accelerated testing in product development

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 Singaporeisetlc@nus.edu.sg

© 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

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How to tailor accelerated reliability testing under DFSS framework?

© 2011 LC Tang. All rights reserved

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.

© 2011 LC Tang. All rights reserved

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

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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

© 2011 LC Tang. All rights reserved

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

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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?

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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!