Plot for Cumulative Particle Countwith 95% confidence band
© 2011 LC Tang. All rights reserved
Transform CTF to TTF distribution
CPC growthCPC growth
CTF distributionCTF distribution
TTF distributionTTF distribution
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TTF distribution and lower confidence limits
Lower Statistical Confidence LimitsLower Statistical
Confidence Limits
Failure Distribution in time
Failure Distribution in time
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Application: Design Selection
• Evaluation of design change• Addition of comb like device to dampen flow-induced
vibration and reduce particle induced failures
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Background
• An ODM with many models which are designed and manufactured to customers’ “specifications”.
• Lack of proper reliability program in design and development and face – High Market Call rate– Unknown design weaknesses
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Focus of this Presentation
• Designing reliability testing to– Explore product endurance limit– Uncover potential design weaknesses– Control warranty risk
timeMTTF
Number of failures
Warranty concerns here!
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Conditions in Product life Cycle
We need to examine the key operating parameters and major environmental factors over the entire product life cycle
Environmental ExposureNominal Voltage
Voltage cycling
Highest Voltage
Lowest Voltage
Number of Power on/off
Cycle
Nominal Ambient
Temperature
Temp Cycling
Fluctuation in the voltage of power supplyVibration induced by reciprocating compressorOperations at some harsh temperature due to extreme weather conditionsExtended hibernation due to pleasant weather during Spring/Fall (Cold-start)
Storage and transportation under extreme weather conditions
30-90C Summer;
? 20-240V
twice a day
(stable in 15 min)
Once per day
.25-35C in summer; -
3-7C in winter
? 5C in Summer
not an issue; dust and cold start
2C in winter
Seasonal fluctuation in ambient temperature and humidity 240V 215V
230V
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Consider Acceleration
• To reduce the test duration, one may consider testing under higher temperature, humidity, voltages, or their any combination of these stresses.
Stress
Time to failure
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Acceleration Models for ALT
• Physics+Statistics based Models– Arrhenius model – Power law ; Coffin-Mason– Eyring (2 stressors)– Peck (temperature and humidity)– Generalised Eyring (3 stressors)
• Statistics-based Models– Linear Model– Proportional Hazards Model
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Example: Arrhenius Model
TTF = A exp [Ea/(kT)]A ConstantEa Activation Energyk Boltzman constant (=8.617 E-5 eV/K)T Temperature in Kelvin
Linearized form :
Acceleration Factor
AF = ⎥⎦
⎤⎢⎣
⎡⎟⎟⎠
⎞⎜⎜⎝
⎛−=
s
a
s
oTTk
ETTFTTF 11exp
0
( ) ( ) io TTTF εββ ++= 1log 1
© 2011 LC Tang. All rights reserved
Example
Storage and Transport test – Test Purpose:
• To test the capability of the design in withstanding high temperature and cyclic temperature stress during storage and transportation.
• To ensure a reliability level of no less than 99.85% after storage and transportation.
Target System Call rate = 0.150% per yearDuration at high temp Level = 4 hours/day
Day in transportation = 30 dayTotal exposure in hour = 120 hours
Use = 0.4Use temperature = 70 degree C
343 KelvinActivation energy 1.2 eV
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A Typical Test Sequence
controller: 15ps controllers:15ps3days 2 days 2day
controller:15ps4days
2dayunit: 3ps
6days2day
units: 15ps14.5days
unit level
Function Testcontroller: 15ps
controller levelHigh temp.&Voltage operation
controller: 15ps
controller level
Cold start+low
Function Testcontroller: 15ps
controller level
Storage&Transport Test
controller level
controller level
Temp. Cycling Test
Vibration test
controller level
Function Test
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Background
• A domestic product with regular design upgrading. • How to reduce design qualification reliability test
time, while – Reducing Market Call rate– Uncovering unknown design weakness by giving failure
a chance.
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Preliminary Data Analysis
RAW DATA NEEDED (breakdown in components)• Release Life Test defect data• Market Returns data • Tabulate Relationship between test parameters and part
characteristics and effect of temperature on test parameters
Probability Plot (Weibull) – identify failure rate trend
Identify critical test parameters
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Susceptibility MatrixTest
VoltageTemp of soleplate
Increase freq of use
Total Iron ON hours
Total Litres consumed
No of on/off cycles
No. of steaming
cyclesAmbient
Temp
Stationary/Movement in
test
Hard water/Norm
al water
Iron Tray (Plastics / Metal / Rubber) 0 0 5 8 5 5 5 5 0 0
Electronics (PCBAs, LEDs, Mains Switch, etc) 10 0 8 8 8 10 8 10 5 0
Wiring system (All wires) 8 0 5 8 0 8 0 8 0 0
Steam Generation (Boiler Assy)
Boiler Support 5 0 5 0 5 5 5 5 0 0
Boiler Vessel 8 0 8 0 10 5 10 5 5 10
Pressostat 10 0 8 0 5 5 10 5 5 8
Fuse/thermostat 10 0 5 0 5 5 5 5 0 8
Heater plate Asy 10 0 8 0 5 5 5 5 0 5
Boiler water level sensor (Conductivity type) 5 0 10 0 10 8 8 0 8 10
Rinse opening 0 0 5 0 5 0 5 0 0 8
Safety valve 5 0 5 0 5 0 5 5 5 10
Steam Regulator (Variable Steam) 0 0 5 0 5 0 5 0 0 8
Steam Delivery
Electrovalve 10 0 10 8 10 8 10 8 0 10
Hose & connection 0 0 5 0 5 0 5 5 8 5
Iron
Plastics 0 5 5 8 5 5 5 5 8 0
Steam trigger / microswitch 10 5 10 8 10 10 10 8 5 0
SOS Knob / Steam Deviator (for SOS version 0 8 5 8 5 5 8 5 0 5
Soleplate - Steam cover 5 10 10 8 8 5 10 5 5 8
Soleplate -Thermostat 10 5 8 10 5 5 5 5 5 5
Soleplate - Heating element 10 10 8 8 5 5 5 5 0 5
Iron Electronics (PCBAs LEDs, LCD, switches, 10 10 8 10 0 10 10 10 5 0
Par
t lis
t
Maj
or fa
ilure
mod
e ob
serv
edTest sequence
How does a test sequence “stress” a component/part?
What is the type of failure that a test sequence designed to induce?
© 2011 LC Tang. All rights reserved
Test Strategy
– Release test at zero failure– Temp cycling for Iron and Stand Electronics– Test for triggering pump– Test for triggering pressostat and electrovalve
• Simultaneous Testing using accelerated usage
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Understand Temperature Profile
Temperature Rise Test - In Life Test Rack2 hrs ON 45 mins OFF, ON time-7s steam ON 14 steam OFF
-40.0-20.0
0.020.040.060.080.0
100.0120.0140.0160.0180.0200.0220.0240.0
1 58 115 172 229 286 343 400 457 514 571 628 685 742 799 856 913 970 1027 1084 1141 1198 1255 1312 1369 1426 1483 1540 1597 1654 1711 1768 1825 1882 1939 1996 2053 2110 2167 2224 2281 2338 2395 2452 2509 2566 2623 2680 2737 2794 2851 2908 2965 3022 3079 3136 3193 3250 3307 3364 3421 3478 3535 3592 3649 3706 3763 3820 3877 3934 3991
Time
Tem
pera
ture
Hose
Plastics-handle
Plastics-side cover
Trigger Switch
Microswitch
Soleplate (IEC point)
Soleplate(inside onsteam coverSoleplate onthermistor/thermostatStand Electronics
Stand asy-tray
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Conclusion
• Key to success: – Understand customers’ needs and their usage profile.– Understand limitations of your products
• 3Cs: Customer…Conformity…Cost (profit)• Key Techniques:
– Knowledge in failure mechanisms– Statistical modeling– Mathematical programming
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Thank you!
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