ESD.33 Systems Engineering
Lecture 6 Requirements Driven Systems
Design
Qi Van Eikema Hommes
Course Layout
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Lecture 7 CPM and DFSS
Lecture 2 Systems Engineering As Human AcKvity
Lecture 4 Stakeholder Analysis and Requirements DefiniKon
Lecture 5 innovaKon in Systems Engineering Lecture 6 AxiomaKc Design and DM‐DSM
Method
Lecture 8 Trade Space ExploraKon
Concept SelecKon
Lecture 10: Experiments Lecture 11: Robust Design I Lecture 12: Robust Design II
Lecture 13 Design VerificaKon
and ValidaKon, Lifecycle
Management
✔ ✔
✔
Lecture Outline IntroducKon to AxiomaKc Design
Four domains
Axiom 1—Independence Axiom Design Matrix
Zigzagging Constraints
Axiom 2—InformaKon Axiom
Design Structure Matrix for Technical Systems
DM—DSM Method Qi Van Eikema Hommes
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The Founder of AxiomaKc Design Theory
• Nam Pyo Suh—MIT Professor Emeritus.
• B.S., Mechanical Engineering, 1959, M.S., Mechanical Engineering, 1961, MIT. • Ph.D, Mechanical Engineering, 1964, Carnegie Mellon University.
• From 1965‐1969, Suh served as a professor at the University of South Carolina. In 1970
he began his professional career at MIT‐‐ serving as director of the MIT‐Industry
Polymer Processing Program from 1973‐1984; director of the Laboratory for
Manufacturing and ProducKvity from 1977‐1984; and Mechanical Engineering
Department Head from 1991 to 2001. Although sKll keeping the Ktle of Ralph E.
Cross Professor of Mechanical Engineering at MIT, Suh is now president of KAIST.
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The Goals of AxiomaKc Design • Establish a scienGfic basis for design • Improve design acKviKes by providing the designer with a theoreKcal foundaKon based on logical and raGonal thought processes and tools.
• Make human designers more creaGve
• Reduce the random search process
• Minimize the iteraKve trial and error process
• Determine the best designs among those proposed
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Suh, Axiomatic Design, 2000, page 5
6/24/10
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•� ��������an interplay between what we want �achieve and how we will achieve it.
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What
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The Four Domains of Design
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Mapping Mapping Mapping
{PVs}{DPs}{FRs}{CAs}
Customer domain Functional domain Physical domain Process domain
Image by MIT OpenCourseWare.
DefiniKons • Customer AOribute (CA)—what customer desire from a product
• FuncGonal Requirement (FR)—minimum set of independent requirements that completely characterize the funcKonal needs of the product in the funcKonal domain.
• Design Parameter (DP)—Key physical variables in the physical domain that characterize the design that saKsfies the specified FRs.
• Process Variables (PV)—key variables in the process domain that characterize the process that can generate the specified DPs.
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Benefits of the Domains • Customer Needs are stated in the customer’s language
• FuncKonal Requirements and Constraints are determined to saKsfy Customer Needs
• “The FRs must be determined in a soluKon neutral environment” (or, in other words, say “what” not “how”)
– BAD = the adhesive should not peel – BETTER = the amachment should hold under the following loading condiKons
• Provide Requirements Traceability Qi Van Eikema Hommes
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Lecture Outline IntroducKon to AxiomaKc Design
Four domains
Axiom 1—Independence Axiom Design Matrix
Zigzagging
Axiom 2—InformaKon Axiom
Design Structure Matrix for Technical Systems
DM—DSM Method
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Axiom • Axioms are truths that cannot be derived but for which there are no counter examples or excepKons.
• Examples of Axioms: – First and second law of thermodynamics
– Newton’s three law of mechanics
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How were the Design Axioms Created?
• IdenKfying the common elements that are present in all good designs: – How did I make such a big improvement in a process?
– How did I create the process? – What are the common elements in good designs?
• Use logical reasoning process to reduce the observaKons to two Axioms.
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The Two Axioms
• Axiom 1: Independence Axiom—maintain the independence of funcKonal requirements (FRs).
• Axiom 2: The InformaGon Axiom—minimize the informaKon content of the design.
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Mapping Mapping Mapping
{PVs}{DPs}{FRs}{CAs}
Customer domain Functional domain Physical domain Process domain
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Image by MIT OpenCourseWare.
Design Matrix
{FR} = [A] {DP}
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€
FR1FR2FR3
=
A11 A12 A13A21 A22 A23A31 A32 A33
•
DP1DP2DP3
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Design Matrix Example
• FR1 = Provide access to the items stored in the refrigerator
• FR2 = Minimize energy loss
• DP1 = VerKcally hung door • DP2 = Thermal insulaKon material in the door
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€
FR1FR2
=x 0x x
DP1DP2
Suh, Axiomatic Design, 2000
Image by MIT OpenCourseWare.
A Different Design • FR1 = Provide access to the items stored in the refrigerator
• FR2 = Minimize energy loss
• DP1 = Horizontal door • DP2 = Thermal insulaKon material in the door
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€
FR1FR2
=x 00 x
DP1DP2
Image by MIT OpenCourseWare.
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Uncoupled
Design
Decoupled
Design
Coupled
Design
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
FR DP
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Axiom 1: Independence Axiom
• To saKsfy the Independence Axiom, the design matrix must be either diagonal or triangular.
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€
A11 0 00 A22 00 0 A33
€
A11 0 0A21 A22 0A31 A32 A33
Uncoupled Design
Decoupled Design
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Water Faucet Example
• FuncKonal Requirements: – FR1: Adjust the water temperature (T)
– FR2: Adjust the water volume (Q)
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What is the Design Matrix?
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Image by MIT OpenCourseWare.
What is the Design Matrix?
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Image by MIT OpenCourseWare.
What is the Design Matrix?
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Image by MIT OpenCourseWare.
FuncKonal Coupling vs Physical Coupling
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# of parts ≠ # of DPs
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Image by MIT OpenCourseWare.
Why MeeKng Axiom 1 is Desirable?
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Lecture Outline IntroducKon to AxiomaKc Design
Four domains
Axiom 1—Independence Axiom Design Matrix
Zigzagging Constraints
Axiom 2—InformaKon Axiom
Design Structure Matrix for Technical Systems
DM—DSM Method Qi Van Eikema Hommes
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Zig Zagging
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FR
FR1
FR11
FR121
FR1231 FR1232
FR12
FR122 FR123
FR2
DP
DP1
DP11
DP121
DP1231 DP1232
DP12
DP122 DP123
DP2
Functional domain Physical domain
Image by MIT OpenCourseWare.
Refrigerator Design Example • FR1 = Freeze food for long‐term preservaKon
• FR2 = Maintain food at cold temp for short‐term preservaKon
• DP1 = the freezer secKon • DP2 = the chiller (refrigerator) secKon
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€
FR1FR2
=x 00 x
DP1DP2
Image by MIT OpenCourseWare.
Decompose the System FR1 = Freeze food for long term preservaKon
FR11 = Control freezer temp
FR12 = Maintain uniform freezer temp FR13 = Control freezer humidity
FR2 = Maintain food at cold temp for short term preservaKon FR21 = Control chiller temp
FR22 = Maintain uniform chiller temp DP1 = The freezer secKon
DP11 = Sensor/compressor system for freezer secKon
DP12 = Air circulaKon system for freezer secKon
DP13 = Condenser that condenses the moisture in the air when dew point is exceeded
DP1 = The chiller secKon DP21 = Sensor/compressor for chiller secKon
DP22 = Air circulaKon system for chiller secKon Qi Van Eikema Hommes
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What Does The Design Matrix Look Like?
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New Cooling System Refrigerator
Capillary tube
Cold air
Compressor
Freezing room
Refrigerating room
F-Fan
R-Fan
Two cooling fan type
Cold air flow
Freezer fan Fridge fan
A cold-air circulation system with many vents.A schematic drawing of a new refrigerator design.
EvaporatorCondenser
Image by MIT OpenCourseWare.
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DP1 DP2
FR1
FR2
DP12 DP11 DP13 DP22 DP21
0
0
00
0 0
0
0 0 0
0 0
0
0
0
0 0
FR11
FR13
FR22
FR21
FR12
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Can We Save the Cost of a Fan?
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Conventional Refrigerator
Capillary tube
Cold air
Compressor
Freezing room
Refrigerating room
Fan
Damper
One cooling fan type
Schematic drawing of a conventional refrigerator.
EvaporatorCondenser
Freezer and fridge fan
Cold air flow
Cold-air circulation in a conventional refrigerator..
Image by MIT OpenCourseWare.
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DP1 DP2
DP12 DP11 DP13 DP22 DP21
FR1 FR12 x 0 0 0 0
FR11 x x 0 0 0
FR13 x 0 x 0 0
FR2 FR22 0 0 0 x 0
FR21 0 0 0 x x X
X
X
X
Coupled design!
Benefits So Far from Axiom 1
• Reduce system coupling early on. • Start the design with requirements first.
• Think about the design concept first before applying robust engineering or opKmizaKon blindly.
• Zig‐zagging instead of staying in one domain.
• Requirements traceability and raKonale.
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Class Discussions
• How does Zig‐zagging help design synthesis? • How does your organizaKon decompose systems and requirements?
• Does this help with requirements traceability throughout the design?
• How does AxiomaKc Design differ from QFD?
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Lecture Outline IntroducKon to AxiomaKc Design
Four domains
Axiom 1—Independence Axiom Design Matrix
Zigzagging Constraints
Axiom 2—InformaKon Axiom
Design Structure Matrix for Technical Systems
DM—DSM Method Qi Van Eikema Hommes
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Constraints in AxiomaKc Design • Constrant (C)—are bounds on acceptable soluKons. Input constraints are imposed as part of the design specificaKon. System constraints are constraints imposed by the system in which the design soluKon must funcKon.
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Constraints • Two types of constraints:
– Input constraints—specific to the overall design goals (all design proposed must saKsfy these). • Example: cost
– System constraints—specific to a given design (they are the result of design decisions made). • Example: Diesel engine tailpipe emission standards for diesel engines
• What kind of constraint is Safety?
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What AxiomaKc Design Says about Constraints
• “Constraints provide bounds on the acceptable design soluKons and differ from the FRs in that they do not have to be independent.”
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Lecture Outline IntroducKon to AxiomaKc Design
Four domains
Axiom 1—Independence Axiom Design Matrix
Zigzagging
Axiom 2—InformaKon Axiom
Design Structure Matrix for Technical Systems
DM—DSM Method
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InformaKon Content • InformaKon Content Ii for a given FRi is defined in terms of the probability Pi of saKsfying FRi: Ii = log2(1/Pi)=‐ log2(Pi)
• When there are m FRs,
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Isys = −log2(Pm ) = − log2 Pii=1
m
∑
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Bias
Design range
System range
Probability density
Target
System pdf
Area within common range (Acr)
FR
Pi
Image by MIT OpenCourseWare.
Axiom 2 InformaKon Content
• The InformaKon Axiom—Minimize informaKon content I.
• Maximize the probability of meeKng FRs.
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€
Isys = −log2(Pm ) = − log2 Pii=1
m
∑
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Example of Buying a House Suh, AxiomaKc Design, 2001
• FR1: Commute Kme 15 – 30 minutes
• FR2: Quality of School (65% or more highschool graduates go to colleges)
• FR3: Quality of air is good over 340 days a year • FR4: price of house (4 BR, 3000 x^2, less than 650K)
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Town FR1 = commute Kme (min)
FR2=Quality of schools (%)
FR3=Quality of air (days)
FR4=Price($)
A 20‐40 50‐70 300‐320 450‐550k
B 20‐30 50‐75 340‐350 450‐650k
C 25‐45 50‐80 350+ 600‐800k
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InformaKon Content CalculaKon Suh, AximaKc Design, 2001
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Town I1 [bits] I2 [bits] I3 [bits] I4 [bits] Sum (I) [bits]
A 1.0 2 infinite 0 Infinite
B 0 1.32 0 0 1.32
C 2.0 1.0 0 2 5
I1 = -log2[(30-20) / (40-20)] = -log2(0.5) = 1
15 20 30 40
Design Range System Range
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Axiom 2 and Robust Design
• “The InformaKon Axiom provides a theoreKcal foundaKon for robust design.” – EliminaKon of bias
– ReducKon of Variance • Reduce sensiKvity to variaKon • MeeKng the Independence Axiom • Minimize random variaKon
• Increase design range – Integrate DP in a single physical part
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Comparison of AxiomaKc Design with Other Methods (Suh, 2001)
• Robust design cannot be accomplished by applying the Taguchi method if the design violates the Independence Axiom.
• OpKmizaKon of a bad design may lead to an opKmized bad design or minor improvements.
• How is AxiomaKc Design similar/different from QFD?
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QuesKons about the Axioms
• Too good to be true? What about constraints? • Are interacKons so bad? That’s what makes a system great! – DefiniKon of System‐‐A combinaKon of interacKng elements organized to achieve one more stated purposes.
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Lecture Outline IntroducKon to AxiomaKc Design
Four domains
Axiom 1—Independence Axiom Design Matrix
Zigzagging Constraints
Axiom 2—InformaKon Axiom
Design Structure Matrix for Technical Systems
DM—DSM Method Qi Van Eikema Hommes
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Matrix Representation of a Network--The Design Structure Matrix (DSM)
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A B C D E F G H I J KA
B
C
D
E
F
G
H
I
J
K
1 1
1
1
1
1
1
1
1
1 111
1 1
1 1
Below Diagonal Marks ___ upstream task feeds informationto downstream tasks
Above Diagonal Marks ___ downstream task feeds informationto upstream tasks.Potential rework.
F J
H
BA
E
C
G
I
D
KK provides inputs to A
DSM is the adjacency matrix of a network graph
Image by MIT OpenCourseWare.
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ParKKoning a DSM Before Partition After Partition
Partitioning identifies truly coupled elements.
A B C D E F G H I J KA A
B BC C
D D
E EF FG G
H HI I
J JK K
x x
x
x
x
x
x
x
x
x xxx
x x
x x
J H D F E G C I B K AJ J
H HD D
F F
E EG GC C
I IB B
K K
A A
xx
x
x
xxxxxx
x x
xxx x
x
K
E
A
CG
B
I
F J
H
D
J
F I CB
E G
K
A
DHLevel 1
Level 1
Level 2
Level 2
Level 3
Level 3
Level 4
Level 4
Image by MIT OpenCourseWare.
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Car Door System Design
Halo cornerHalo (sections)Header seals
B pillarGlass runs
Glass
Belt seals joint with glass
Belt seals
Regulator arms
Below belt retainer
Access hole
Equalizer channel
Motor
2
7
8
6
5
39
1
10
AA pillar
Mirror sail
Sheet Metal
Electrical System
Moveable Glass System
Image by MIT OpenCourseWare.
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Car Door System Engineering Process (Before ParKKoning DSM)
Sheet Metal Subsystem
Moveable Glass Subsystem
Electrical Subsystem
50+ people system interface engineering meetings
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Car Door System Engineering Process (Axer ParKKoning)
Belt and Above Belt Frame
Glass and Track
Power and Motion
Electrical Packaging
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The Control Soxware System
• What can I do about this web of interacKons?
• How can I convince management that changes are needed? • How do I know I actually improved the architecture?
• 1 production-level software • 117 software modules (red dots) • 1423 binary interactions (black lines) • 39 such production software releases per year • <2 weeks per release
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DSM of the Control System Soxware
• What can I do about this web of interactions? • How do I know I actually improved the architecture?
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Comparison of Various Modularity Metrics
Use Whitney Index and Change Cost to measure modularity improvements.
Use network centrality indices to identify system elements for improvement.
DETC2008-DTM-49140
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Whitney Index Comparison
Embedded Software System A
Embedded Software System B
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Change Cost Comparison Observation: Embedded control software systems are more like hardware systems, less like pure software products.
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Network Centrality—Degree Centrality
In degree—how many others pass information to the element of interest. Out degree—how many others depend on the element of interest for information.
Degree Centrality identifies which few elements, if any, in the system have a central effect on the rest of the systems.
However, the metrics values don’t correlate well with components modularity.
(Sosa, Eppinger, Rowles 2007, Borgatti, Everett, and Freeman, 2002, UCINET)
A B C D E F G H1
1
1
1
1
1
1
1
1 1
1
1
1
A
B
C
D
EFG
H
1
1
1
1
11 1
11
A B C D E F G H1
1
1
1
1
1
1
1
11
11
1
1
A
B
C
D
EF
GH
1
1
1
1
11 1
1 11
A B C D1 1 1 1
1 1 1 11 1 1 1
1 1 1 1
E F G H
1 1 1 11 1 1 11 1 1 1
1 1 1 1
A
BC
D
EFG
H
1
11
1
111
1
1
11
1
111
1
85.70% 85.70%
2
22
2
222
2
2
22
2
222
2
0% 0%
3
33
3
333
3
3
33
3
333
3
FreemanCentralityin degree
FreemanCentralityout degree
FreemanCentralityOverall in
FreemanCentrality
Overall out
0% 0%
A B C D E F G H1
1
1
1
1
1
1
1
1 1
1
1
1
A
B
C
D
EFG
H
1
1
1
1
11 1
11
A B C D E F G H1
1
1
1
1
1
1
1
11
11
1
1
A
B
C
D
EF
GH
1
1
1
1
11 1
1 11
A B C D1 1 1 1
1 1 1 11 1 1 1
1 1 1 1
E F G H
1 1 1 11 1 1 11 1 1 1
1 1 1 1
A
BC
D
EFG
H
7
11
1
111
1
7
11
1
111
1
85.70% 85.70%
2
22
2
222
2
2
22
2
222
2
0% 0%
3
33
3
333
3
3
33
3
333
3
FreemanCentralityin degree
FreemanCentralityout degree
FreemanCentralityOverall in
FreemanCentrality
Overall out
0% 0%
Matrix 4
Matrix 5
Matrix 8
Image by MIT OpenCourseWare.
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Network Centrality
• Network centrality metrics can idenKfy the few elements that have the largest impact on the system.
• If the network has central players, the network may be bus‐modular.
• If the network does not have central player, the network system is either not connected, or highly integral.
• Central players can be the priority for system complexity reducKon strategy.
(Sosa, Eppinger, Rowles 2007, Borgatti, Everett, and Freeman, 2002, UCINET)
DSM Method
• Capture system interacKons • Analyze and improve system architecture and system interfaces.
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Lecture Outline IntroducKon to AxiomaKc Design
Four domains
Axiom 1—Independence Axiom Design Matrix
Zigzagging Constraints
Axiom 2—InformaKon Axiom
Design Structure Matrix for Technical Systems
DM—DSM Method Qi Van Eikema Hommes
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63
ExisKng Methods Concerning System InteracKons Design Structure Matrix (DSM)
Axiomatic Design’s Design Matrix (DM)
Requirements Management
What We Want
Provide analytical system analysis
Yes Yes
Allow iterations and feedback loops
Yes Yes
Relate the requirements to the system design
Yes Yes
Can be applied in the early design phases
Yes Yes Yes
Provide complete understanding of all requirements
Yes Yes
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Solving System of Linear EquaKons
Question: 3 * x1 + 5 * x2 = 6 (1) 2 * x1 - x2 = 4 (2) What is x1 and x2?
Solving by substitution: Select x1 as the output variable in (1): x1 = (6 – 5 * x2 ) / 3 Select x2 as the output variable in (2): x2 = 2 * x1 – 4 = 2 * (6-5*x2)/3 - 4 x1=2 x2=0
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ConverKng a DM into a DSM
1. Construct an Axiomatic Design’s Design Matrix.
2. Select Output Variables. DP3 = f (FR1, DP1) DP1 = f (FR2, DP2) DP2 = f (FR3, DP3)
3. Permute the matrix by row so that the output variables are on the diagonal. We get a precedence matrix (DSM) of the Design Parameters.
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X X 0 FR3
0 X X FR2
X 0 X FR1
DP3 DP2 DP1
X X 0 FR3
0 X X FR2
X 0 X FR1
DP3 DP2 DP1
X 0 X DP3
X X 0 DP2
0 X X DP1
DP3 DP2 DP1
66
SelecKng Output Variables
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DP2
DP4
DP3
DP4
DP3 DP2
67
CVC Cluster Machines
Central Wafer Handler
Wafer Processing Module
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Courtesy of KDF electronics. Used with permission.
68
CVC Electro‐staKc Chuck (ESC)
Wafer
Electro-statically charged plate
Cooling Plate
Plate for interface with various process modules
Standard interface on all process modules Backside Gas,
Cooling Water, Electricity
Process Chamber
Backside gas channel
ESC
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System View of ESC
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Process Module 2
Process Module 4
Process Module 3
Process Module
Wafer Transport Robot
Control and Logistics
Wafer Processing Cluster Machine
ESC
ESC ESC
ESC
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Design Structure Matrix Built from Design Matrix
Heat Transfer Package the ESC into the Existing Process Modules
Control Circuit Design
Heat Transfer
Packaging into the Modules
Control Circuit
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The SelecKon of Output Variables
Choosing non-diagonal elements in the DM as output variable set is like designing components not for their main functional purposes, but for their side effects. The resulting DSM is a non-executable design process.
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The SelecKon of Output Variables
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DP1 = 1/0.75 * ! FR1 - 0.2/0.75* DP2 DP2 = 1/0.9 * ! FR2 - 0.2/0.9 * DP1
Eigen Value = 0.243 This process converges.
Eigen Value = 4.1 This process does NOT converge.
DP1 DP2 FR1 0.75 0.2 FR2 0.2 0.9
DP1 DP2 DP1 0 0.2/0.75 DP2 0.2/0.9 0
DP1 DP2 FR1 0.75 0.2 FR2 0.2 0.9 DP1 = 1/0.2 * ! FR1 Ð 0.75/0.2* DP2 DP2 = 1/0.2 * ! FR2 - 0.9/0.2* DP1
DP1 DP2 DP1 0 0.75/0.2 DP2 0.9/0.2 0
ΔΔ
ΔΔ Δ
ΔΔ Δ
Δ ΔΔ
Δ
ΔΔ
Δ
Δ
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The Interchangeability of DM and DSM
DM
DSM
The diagonal elements in the DM are the dominant elements in their corresponding rows.
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Johnson and Johnson Ortho‐Clinical DiagnosKcs OASIS Analyzer
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Image of Vitros 5.1 cluster removed due to copyright restrictions.
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OASIS Major Subsystems
Machine Control (MACO) Application Services (APPS)
Sample Handling (SAHA)
Power Distribution (POWR)
Primary Sample Metering (SRME)
Reagent Supply (RGSU)
Secondary Sample Metering (SRME)
Frame and Cabinetry (STRU)
Cuvette Incubator (CUIN)
Aliquot Buffer (ALBU)
Slide Incubator (SLIN)
Photometer (PHMT)
Microtip Loading (MTLD)
Sample Integrity (SAIN)
Vitros Tips Loader (VTLD)
Slide Supply (SLSU)
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Case Study ObjecKves 1. Build a DSM from requirements using the DM‐
DSM conversion method; 2. Compare the resulKng DSM with the DSM
experts built using tradiKonal DSM construcKon method.
3. Understand which types of requirements can be used to predict system interacKons. Judge whether the predicKon DSM is complete.
4. Aid the system integraKon manager’s work on planning and managing OASIS subsystem interfaces.
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DSM Constructed from Requirements
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Compare Requirements DSM with Expert DSM
System Interactions Predicted by DSM from requirements. System Interactions Predicted by JNJ engineers. System Interactions Predicted by both the requirements and JNJ engineers.
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How Many Marks Match
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There are 54 marks captured by both the experts DSM and the DSM from requirements.
The requirements prediction DSM missed 118 interactions captured by the experts.
The experts did not capture 75 interfaces predicted by the requirements.
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Analyzing the Unmatched Marks
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The Achievable PotenKal
Providing: • JNJ engineers involve soxware engineers in the DSM building exercise
• Chemists write assay requirements • JNJ updates the trace‐ability between product level requirements and subsystem level requirements
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JNJ experts miss 6 marks.
DSM from requirements misses 26 marks. 215
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LimitaKon of the Method
Can all requirements be decomposed to predict system interactions?
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Requirements DM DSM
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Requirements DecomposiKon Can predict system interactions
Cannot predict system interactions
Can be decomposed in the same way as the FR’s in the Axiomatic Design
Functional, Maintainability, Operational, Environment Expandability, Appearance
None
Can be decomposed but not in the same way as decomposing the FR’s in the Axiomatic Design
Performance (Modeling) Packaging (DFC, DSM) Design Constraints (DSM)
Reliability (budgeting) Size (budgeting) Weight (budgeting) Cost (budgeting)
Difficult to decompose
None Installation Standards Safety DFMAS Component Reuse Operability Shipping
No strong evidence in this case study
Disposal Distribution Training Budget and Timing Patents
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Comparison of the Three Methods
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AxiomaKc Design Matrix (DM)
Uncoupled Design
Decoupled Design
DM - DSM Reduce the amount of
coupling through good design.
Manage the inevitable coupling when a coupled design makes more business sense.
DSM shows the bottleneck in systems and ultimately drive people toward Axiomatic Design preferred results.
DSM
Avoids coupling by smart engineering design.
Accepts coupling and manage it by streamlining the process, or modularizing the system architecture.
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J H D F E G C I B K AJ J
H HD D
F F
E EG GC C
I IB B
K K
A A
xx
x
x
xxxxxx
x x
xxx x
x
Image by MIT OpenCourseWare.
X
X
X X
X
Summary of DM‐DSM Method • We can get a DSM from a DM • The diagonal elements are the output variables in matrix conversion
• Not all system interacKons can be predicted from DM
• Coupled design can be managed and improved using DSM.
• Do think about reducing system coupling by exploring alternaKve design concepts first.
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Lecture Summary IntroducKon to AxiomaKc Design
Four domains
Axiom 1—Independence Axiom Design Matrix
Zigzagging
Axiom 2—InformaKon Axiom
Design Structure Matrix for Technical Systems
DM—DSM Method
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MIT OpenCourseWarehttp://ocw.mit.edu
ESD.33 Systems EngineeringSummer 2010
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