ESD.33 Systems Engineering
Lecture 6 Requirements Driven Systems Design Qi Van Eikema Hommes Course Layout ✔
Lecture 4 Stakeholder Analysis and Requirements DefiniKon ✔
Lecture 2 Systems Engineering As Human AcKvity Lecture 5 innovaKon in Systems Engineering Lecture 6 AxiomaKc Design and DM‐DSM Method ✔
Lecture 13 Design VerificaKon and ValidaKon, Lifecycle Management Lecture 7 CPM and DFSS Lecture 8 Trade Space ExploraKon Concept SelecKon Lecture 10: Experiments Lecture 11: Robust Design I Lecture 12: Robust Design II 6/24/10 Qi Van Eikema Hommes
2 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 6/24/10 Qi Van Eikema Hommes 3 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. 6/24/10 Qi Van Eikema Hommes 4 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 Suh, Axiomatic Design, 2000, page 5
6/24/10 Qi Van Eikema Hommes 5 • an interplay between what we want
achieve and how we will achieve it.
What
6/24/10 Qi Van Eikema Hommes 5 The Four Domains of Design Mapping
Mapping
Mapping
{CAs}
{FRs}
{DPs}
Customer domain
Functional domain
Physical domain
{PVs}
Process domain
Image by MIT OpenCourseWare.
6/24/10 Qi Van Eikema Hommes 7 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. Qi Van Eikema Hommes 6/24/10 8 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 6/24/10 Qi Van Eikema Hommes 9 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 6/24/10 Qi Van Eikema Hommes 10 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 6/24/10 Qi Van Eikema Hommes 11 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. 6/24/10 Qi Van Eikema Hommes 12 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. 6/24/10 Qi Van Eikema Hommes 13 Mapping
{CAs}
Mapping
{FRs}
{DPs}
Customer domain
Functional domain
Mapping
{PVs}
Physical domain
Process domain
Image by MIT OpenCourseWare.
Design Matrix {FR} = [A] {DP}  FR1   A11

 
 FR2  =  A21
 FR   A
 3   31
6/24/10 A12
A22
A32
A13   DP1 

 
A23  •  DP2 


A33   DP3 
Qi Van Eikema Hommes 15 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  FR1  x

=
 FR2  x
0  DP1


x  DP2
Image by MIT OpenCourseWare.
Suh, Axiomatic Design, 2000
6/24/10 Qi Van Eikema Hommes 16 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  FR1  x

=
 FR2  0
6/24/10 0  DP1


x  DP2
Qi Van Eikema Hommes Image by MIT OpenCourseWare.
17 Uncoupled
Design
A11
0
0
Decoupled
Design
A11
A21
A31
Coupled
Design
A11
A21
A31
6/24/10 0
A22
0
0
A22
A32
A12
A22
A32
FR
DP
1
1
2
2
3
3
0
0
A33 1
1
2
2
3
3
A13 A23 A33 1
1
2
2
3
3
0
0
A33 Qi Van Eikema Hommes 18 Axiom 1: Independence Axiom •  To saKsfy the Independence Axiom, the design matrix must be either diagonal or triangular. A11

0
 0
0
A22
0
0

0
A33 
Uncoupled
Design
€
6/24/10  A11

A21
A31
€
Qi Van Eikema Hommes 0
A22
A32
0

0
A33 
Decoupled
Design
19 Water Faucet Example •  FuncKonal Requirements: –  FR1: Adjust the water temperature (T) –  FR2: Adjust the water volume (Q) 6/24/10 Qi Van Eikema Hommes 20 What is the Design Matrix?
Image by MIT OpenCourseWare.
6/24/10 Qi Van Eikema Hommes 21 What is the Design Matrix? Image by MIT OpenCourseWare.
6/24/10 Qi Van Eikema Hommes 22 What is the Design Matrix? Image by MIT OpenCourseWare.
6/24/10 Qi Van Eikema Hommes 23 FuncKonal Coupling vs Physical Coupling Image by MIT OpenCourseWare.
# of parts ≠ # of DPs
6/24/10 Qi Van Eikema Hommes 24 Why MeeKng Axiom 1 is Desirable? 6/24/10 Qi Van Eikema Hommes 25 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 6/24/10 Qi Van Eikema Hommes 26 Zig Zagging Functional domain
Physical domain
FR
DP
FR1
DP1
FR2
FR11
FR12
FR121
FR122
FR123
FR1231
DP2
DP11
DP12
DP121
DP122
DP123
DP1231
FR1232
DP1232
Image by MIT OpenCourseWare.
6/24/10 Qi Van Eikema Hommes 27 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  FR1  x

=
 FR2  0
6/24/10 0  DP1


x  DP2
Qi Van Eikema Hommes Image by MIT OpenCourseWare.
28 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 6/24/10 Qi Van Eikema Hommes 29 What Does The Design Matrix Look Like? Freezer fan
Fridge fan
Two cooling fan type
Capillary
tube
F-Fan
Freezing room
Cold air
Condenser
Cold air flow
Evaporator
R-Fan
Refrigerating
room
Compressor
New Cooling System Refrigerator
A schematic drawing of a new refrigerator design.
A cold-air circulation system with many vents.
Qi Van Eikema Hommes 6/24/10 Image by MIT OpenCourseWare.
30 FR1
FR2 DP1 DP12 DP11
FR12 0
FR11 FR13 0
0 0 FR22 0 FR21 0 DP2
DP13 DP22 DP21
0 0
0 0 0 0 0 0 0 0 0 6/24/10 Qi Van Eikema Hommes 31 Can We Save the Cost of a Fan? Freezer and fridge fan
One cooling fan type
Capillary
tube
Fan
Freezing room
Cold air
Evaporator
Cold air flow
Condenser
Damper
Refrigerating
room
Compressor
Conventional Refrigerator
Schematic drawing of a conventional refrigerator.
Cold-air circulation in a conventional
refrigerator..
Image by MIT OpenCourseWare.
6/24/10 Qi Van Eikema Hommes 32 FR1 FR2 DP1 DP2 DP12 DP11 DP13 DP22 DP21 X
FR12 x 0 0 0 0 X
FR11 x x 0 0 0 FR13 x 0 x 0 0 FR22 0 0 0 x 0 FR21 0 0 0 x x X
X
Coupled design!
6/24/10 Qi Van Eikema Hommes 33 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. 6/24/10 Qi Van Eikema Hommes 34 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? 6/24/10 Qi Van Eikema Hommes 35 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 6/24/10 Qi Van Eikema Hommes 36 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. 6/24/10 Qi Van Eikema Hommes 37 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? 6/24/10 Qi Van Eikema Hommes 38 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.” 6/24/10 Qi Van Eikema Hommes 39 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 6/24/10 Qi Van Eikema Hommes 40 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) Probability density
Target
•  When there are m FRs, Bias
Design range
System pdf
m
Isys = −log 2 (Pm ) = −∑ log 2 Pi
i=1
Pi
Area within
common
range (Acr)
System range
FR
Image by MIT OpenCourseWare.
€
6/24/10 Qi Van Eikema Hommes 41 Axiom 2 InformaKon Content •  The InformaKon Axiom—Minimize informaKon content I. •  Maximize the probability of meeKng FRs. m
Isys = −log 2 (Pm ) = −∑ log 2 Pi
i=1
6/24/10 Qi Van Eikema Hommes 42 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) Town FR1 = commute Kme (min) FR2=Quality FR3=Quality of schools (%) 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 Qi Van Eikema Hommes 6/24/10 43 InformaKon Content CalculaKon Suh, AximaKc Design, 2001 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 Design Range
15
20
System Range
30
40
I1 = -log2[(30-20) / (40-20)] = -log2(0.5) = 1
6/24/10 Qi Van Eikema Hommes 44 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 6/24/10 Qi Van Eikema Hommes 45 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? 6/24/10 Qi Van Eikema Hommes 46 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. 6/24/10 Qi Van Eikema Hommes 47 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 6/24/10 Qi Van Eikema Hommes 48 Matrix Representation of a Network
--The Design Structure Matrix (DSM)
DSM is the adjacency matrix of a network graph
A B C D E
A
1
B
1
C
1
F
G H
I
J
K
1
1
1
Above Diagonal Marks ___ downstream
task feeds information
to upstream tasks.
Potential rework.
K provides inputs to A
D
K
E
F
G
1
1
1
1
H
A
1
1
J
1
H
I
F
1
1
B
G
E
J
K
1
1
C
Below Diagonal Marks ___ upstream
task feeds information
to downstream tasks
D
I
Image by MIT OpenCourseWare.
Qi Van Eikema Hommes
49
ParKKoning a DSM Before Partition
A B
C D E
A A
B
I
J
x
B
C
F G H
After Partition
K
J
J
x
C
D
x
x
F
D
E
E
x
F
F
G
x
x
x
G
x
H
I
x
x
x
I
x
J
J
K
x
K
x
K
E
F
J
J
H
A
x
F
x
E
x
G
x
G
x
x
x
C
x
x
x
x
I
x
B
x
K
x
A
x
F
C
D
I
Level 2
B
Level 3
Level 4
K
x
x
H
B
G
Level 1
E
I
B K A
D
C
H
I
H
D
x
E G C
J
H
x
H D F
D
A
Level 1
I
C
E
G
Level 2
K
Level 3
B
A
Level 4
Image by MIT OpenCourseWare.
Partitioning identifies truly coupled elements.
6/24/10 Qi Van Eikema Hommes 50 Car Door System Design Moveable Glass System
Header seals Halo (sections)
5
10
3
A
A pillar 9
Halo corner
B pillar
Glass runs
2 Glass
Belt seals joint with glass
Belt seals
1
Mirror sail
Regulator arms
6 Below belt retainer
Sheet Metal
Access hole
8
Motor
7
Equalizer channel
Electrical System
Image by MIT OpenCourseWare.
6/24/10 Qi Van Eikema Hommes 51 Car Door System Engineering Process (Before ParKKoning DSM) Sheet Metal
Subsystem
Moveable Glass
Subsystem
50+ people system
interface engineering
meetings
Electrical
Subsystem
6/24/10 Qi Van Eikema Hommes 52 Car Door System Engineering Process (Axer ParKKoning) Belt and Above Belt Frame
Glass and Track
Power and Motion
Electrical Packaging
6/24/10 Qi Van Eikema Hommes 53 The Control Soxware System •
•
•
•
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
•  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? 4/8/2010 Copyright Qi D. Van Eikema Hommes 54 DSM of the Control System Soxware •
•
What can I do about this web of interactions?
How do I know I actually improved the architecture?
4/8/2010 Copyright Qi D. Van Eikema Hommes 55 Comparison of Various Modularity Metrics Use Whitney Index and
Change Cost to measure
modularity improvements.
4/8/2010 Use network centrality
indices to identify system
elements for improvement.
Copyright Qi D. Van Eikema Hommes DETC2008-DTM-49140
56 Whitney Index Comparison Embedded Software
System B
Embedded
Software
System A
4/8/2010 Copyright Qi D. Van Eikema Hommes 57 Change Cost Comparison Observation: Embedded control software
systems are more like hardware systems,
less like pure software products.
4/8/2010 Copyright Qi D. Van Eikema Hommes 58 Network Centrality—Degree Centrality
(Sosa, Eppinger, Rowles 2007, Borgatti, Everett, and Freeman, 2002, UCINET)
Matrix 4
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.
AA BB CC
11 11 11
11 11 11
CC 11 11 11
DD 11 11 11
AA
BB
EE
FF
DD EE FF GG HH
11
11
11
11
GG
HH
11 11 11
11 11 11
11 11 11
11 11 11
11
11
11
11
Freeman
Freeman
Centrality
Centrality
inindegree
degree
Freeman
Freeman
Centrality
Centrality
out
outdegree
degree
33
33
33
33
33
33
33
33
33
33
33
33
33
33
Freeman
Freeman
Centrality
Centrality
Overall
Overallinin
0%
0%
Freeman
Freeman
Centrality
Centrality
Overall
Overallout
out
0%
0%
33
33
Matrix 5
AA
BB
CC
DD
EE
AA BB CC DD EE FF GG HH
11 11 11 11 11 11 11 11
11 11
11
11
11
FF 11
GG 1
1
11
11
11
DD
EE
FF
GG
HH
71
11
11
11
11
11
11
11
11
11
11
11
11
BB CC DD EE FF GG HH
11
11
11 11
22
22
22
22
22
22
22
22
HH 11
AA
AA 11
BB 11
CC
11
71
11
11
11 11 11
11 11 11
11 11 11
11 11 11
11 11 11
11 11
11
22
22
22
22
85.70%
85.70%
85.70%
85.70%
0%
0%
0%
0%
11
11
22
22
22
22
Matrix 8
Image by MIT OpenCourseWare.
4/8/2010 Copyright Qi D. Van Eikema Hommes 59 Network Centrality
(Sosa, Eppinger, Rowles 2007, Borgatti, Everett, and Freeman, 2002, UCINET)
•  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. 4/8/2010 Copyright Qi D. Van Eikema Hommes 60 DSM Method •  Capture system interacKons •  Analyze and improve system architecture and system interfaces. 6/24/10 Qi Van Eikema Hommes 61 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 6/24/10 Qi Van Eikema Hommes 62 ExisKng Methods Concerning System InteracKons Provide analytical
system analysis
Allow iterations and
feedback loops
Relate the
requirements to the
system design
Can be applied in the
early design phases
Provide complete
understanding of all
requirements
6/24/10 Design
Structu
re
Matrix
(DSM)
Yes
Axiomatic
Design’s
Design
Matrix
(DM)
Requireme What
nts
We
Manageme Want
nt
Yes
Yes
Yes
Yes
Yes
Qi Van Eikema Hommes Yes
Yes
Yes
Yes
Yes
63 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
6/24/10 Qi Van Eikema Hommes 64 ConverKng a DM into a DSM 1.
2.
3.
6/24/10 Construct an Axiomatic
Design’s Design Matrix.
Select Output Variables.
DP3 = f (FR1, DP1)
DP1 = f (FR2, DP2)
DP2 = f (FR3, DP3)
Permute the matrix by row
so that the output variables
are on the diagonal. We
get a precedence matrix
(DSM) of the Design
Parameters.
Qi Van Eikema Hommes DP1
DP2
DP3
FR1
X
0
X
FR2
X
X
0
FR3
0
X
X
DP1
DP2
DP3
FR1
X
0
X
FR2
X
X
0
FR3
0
X
X
DP1
DP2
DP3
DP1
X
X
0
DP2
0
X
X
DP3
X
0
X
65 SelecKng Output Variables DP2
DP3
DP4
DP2
DP3
DP4
6/24/10 Qi Van Eikema Hommes 66 CVC Cluster Machines Central
Wafer
Handler
Wafer
Processing
Module
6/24/10 Qi Van Eikema Hommes Courtesy of KDF electronics. Used with permission.
67 CVC Electro‐staKc Chuck (ESC) Process Chamber
Wafer
Backside gas channel
Electro-statically charged
plate
Cooling Plate
ESC
Plate for interface with
various process
modules
Backside Gas,
Cooling Water,
6/24/10 Electricity
Standard interface on all
process modules
Qi Van Eikema Hommes 68 System View of ESC Wafer Processing
Cluster Machine
Process
Module
2 ESC
Process
Module 3
Wafer
Transport
Robot
Process
Module
ESC
Control
and
Logistics
6/24/10 Qi Van Eikema Hommes ESC
Process
Module
4 ESC
69 Design Structure Matrix Built from Design Matrix Heat Transfer
Heat Transfer
Package the ESC into the
Packaging
the
Existing
Processinto
Modules
Modules
ControlCircuit
Circuit
Control
Design
6/24/10 Qi Van Eikema Hommes 70 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.
6/24/10 Qi Van Eikema Hommes 71 The SelecKon of Output Variables DP1 DP2
DP1 DP2
FR1 0.75 0.2
FR2 0.2 0.9
FR1 0.75 0.2
FR2 0.2 0.9
Δ DP1 = 1/0.75 * ! FR1 - 0.2/0.75* Δ DP2
Δ DP2 = 1/0.9 * ! FR2 - 0.2/0.9 * Δ DP1
Δ DP1
Δ DP1
0
Δ DP2
0.2/0.75
Δ DP2
0.2/0.9
0
Eigen Value = 0.243
This process converges.
6/24/10 Δ DP1 = 1/0.2 * ! FR1 Ð 0.75/0.2*
Δ DP2
Δ DP2 = 1/0.2 * ! FR2 - 0.9/0.2* Δ DP1
Δ DP1
Δ DP2
Δ DP1
0
0.75/0.2
Δ DP2
0.9/0.2
0
Eigen Value = 4.1
This process does NOT converge.
Qi Van Eikema Hommes 72
The Interchangeability of DM and DSM DM
DSM
6/24/10 The diagonal elements in the DM are the
dominant elements in their corresponding
rows.
Qi Van Eikema Hommes 73 Johnson and Johnson Ortho‐Clinical DiagnosKcs OASIS Analyzer Image of Vitros 5.1 cluster removed due to copyright restrictions.
6/24/10 Qi Van Eikema Hommes 74 OASIS Major Subsystems Frame and
Cabinetry
(STRU)
Primary
Sample
Metering
(SRME)
Slide
Incubator
(SLIN)
Aliquot
Buffer
(ALBU)
Sample
Integrity
(SAIN)
Secondary
Sample
Metering
(SRME)
Sample
Handling
(SAHA)
Reagent
Supply
(RGSU)
Power
Distributio
n (POWR)
Machine Control
(MACO)
Application
Services (APPS)
6/24/10 Cuvette
Incubator
(CUIN)
Slide
Supply
(SLSU)
Photometer Microtip
(PHMT)
Loading
(MTLD)
Qi Van Eikema Hommes Vitros Tips
Loader
(VTLD)
75 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. 6/24/10 Qi Van Eikema Hommes 76 DSM Constructed from Requirements 6/24/10 Qi Van Eikema Hommes 77 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.
6/24/10 Qi Van Eikema Hommes 78 How Many Marks Match The experts did not
capture 75 interfaces
predicted by the
requirements.
The requirements
prediction DSM
missed 118
interactions captured
by the experts.
There are 54 marks captured by
both the experts DSM and the
DSM from requirements.
6/24/10 Qi Van Eikema Hommes 79 Analyzing the Unmatched Marks 6/24/10 Qi Van Eikema Hommes 80 The Achievable PotenKal JNJ experts miss
6 marks.
215
DSM from
requirements misses
26 marks.
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 6/24/10 Qi Van Eikema Hommes 81 LimitaKon of the Method Requirements
DM
DSM
Can all requirements be decomposed to
predict system interactions?
6/24/10 Qi Van Eikema Hommes 82 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)
Difficult to
decompose
No strong evidence
in this case study
6/24/10 None
Reliability (budgeting)
Size (budgeting)
Weight (budgeting)
Cost (budgeting)
Installation
Standards
Safety
DFMAS
Component Reuse
Operability
Shipping
Disposal
Distribution
Training
Budget and Timing
Patents
Qi Van Eikema Hommes 83 Comparison of the Three Methods AxiomaKc Design Matrix (DM) J
J
H D F
H
F
X
X
Uncoupled
Design
X
X
Decoupled
Design
Avoids coupling by
smart engineering
design.
6/24/10 E G C
I
H
D
x
F
x
E
E
x
G
x
G
x
x
x
C
x
x
x
x
I
C
I
B K A
J
D
X
DM - DSM
DSM
x
B
x
K
x
A
x
B
x
K
x
A
Image by MIT OpenCourseWare.
Accepts coupling and
manage it by
streamlining the
process, or
modularizing the
system architecture.
Qi Van Eikema Hommes 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.
84 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. 6/24/10 Qi Van Eikema Hommes 85 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 6/24/10 Qi Van Eikema Hommes 86 MIT OpenCourseWare
http://ocw.mit.edu
ESD.33 Systems Engineering
Summer 2010
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