Kittiphan Pomoung

About Me

• Mr Kittiphan Pomoung

• Education : Master degree in Recording Technology @ KMITL

• Experiences :

– 21 years working experience in Hard Disk Drive companies

– 9 months in Data Mining Project Collaboration with IBM

• Email Address : Kittiphan.pomoung@wdc.com

Topics

• Industry Revolusion (Trend) .

• Power of prediction: assess results in advance, identify

key challenges and how to overcome them.

• A taste of success: simple data modeling applied to a

real case in manufacturing process with a satisfactory

result.

Special Thanks

• Eakasit Pacharawongsakda, PhD.

• Aimamorn Suvichakorn, PhD.

• Kosit Bunsri, M. Eng.

Industry 4.0

• Water and

Steam Power

• First Power

Loom- 1784

• Electric Energy

• Assembly belt ,

1870

• Electronics and

information

• Programmable

Logic Control –

PLC, 1969

• Cyber-Physical

System

• All tools will

communicate and

Data will be

shared to each

other .

• Product and

Machine talk

together

• Build Per Order,(flexible with RFID)

2nd

Revolution1st

Revolution

3rd

Revolution

4th

Revolution

Prediction in Manufactory

• Market and Demand Forecast

• Machine Utilization

• Preventive Maintenance

• Quality Improvement

Challenges

• High expectation in prediction accuracy

• Unknown factors and variables– Oli Price

– Market’s demand

• Inadequate resources – Knowledgeable staffs

– Tools

• Limited data and understanding.

Part 2

Reliability Prediction

• Reliability test can take very long time (>1000 hrs),

sometimes with temperature variation.

– Tyre, chair, motor and HDD.

• What if, we can predict the result earlier, “before the

test starts”.– Traditional Method

– Advance / Numerical Predictive Method

10

Components in an HDD

• Can be more than 17 components

• One component possibly comes from 2 suppliers

• At least 34 variables in total! , Many data stored

Reliability Prediction : Background

Electrical

TestAssembly

Components

> 16 parts

Reliability

TestDone

• Data : > 200 parameters (attributes and variables). 1mil data entries per week.

• Duration:

• Some components manufacturing process > 60-90 days

• Reliability test 700-1200 hrs

• Worst case total processing time is 4 months

• What if, the predictive model can predict the result earlier

Basic Hard disk Process

Basic Hard Disk Drive Reliability Test Process• Test under stress condition

• 700-1200 hrs test time

• Limited samples for training

• 200-300 drives per batch

• Only 1-2 failed units per batch

• Some failures occurred at late test hours (worn-out).

Reliability Prediction : Background

• Objective : To predict the Reliability test result for new

material qualification in term of failure rate.

• Benefit : Time saving ($$) and Quality Improvement.

• Background :

– New material qualification usually takes 3 months.

– Failure could occur at the last minute of Reliability test, at the last

test station.

– If happens, to re-design and re-qualification again.

• Challenges :

– Limited failed samples to form the correlation

– Reliability test is more stressed than usual electrical tests

Reliability Prediction : Project

Data Preparation

Classification

Test

Train

Deployment

Validation

Predictive Model

Feature Selection • To improve efficiency and accuracy

• 200 parameters down to ~ 20 attributes

Classification :• Rule Base Moderate

• *Decision Tree Good

• Fusion (Naive Bayes + Decision Tree) Best In Class

Techniques : Limited failed drives• *Over sampling / Boosting

• Under Sampling

• Result ~ 65-70% accuracy when implemented.

• The classification model is continuingly optimized by

training with new samples.

Reliability Prediction : Workflow

• Different products require different modelling techniques.

• Classification method could be constrained when

implementing

• Rule Base --> Moderate

• Decision Tree Good results, easy for implementation

• Fusion (Naive Bayes + Decision Tree) Best In Class

• Future Works

• Defined key parameters input variables (KPIV)

• Establish KPIV/KPOV that correlating to component level .

• Establish Predictive model at component level (prior to HDD

assembly)

Reliability Prediction : Lessons

Part 3

Process/Model(Transfer Function)

Inputs

Parameters

Output

Defect/Pareto

Performance

/Distribution

Factors(Power/ Temp)

Performance Prediction : Objective• Objectives :

– Product Boundary or Product Capability

– Relationship / transfer Function

– Know it earlier , as fast as it possible

– What is the effect if input change (optimization)

Performance Prediction : Process-1

• Average and Stdev of Input Population

• Buy off Distribution Type of output Population

Performance Prediction : Process-2

Outp

ut

Input

• Transfer function

– Output= 15.328527 + 7.012858*Input- 1.5895329*(Input-3.2)^2

Trails -2 -1 CT +1 +2

Input (Avg) 2.7 3.0 3.2 3.4 3.6

Output (Avg) 34 36 37.7 39.3 40.7

• Calculate (simulate ) the Average of output distribution

Performance Prediction : Process-3• Generate (pseudo) output distribution with Random Technique

– Excel/JMP : Random Normal (Output’s Avg, sigma)

– SS = 1000*

• Iterations and Sample size are able to improve accuracy

Trails -2 -1 CT +1 +2

Input (Avg) 2.7 3.0 3.2 3.4 3.6

Output (Avg) 34 36 37.7 39.3 40.7

2.48 2.72 2.96 3.2 3.44 3.68 3.922.48 2.72 2.96 3.2 3.44 3.68 3.92

Output

Input

Failure Rate (CDF Plot)

Spec/Limit

(LCL)Good

• 3.4 is minimum

requirement to meet

product capability

Performance Prediction : Result

Output

Input

• Product Performance(Output) vs Incoming Performance (Input)

• Linear Regression and Monte Carlo Techniques

– Simple

– Decent and Fair Result

• Iterations and Sample size

– More is Good, better accuracy

– Sample size > 50k , 5 times per each input.

– Averaged the result for each run .

Performance Prediction : Lessons

End