Accelerated testing in product development

Accelerated Testing in Product
Development (加速试验在产
D l t (加速试验在产
品开发中的运用)

Dr. Loon Ching Tang (董润楨博
士)
©2011 ASQ & Presentation Tang
Presented live on Oct 12th, 2011

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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.D
Professor and Head, Department of Industrial and Systems Engineering
National University of Singapore
isetlc@nus.edu.sg

© 2011 LC Tang. All rights reserved

Paradigm of Reliability Program
Past Present
• Prediction reliability based • New materials and devices
on part-count pushing the technology
• Test and Fix cycle frontiers
• Accelerated tests through • HAST and HASS; ALT
intensive usage and higher • Short development cycle
temperature • Design for Six Sigma
• Accelerated Degradation
Testing


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


How to tailor accelerated reliability
testing under DFSS framework?


3 Cases
• Accelerated testing of small • Sophisticated user; Early
Hard disk Drive (HDD) design selection (Science
Park, Singapore)

• Design verification for ODM • New in reliability
in split unit air-conditioning (Shenzhen, China)
unit.

• Design release test for • Mature product (moving
system iron from Singapore to
Shanghai, China)


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.


Project Focus
• Test Setup and Data Collection Framework
• Preliminary Analysis: ALT and Data Analysis
• Reliability prediction framework based on Cumulative
Particle Counts (CPC)


Test Setup Schematic

Particle Particle Particle Particle Throughput
Process

Generator Classifier Distributor Counter
Flow

HDDs Testing and
Failure Detection
Output

•Time to Failure
Data

Particle Size Particle Throughput
Distribution •Failure Code Counts


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


Reliability Assessment Framework

50
Lognormal Plot for Cumulative Particle Count
45 with 95% confidence band

Establish CPC-

Cumulative Particle Counts (CPC)
40 99
Table of Statistics

Establish CPC
Loc 12.9270
35 Scale 1.13363
95
Mean 782001

to-failure (CTF)
30 90 StDev 1264597
Median 411288
25 80 IQR 692065

Percent Failure
Failure 22
70

growth model
20 Censor 14
60 AD* 10.209
50 Correlation 0.978
15
40
10

5
30
20
distribution
over time
10
0
5
0 2000 4000 6000 8000 10000 12000 14000 16000
Time (sec)
1
Fitted Model - MLE Upper 95% Confidence Limits - MLE
Fitted Model - LS Upper 95% Confidence Limits - LS 10000 100000 1000000 10000000
Raw CPC Counts Cumulative particle count

Translate CTF
distribution to
TTF distribution

Establish CTF
distribution and lower
confidence limit to
control risk

CPC growth model-empirical data
• Empirical CPC growth: Two phases of
Two phases of
particle growth
particle growth

Run-in
phase Non-linear
Non-linear
trends
trends

α 2 = α1 + β1 ln (τ )

Cumulative Particle Counts (CPC)

• CPC data collected (CPC-to-failure data)
• CPC-to-failure failure data appears to follow lognormal
distribution
Lognormal Plot for Cumulative Particle Count
with 95% confidence band
99
Table of Statistics
Loc 12.9270
Scale 1.13363
95
Mean 782001
90 StDev 1264597
Median 411288
80 IQR 692065
Percent Failure

Failure 22
70 Censor 14
60 AD* 10.209
50 Correlation 0.978
40
30
20

10
5

1
10000 100000 1000000 10000000
Cumulative particle count


Transform CTF to TTF distribution

TTF distribution
TTF distribution

CPC growth
CPC growth

CTF distribution
CTF distribution


TTF distribution and lower confidence limits

Lower Statistical
Lower Statistical
Confidence Limits
Confidence Limits

Failure Distribution
Failure Distribution
in time
in time


Application: Design Selection
• Evaluation of design change
• Addition of comb like device to dampen flow-induced
vibration and reduce particle induced failures


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


Focus of this Presentation
• Designing reliability testing to
– Explore product endurance limit
– Uncover potential design weaknesses
– Control warranty risk

Number of failures

time
MTTF
Warranty
concerns here!

Conditions in Product life Cycle
We need to examine the key operating parameters and major
environmental factors over the entire product life cycle
Number of Nominal
Nominal Voltage Highest Lowest Temp
Power on/off Ambient
Environmental Exposure Voltage cycling Voltage Voltage
Cycle Temperature
Cycling

Seasonal fluctuation in ? 5C in
? 20-240V
Summer
ambient temperature and
humidity 240V 215V
Fluctuation in the voltage twice a
day 2C in .25-35C in
of power supply Once per summer; -
230V (stable in winter
Vibration induced by day 3-7C in
15 min)
reciprocating compressor winter
Operations at some harsh
temperature due to
extreme weather
conditions
Extended hibernation due
to pleasant weather
not an issue; dust and cold start
during Spring/Fall (Cold-
start)

Storage and
transportation under 30-90C
extreme weather Summer;
conditions

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

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


Example: Arrhenius Model

TTF = A exp [Ea/(kT)]
A Constant
Ea Activation Energy
k Boltzman constant (=8.617 E-5 eV/K)
T Temperature in Kelvin

Linearized form : log(TTF ) = β o + β1 (1 T ) + ε i
Acceleration Factor
TTF o ⎡ Ea ⎛ 1 1 ⎞⎤
= exp ⎢ ⎜ − ⎟ ⎥
k ⎜ T0 Ts ⎟ ⎦
AF =
TTF s ⎣ ⎝ ⎠


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 year
Duration at high temp Level = 4 hours/day
Day in transportation = 30 day
Total exposure in hour = 120 hours
Use = 0.4
Use temperature = 70 degree C
343 Kelvin
Activation energy 1.2 eV


A Typical Test Sequence

Storage&Transport Test
Vibration test Function Test
controller: 15ps controllers:15ps controller: 15ps
3days 2 days 2day
Temp. Cycling Test controller level controller level
controller:15ps
4days Function Test
controller level controller: 15ps
2day Cold start+low
Function Test controller level unit: 3ps
controller: 15ps 6days
2day High temp.&Voltage operation unit level
controller level units: 15ps
14.5days
controller level


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.


Preliminary Data Analysis
RAW DATA NEEDED (breakdown in components)
• Release Life Test defect data Probability Plot (Weibull)
• Market Returns data – identify failure rate trend

• Tabulate Relationship between test parameters and part
characteristics and effect of temperature on test
parameters

Identify critical test parameters


Susceptibility Matrix
No of No. of Stationary/Mo Hard
Test Temp of Increase Total Iron Total Litres on/off steaming Ambient vement in water/Norm
Voltage soleplate
Test sequence
freq of use ON hours consumed cycles cycles Temp test al water

Iron Tray (Plastics / Metal / Rubber) 0 0 5 8 5 5 5 5 0 0
Electronics (PCBAs, LEDs, Mains Switch, etc)
Major failure mode observed 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 How does a test sequence “stress” a
0 5 0 5 5 5 5 0 8
Heater plate Asy 10 0 8 0 5 5 5 5 0 5
component/part?
Part list

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
Steam Regulator (Variable Steam)
5
0
What is the type of failure that a test
0
0
5
5
0
0
5
5
0
0
5
5
5
0
5
0
10
8
Steam Delivery sequence designed to induce?
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


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


Understand Temperature Profile

Temperature Rise Test - In Life Test Rack
2 hrs ON 45 mins OFF, ON time-7s steam ON 14 steam OFF

Hose
240.0
220.0 Plastics-handle
200.0
Plastics-side cover
180.0
160.0 Trigger Switch
140.0
Temperature

Microswitch
120.0
100.0 Soleplate (IEC point)
80.0
Soleplate(inside on
60.0 steam cover
40.0 Soleplate on
20.0 thermistor/thermostat
Stand Electronics
0.0 1 58 115 172 229 286 343 400 457 514 571 628 685 742 799 856 913 970 10 7
2 10 4 1141 1198 1255
8 1312 1369 1426 1483 1540 1597 1654 1711 1768 1825 1882 1939 1996 20 3
5 2110 2167 2224 2281 2338 2395 2452 250 2566 2623
9 2680 2737 2794 2851 290 2965
8 30 2
2 30 9
7 3136 3193 3250 330 3364 3421
7 3478 3535 3592 3649 370 3763
6 3820 3877 3934 3991

-20.0 Stand asy-tray
-40.0
Time


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


Thank you!


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