dfM at Stanford
Design for Manufacturability
ME317 dfM
Quality by Design: Fundamentals
“Quality is Far More than Just a Manufacturing Problem”
Philip Barkan, 1996
Kos Ishii, Professor
Department of Mechanical Engineering
Stanford University
ishii@stanford.edu
http://me317.stanford.edu
©2006 K. Ishii
dfM at Stanford
Today’s Agenda
 Quality by Design the “Stanford-Way”
Quick review of quality lingo
Measure negative VOC, not scrap
Complexity is the enemy
Address variation and mistakes
From Quality Control to “Quality by Design”
 Quality Scorecarding
Framework to thread ME317B tools together
ME317 Team Examples
©2006 K. Ishii
dfM at Stanford
Quality by Design at Stanford
Recall the ME317 Philosophy:
“More than ease of Manufacture”
How to make money in manufacturing business.
Define, develop, and produce competitive products.
ME317A: Product Definition
Goal Identification
“What Quality” to Deliver
ME317B: Quality by Design
“How to Deliver” the Identified Quality
How to proceed from the developed concept?
Robust Design using Quality Scorecarding
©2006 K. Ishii
dfM at Stanford
Defects: ME317 Definition
 DEFECTS = negative VOC
Anything that leads to customer dissatisfaction
 Consequences to the user
Life threatening
Irrevocable Loss
Major inconvenience / Annoyance
 Costs to the Manufacturer
Recall Costs
Litigation
Loss of repeat sales
Company Survival!
 A tiny part can hurt a big company, big time...
©2006 K. Ishii
dfM at Stanford
Quick Review of Quality “Lingo”
To be explained later in the lecture:
 Process Specification Width (PSW)
 Manufacturing Capability: NT = +3s
 Capability Index Cp = PSW / NT
Cp = 1.0 leads to 2,700 PPM scrap (99.73% yield)
Process Spec. Width (PSW)
Mfg. Capability
STDEV = s
Scrap
USL: Upper Spec Limit
LSL: Lower Spec Limit
LSL
Target
USL
©2006 K. Ishii
dfM at Stanford
Motorola Six-Sigma
More Proactive than Statistical Process Control
 Systematic look at each PROCESS ELEMENT
Process Element: discrete operation
Apply early to design
 Address both random deviation & process shift
 Definition of process capabilities Cp and Cpk
 Guidelines for:
Design: tight tolerance is expensive!
Process Control: resource allocation
©2006 K. Ishii
dfM at Stanford
Let’s review a few definitions...
Natural Tolerance (NT) of a Process
 NT: Variance that can be expected in a process
 NT Depends on:
How tight or broad the variation is
 Common assumption: Normal Distribution
NT = + 3s
 Consider a precision ground shaft dia. 1.125cm
 Process s = + 0.001cm
 +3sgives 99.73% yield (2,700ppm defects)
 NT: 1.125 + 0.003cm for 99.73% yield
©2006 K. Ishii
dfM at Stanford
Population under Normal Curve
99.9%
99.5%
99.0%
98.0%
95.0%
90.0%
80.0%
+ 3.291
+ 2.807
+ 2.576
+ 2.326
+ 1.960
+ 1.645
+ 1.282
3s
3s
99.73%
 If you want 99.9% yield
Must go for 3.29s, either:
Tighten the NT and reduce s
Wider tolerance: 1.125 + 0.0039 cm
©2006 K. Ishii
dfM at Stanford
Process Shift: PS
 Otherwise called drift
E.g. tool wear causes dia shift
From 1.125 cm to 1.13 cm
Accepted value of PS = 1.5s
Dia.
USL
3s
1.5s
Time
3s
LSL
©2006 K. Ishii
dfM at Stanford
Multiple Step Processes
 Throughput without repairing parts:
Must consider every defect source
Yield goes down dramatically with 100 processes
6s
1
0.9
6s +1.5s
0.8
0.99379
for
1 process
0.7
4s
0.6
0.5
0.4
0.53638
for
100 process
0.3
0.2
4s +1.5s
0.1
10000
3000
Number of Processes
1000
300
100
30
10
3
0
1
Defect
Rate
©2006 K. Ishii
dfM at Stanford
What does 6 Sigma mean in real life?
USL
LSL
USL
LSL
Defect
3
2
1
μ
1
2
3
3s
2700ppm
66, 810 ppm
6
4
2
μ
2
4
6
6s
0.002 ppm
3.4 ppm
5 Emergency landings at
Chicago O’hare airport / day
1 Emergency landing in all
US airports in 10 yr
5000 Malpractice cases/ week
5 Malpractice cases/ 10yr
©2006 K. Ishii
dfM at Stanford
Process Capability Indices
 Process Capability Index
Cp = PSW / NT
Industry minimum = 4/3
 Process Shift
k = 2 PS / PSW
k = 0.5 if PS=1.5s and PSW=6s
 Shifted Process Capability Index
Cpk = Cp (1 - k)
Industry minimum: 6/5
 WHAT DO YOU DO IF Cp < 1 ?
©2006 K. Ishii
dfM at Stanford
Capability Ratio Implications
Traditional Approach
 If Cp < 1
Shift job to another process
Improve capability
Review tolerance requirements
100% inspection
 If 1.3 < Cp < 1.5
System is adequate
Periodically check production
 If Cp > 2
Do you actually need the tight tolerance?
Could you perhaps use a cheaper process?
©2006 K. Ishii
dfM at Stanford
Motorola Targets
 Cp > 2
 PSW > 12s6son each side) OR tighter process
 k = 0.25
 PS = 1.5s (based on PSW=12s)
 Cpk >1.5
 PSW=12sand PS=1.5s
 Motorola 6s Strategies: Summary
Design products for wide PSW
Employ processes with tight variation
Apply the mfg. capability concept to design
©2006 K. Ishii
dfM at Stanford
Six Sigma Limitations
 Six Sigma a significant advancement, but...
Still underestimates defect rates often
Defects often still over 1000ppm
 Non-normally distributed variations & interactions
Multi-dimensional feature interactions
Refer to reader / lecture on Poka Yoke
 Six Sigma does not take into account MISTAKES
 Matsushita actually targets 4.5 Sigma
Focus more on “Zero Defect”
©2006 K. Ishii
dfM at Stanford
Quality by Design: Stanford Way
 Complexity leads to defects!
Address not only design but processes (project, supply chain…)
Must address both variations and “mistakes”
 Tools to fight quality non-conformance
Robust Design
Error Proofing
Quality Scorecarding
Non Conformity
M is tak e s
Log(Likelihood)
Complexity
"Normal" Model
of Hole
Diameters
Drilling
Drilling
Omitted
Omitted
Variation
00
Hole Diameter
©2006 K. Ishii
Complexity: Toyota’s 4M and an i
(from Martin Hinckley “Quality by Design”)
Man
Complexity
Mistakes
Variation
Training
Experience
Skill
Omission
Errors
Dropped parts
Individual
Differences
Emotion
Machine
Tool Wear
Bearing Wear
Fixture Wear
Vibration
Incorrect
Setup
Software
Errors
Difficult
Setup
Difficult
Operation
Material
Methods
Information
Purity
Density
Differences in
Execution
Homogeneity
Wrong
Material
Wrong
Part
Wrong
Method
Error in
Method
Difficult to
Make
Difficult to
shape
Difficult
Method
Gage
Consistency
Or Accuracy
Wrong
Instructions
Instruction
Error
Misread Gage
Lengthy or
Verbose
Instructions
Many
Interpretations
dfM at Stanford
Mitigation 1:
Design for Robustness against Complexity
 Wafer Handling Robot
Example
 Complexity = No. of
Error Sources
Complexity Indicator
Estimator of Defect
Rate
Prior
Proposed
FL
DP
0.32
0.44
Error sources
22
Parts
SDP
RP
FB
2.73
4.29
2.29
26
12
8
8
34
46
41
34
25
Enclosure radius (in)
8.8
7.0
7.8
12 .0
9.0
Natural frequency (hz)
80
23
61
38
56
Particle generation
14
18
17
4
10
5
1
9
8
10
Robustness
Reliability
©2006 K. Ishii
dfM at Stanford
Mitigation 2:
Source Inspections & Error Proofing (Poka Yoke)
 Detect error / deviation BEFORE defects occur
Remember the book “The Goal?”
Requires cause-effect understanding (QFD)
Source
Inspections
Opportunity
for QUALITY
by DESIGN
Inputs
Accept
Inspect
Shutdow n
Process
Product
Control
Warn
©2006 K. Ishii
dfM at Stanford
Quality Scorecarding Compiles
the Following Methods
 Robust Design
Robust Design Basics
Taguchi Method
Robust Design Applications
Robust Conceptual Design
 Zero Defect
Six Sigma Concepts
Poka Yoke (Error-Proofing)
Quality Scorecarding
Key: Identification of NOISE
©2006 K. Ishii
dfM at Stanford
Six Sigma Scorecarding

Project Objective (Biggest Y)
 Decrease Cost

Objective Measures (Y’s and y’s)





System Level: Anti-gravity device cost per server (Y)
Component Level: Cost per installable card (y)
Ergonomics / Safety: Number of Injuries
Mean Time Between Failure
Control Factors (Vital X)
 Geometry of the server box
 Material Selection (EMI, Heat, Debris)
 Anti-gravity Device Design

Noise Factors (V’s)
 Manufacturing Variations / Material Variation
 Moving Parts / Part Interference

Transfer Function
MTBF
 Y = Function of Mfg. Variations, Material Variation, Injury, Cost
©2006 K. Ishii
Scorecarding: Toyota Safety System (2003)
 Project Objective (Biggest Y)
 Increase Toyota’s overall profits (Y)
 Objective Measures (Ys and ys)
 Improve Toyota’s safety-conscious image (y)
 Reduce Toyota accidents (y)
 Control Factors (Vital Xs)
 Number of bad manners addressed (x)
 Demographic spectrum served (x)
 Product price & quality (x)
 Noise Factors (Vs)
 Changing rules & regulations (v)
 Transfer Function
Transfer F unction 
Areas Addr essed  User Frie ndliness
Cost
dfM at Stanford
Decision Influence Diagram
& Scorecarding
 Biggest Y:
NPV/BET
NPV/BET
 Big Y:
Price & Volume
Cost, Investment, TTM
 Vital X:
Back-Bending or No
New controller or No
 V(noise):
Customer Response to
Back-Bending Feature
Customer Response to
New Controller
Cost
Investment
TTM
Back-Bending/
No Back-Bending
Mechanics Only/
Mechanics+Controls
Revenue
Price & Volume
Customer Response
To Back-Bend
Marketing
Report
Customer Response
To New Controls
©2006 K. Ishii
dfM at Stanford
Scorecarding Terminology
Summary
 Biggest Y Scorecarding
Clarifies business objectives
Identifies decision & noise drivers
Establishes relationships between product
performance and decision variables
 Product Scorecards
Track design progress
Focus teams on quality objectives
 Transfer Functions
Relationship between characteristics
Essential in robust design / error proofing
©2006 K. Ishii