บรรยายโดยคุณกิตติพันธุ์ โพธิ์ม่วง ผู้อำนวยการฝ่ายเฮดและมีเดีย บริษัท เวสเทิร์น ดิจิตอล (ประเทศไทย) จำกัด

1. Kittiphan Pomoung

2. 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

3. 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.

4. Special Thanks
• Eakasit Pacharawongsakda, PhD.
• Aimamorn Suvichakorn, PhD.
• Kosit Bunsri, M. Eng.

5. 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
Revolution
1st
Revolution
3rd
Revolution
4th
Revolution

6. Prediction in Manufactory
• Market and Demand Forecast
• Machine Utilization
• Preventive Maintenance
• Quality Improvement

7. Challenges
• High expectation in prediction accuracy
• Unknown factors and variables
– Oli Price
– Market’s demand
• Inadequate resources
– Knowledgeable staffs
– Tools
• Limited data and understanding.

8. Part 2

9. 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. 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

11. Reliability Prediction : Background
Electrical
Test
Assembly
Components
> 16 parts
Reliability
Test Done
• 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

12. 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

13. • 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

14. 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

15. • 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

16. Part 3

17. 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)

18. Performance Prediction : Process-1
• Average and Stdev of Input Population
• Buy off Distribution Type of output Population

19. Performance Prediction : Process-2
Output
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

20. 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

21. 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)

22. • 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

23. End