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A methodology for integrating design for quality in modular product design Bimal Nepal, Leslie Monplaisir, Nanua Singh Department of Industrial and Manufacturing Engineering Wayne State University April 21, 2006
Outline Motivation Research objective Prior work on modularization Proposed framework Case example Conclusions
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Motivation Inspection based quality controlling techniques can not compensate the flawed design Quality should be built-in during the design stage Product architecture is one of the key drivers of poor quality and high manufacturing cost Yet, current DFQ techniques do not address the product architecture issue
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Modular Vs. Integral Product Architecture
Source: Pimmler and Eppinger, 1994
Modular
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box
protect cargo from weather
upper half
protect cargo from weather
hitch
connect to vehicle
lower half
connect to vehicle
fairing
minimize air drag
nose piece
minimize air drag
bed
support cargo loads
springs
suspend trailer structure
spring slot covers
wheels
transfer loads to road
wheels
Integral
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cargo straps
support cargo loads suspend trailer structure transfer loads to road
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Research Objective Develop an integrated framework for product modularization that 9incorporates design for quality issues in modular design. ←Optimization 9Considers cost of modularization . model 9Incorporates subjective/prior product knowledge during concept development ← Fuzzy Logic 9Facilitates sensitivity / What-If analysis for determining the optimal number of modules April 21, 2006
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Prior Modularization Methodologies Heuristic Based Methods Clustering heuristics are derived based on single factor e.g., similarity (Tsai and Wang,1999 ) , manufacturing cost (Pahl and Beitz, 1961, 1984), functional interactions (Pimmler and Eppinger, 1994) , and testability (Huang and
.
Kusiak, 1997; Kusiak, 2002; Tsai et al., 2003 )
Modular Function Deployment (MFD) Module drivers such as technology, carryover, flexibility etc. are used to evaluate the conceptual modules (Erixon and Ericssion, Ericssion, 1999) .
Optimization Models Single objective such as similarity index (Slahieh and Kamrani, 1999), 1999), non-linear heuristic optimization (Gu and Sosale, Sosale, 1999) , limited scope of optimization (Kreng and Lee, 2004) .
Functional Structure Models Qualitative heuristics approach based on flow pattern in functional structure diagram (Stone et al., 2000). 2000) May result in ambiguous modules. April 21, 2006
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Limitations of Prior Methods Limited in Scope 9Heuristic approaches 9Simplistic optimization objectives
Inability to exploit the subjective product knowledge during early stages of development Inability to perform sensitivity analysis
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IDEF0 Modeling of proposed framework Acquire Product Knowledge General Analysis of Product
C(n,2)Evaluation candidate Guidelines modules Optimization Model Constraints Membership Function Types Quality and cost metrics Subject Knowledge
Past Experience
Level of Analysis (Decomposition)
Acquire Product Knowledge
Identification of Cost & Quality Metrics
Evaluate Candidate Modules
Identification of Candidate Modules
A1
-Decomposition Fuzzy 'if-then' rules
Evaluate Candidate Modules
-Formation of candidate modules
Cost Performance Index Quality Performance Index
Modularize Product Architecture A*2
-Develop structured -Define design Number of Median Components requirements guidelines -Evaluate modules
Cost and Quality performance indices
Modularize Product Architecture
Modular Modules and Components Architecture Aspiration Levels
A*3
Design Engineer
Analyze
Optimal candidate "What-If" -Formulate goal Mamdani Fuzzy Inference No. of Scenarios System Modules programming model A*4 Perform What-if Product Decompositional Analysis
Design Theory and Methodologies -Compute cost and -Change theoptimal parameters and -Identify Analysis quality indices (Fuzzy Optimization Package (e.g., Solver) rerun model Design For Quality Principles modules Fuzzythe Set Theory Logic Model) -Select the best scenario April 21, 2006 Optimal # of modules8
Requirements Definition Design Objectives
Performance Metrics
Cost of Modularization
Interface cost, Assembly resource requirements, Reusability cost
Quality and Robustness
Perceived quality, Robustness, Compliance to axiomatic design
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Evaluation of Candidate Modules Each candidate module is evaluated against all six metrics Each performance metric has five measure levels such as very low, low, moderate, high, and very high. Set of evaluation guidelines for each metric Example: Rating
Cost Level Description of cost of interface
1,2
Very Low
If there is no separate interfacing medium or the interfacing medium is a standard component e.g. rubber seal, washer with simple fastening design.
9, 10
Very High
If the interfacing medium is internal/external and the cost of interface is very high or interfacing is infeasible due to practical reasons.
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Evaluation of Candidate Modules.. DESIGN PRINCIPLES, DESIGN FOR X, AND OTHER INFO SOURCES
HUMAN KNOWLEDGE & EXPERTISE BASE
COST QUALITY METRICS
(INPUTS)
FUZZY RULE BASE
FUZZIFICATION
FUZZY INFERENCE ENGINE
DEFUZZIFICATION
HISTORICAL DATA ANALYSIS
CPI QRI
(OUTPUTS)
Fuzzy Logic Model April 21, 2006
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Fuzzy Rules: Few Examples Rule #
Input (IF) Interface Cost
Output (Then)
Assembly Reusability Resource Cost Requirements
Cost Performance Index
1
Very Low
Very Low
Very Low
Very Low
2
High
Low
Very Low
Moderate
3
Low
Moderate
Very High
High
4
Very High
Very High
Very High
Very High
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Product Modularization Optimization Model Objective Functions Max Min
Q= C=
∑ ∑ n
j =1
∑ ∑
n
Xij q * ij i =1
n
n
j =1
i =1
cij * Xij
(Quality) (Cost)
Decision Variables Xij = 1, If component i and j are in the same module (i = 1, 2,….n; j = 1,2,….n) 0, otherwise April 21, 2006
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Product Modularization.. Objective function Minimize
δ
Unwanted deviations from each aspiration level
The Chebychev goal programming model
Subject to Constraints
∑
n
∑
n
j =1
j =1
Xij = 1
∀i
Xjj = N
Clustering or ‘Hard’ Constraints
. Xij ≤ Xjj
∀i , ∀j
Xij = 0 / 1
∀i , ∀j
∑
n
∑
n
j =1
j =1
∑ ∑
Xij, N April 21, 2006
n
q i = 1 ij
* Xij
n
* Xij
i =1
cij
+ δ + δ
≥ aq ≤ ac
(Quality) (Cost)
Goal or ‘Soft’ Constraints
(Non-Negativity)
≥ 0.
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Case Example- A Coffeemaker
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Coffeemaker-Functional Decomposition M AK E CO FFEE M anage Cold W ater
Store Ground Coffee
Heat W ater
Enclose Filter
Im port Electric.
Im port W ater
Store W ater
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Actuate Electric.
Stop W ater flow
Regulate Electric.
Im port Export Ground Coffee
Convert Elec. To Therm al Energy
Transport Brewed Coffee
M anage Hot W ater
Store Ground Coffee
Transm it Therm al Energy
Transport Coffee
Transport W ater
Export W ater
M ix Coffee & W ater
Regulate Flow
Keep Coffee W arm
Im port Electrical Energy
Regulate Steam Flow
Transport W ater
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Store Coffee
M ix CoffeeW ater
Cover Coffee
M anage Brewed Coffee
Dissipate Therm al Energy
Refine Coffee
Guide Coffee
Export Coffee
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Coffeemaker-Physical Decomposition COFFEE MAKER Electrical
Cable
Top Surface Assy.
Boiler Set
Connectors
Water Reservoir
Dust Cover
One-way Valve
Top Surface
Cold water Tube
Water Heater
Warming Plate Assy.
Dripping Surface
Hot Aluminum Tube
Plate Heater
Heating Element
Bottom Cover
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Warming Plate
Lighted On/Off Switch
Brew Basket Assy.
Plate Sensors
Brew Basket
Indicator Light
Exit Valve
Decanter Assy.
Switch
Decanter
Handle
Decanter Cover
Bottom Mounting
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Evaluation of Candidate Modules Component I
Component J
Perceived Quality
Robustness
Axiomatic Compliance
Quality performance Index
Water Reservoir
One Way Valve
8.5
7.25
7.5
8.21
Water Intake Cover
9.5
9
8.25
9.25
Heating Element
1.5
1.5
1.5
1.8
Hot Water Tube
4.5
5.5
5.5
4.5
Cold Water Tube
4.5
3.5
2.5
2.8
Aluminum Tube
1
1
1
1.2
Hot Water Tube
1.5
1
1
1.25
Cold Water Tube
9.0
6.0
4.5
7.52
Aluminum Tube
7.5
3.5
4.5
5.5
One way valve
Engineering judgments using guidelines April 21, 2006
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Using Fuzzy Logic 18
Coffeemaker: Modular Architecture Module-1 Top Surface, Water Intake Cover, Water Reservoir Dripping Surface
Module-2 One Way Valve, Hot Water Tube, Cold Water Tube, Aluminum Tube Module- 4 Heating Element, Warm. Plate, Bottom Cover April 21, 2006
Module-5 Cable, Connectors, Plate Sensors, Indicator Lights IME Graduate Research Symposium, 2006
Module-3 Brew Basket, Exit Valve Module- 6 Decanter, Handle, Decanter Cover 19
Coffeemaker: Post Optimality Analysis Most unwanted deviation
80 70 60 50 40 30 20 10 0 0
1
2
3
4
5
6
7
8
9
Num ber of m odules
Optimal number of modules April 21, 2006
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Research Contributions Developed a framework for incorporating DFQ in modular product design Developed a Fuzzy Logic based model for exploiting subjective product knowledge at conceptual stage Enhanced current DFQ techniques by integrating architectural issues and considering them early on Provided a generic framework that can be used for optimizing multiple design and performance objectives
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Thank you!
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