My AD Lab Presentation (Spring 2008)

Automating the Conceptual Design Process

Automated Design LaboratoryThe University of Texas at Austin

Mechanical Engineering Department

April 30, 2008Presented by Justin McKay

Automating the Systematic Design Process

Research Effort 1: Function Structure Grammar

Research Effort I1: Configuration Grammar

Research Effort III : Tree Search & Component Selection

Functional Specifications

Customer Needs

Black Box Model

Functional Decomposition

Function Structure

Configuration Design

Configuration Flow Graph

Component Selection

Analysis and

Evaluation

* Final * Design

prior work – automated concept generation

Function Structure

Functional Synthesis by Rule Execution

Design Grammar

Rules

Configuration Flow Graph’sFunction Structure

Functional Synthesis by Rule Execution

Configuration Flow Graph’s

• Graph Grammars – Function to Form mapping

Function Structure – describes the functions necessary to translate input flows into output flows

Graph Grammar - Rules for manipulating nodes and arcs within graph

Configuration Flow Graph – synthesizes individual or sets of components into a set of conceptual design configurations

Deriving Grammar Rules

The Sharper ImageHybrid “Shake” 7-LED

Magnetic-Induction Flashlight

The Bill of Materials

Housing Assembly

Cap Seals (O-rings)

Cover

Knob

Housing

LED Assembly

Cover

Lenses

HousingLEDs

Circuit Board

Screws

Power Source Assembly

CoilCap

Cushion 1

Wire 1

Cushion 2Magnet

Wire 2

Wire 3

Switch

Circuit Board

Capacitor(Reverse Side)

Function Structure (the Seed)

Arc=flow type

Node=Function

Function to Form Mapping via Grammar Rules

Configuration Flow Graph

Function Structure

Grammar Rules

Derived Grammar Rules

LHS:Function Structure

RHS:Configuration Flow

GraphI

II

III

If LHS, then RHS

TK’s designer preference modeler (dpm)

1. observe designer’s decision making2. construct a model of designer preferences3. find best designs

Evaluate Designs

Generate Designs

Computer-to-Human Generation Feedback(heuristic sampling)

Human-to-Computer Evaluation Feedback

(designer preferences)

DPM

Designer SynthesisSoftware

Evaluate Designs

Generate Designs

Computer-to-Human Generation Feedback(heuristic sampling)

Human-to-Computer Evaluation Feedback

(designer preferences)

DPM

Designer SynthesisSoftware

Automated Concept Generation

feasible design space

candidate 1

candidate 2feasible design space

candidate 1

candidate 2result of generation

• multiple candidates• grammar generates 594 “bread slicer” designs• need to score and rank generated candidates

• i.e. Pugh’s Chart

DPM (designer preference modeler)

evaluation scheme

Generate CFG’s

Sample from solution space

Get designer’srating for selected concepts

Project concept ratings on to rule choices using recipes

feasible design spacefeasible design spacefeasible design spacefeasible design space

Compute “preference score”for each rule choice

A B C D

Score A Score C Score D Score B

Propagate “preference scores”over each recipe to compute overall worth of each design

Recipe A:

Rule #Rule # Rule #

.

.

.

Recipe B:

Rule #Rule # Rule #

.

.

.

Recipe C:

Rule #Rule # Rule #

.

.

.

Recipe D:

Rule #Rule # Rule #

.

.

.

Rule #1

Rule #2

Rule #3 . . .

Rule Score2

Rule Score1

Rule Score3

Rule Preference Scores:

feasible design spacefeasible design space

Figure 4: Illustration of design evaluation.


Generate CFG’s






A B C D



Recipe A:

Rule #Rule # Rule #

.

.

.

Recipe B:

Rule #Rule # Rule #

.

.

.

Recipe C:

Rule #Rule # Rule #

.

.

.

Recipe D:

Rule #Rule # Rule #

.

.

.

Rule #1

Rule #2

Rule #3 . . .

Rule Score2

Rule Score1

Rule Score3




Sampling Goals:• select a set to be

presented to the designer• reduce the number of

designer evaluations• capture the variety in

the design solution space

Generate CFG’s






A B C D



Recipe A:

Rule #Rule # Rule #

.

.

.

Recipe B:

Rule #Rule # Rule #

.

.

.

Recipe C:

Rule #Rule # Rule #

.

.

.

Recipe D:

Rule #Rule # Rule #

.

.

.

Rule #1

Rule #2

Rule #3 . . .

Rule Score2

Rule Score1

Rule Score3




Seed of the design generation tree

31

313 10

13 10 22 31 22 29

29 35 29 35 31 35

C1 C2 C3

C4 C5 C6 C7 C8 C9

Candidate

C1 (1,3,13)

C2 (3,13,22)

C3 (3,13,31)

C4 (1,3,10,29)

C5 (1,3,10,35)

C6 (3,10,22,29)

C7 (3,10,22,35)

C8 (3,10,29,31)

C9 (3,10,31,35)

Recipe

Recipe Pool (1,3,10,13,22,29,31,35)


31

313 10

13 10 22 31 22 29

29 35 29 35 31 35

C1 C2 C3

C4 C5 C6 C7 C8 C9


31

313 10

13 10 22 31 22 29

29 35 29 35 31 35

C1 C2 C3

C4 C5 C6 C7 C8 C9

Candidate

C1 (1,3,13)

C2 (3,13,22)

C3 (3,13,31)

C4 (1,3,10,29)

C5 (1,3,10,35)

C6 (3,10,22,29)

C7 (3,10,22,35)

C8 (3,10,29,31)

C9 (3,10,31,35)

Recipe

Recipe Pool (1,3,10,13,22,29,31,35)

1) compute the recipe pool


2) calculate commonality measure of each candidate

3) select candidate with most commonly occurring rules and add it to the feedback set,

4) update pool, repeat steps 1-3

Frequency of Appearance of Each Rule in the Recipe Pool

3

9

6

3 3 3 3 330 0

3 3 30 0

30 0

30 0 0 00 0 0 0 0 0 0 0

1 3 10 13 22 29 31 35

Rule Number

Fre

qu

ency

First Iteration

Second Iteration

Third Iteration

Sampling Finished

Commonality Measure of Each Candidate

15 15 15

21 21 21 21 21 21

6 6

3

6 6

9

3 3 0

6

3 3 3 3 0 0 0 00 0 0 0 0 0 0 0 0

C1 C2 C3 C4 C5 C6 C7 C8 C9

Candidate

Co

mm

on

alit

y M

easu

re

First Iteration

Second Iteration

Third Iteration

Sampling Finished

Generate CFG’s


Get designer ’srating for selected concepts


Compute “ preference score ”for each rule choice

Propagate “ preference scores ”over each recipe to compute overall worth of each design


4) pair-wise comparison matrix for DFS


Generate CFG’s






A B C D



Recipe A:

Rule #Rule # Rule #

.

.

.

Recipe B:

Rule #Rule # Rule #

.

.

.

Recipe C:

Rule #Rule # Rule #

.

.

.

Recipe D:

Rule #Rule # Rule #

.

.

.

Rule #1

Rule #2

Rule #3 . . .

Rule Score2

Rule Score1

Rule Score3




Generate CFG’s






A B C D



Recipe A:

Rule #Rule # Rule #

.

.

.

Recipe B:

Rule #Rule # Rule #

.

.

.

Recipe C:

Rule #Rule # Rule #

.

.

.

Recipe D:

Rule #Rule # Rule #

.

.

.

Rule #1

Rule #2

Rule #3 . . .

Rule Score2

Rule Score1

Rule Score3




6) extract how much the designer prefers a particular rule (i.e. function-to-form mapping) over others


Generate CFG’s






A B C D



Recipe A:

Rule #Rule # Rule #

.

.

.

Recipe B:

Rule #Rule # Rule #

.

.

.

Recipe C:

Rule #Rule # Rule #

.

.

.

Recipe D:

Rule #Rule # Rule #

.

.

.

Rule #1

Rule #2

Rule #3 . . .

Rule Score2

Rule Score1

Rule Score3





7) calculate best/worst designs in the population

Design ProblemExample Problem Bread SlicerEvaluation Criteria Quality of Concepts

Design Generation and Sampling# of candidates generated 594# of rules in the recipe pool 30

# of candidates in DFS 12Design Evaluation

Recipe Pool1,7,8,10,11,12,13,15,17,20,21,22,23,24,25,26,

28,29,30,31,33,34,35,36,38,39,40,41,42,43

RPS-3,3,14,3,0,6,-3,7,-21,-7,15,3,24,-9,-14,7,

-3,3,-9,0,2,-9,0,3,0,0,9,9,9,-9 Recipe ScoreBest Candidate C155 10,11,15,22,23,29,38,39,40,41,42 67

Worst Candidate C498 1,8,11,13,17,38,39,43 -50

• 12 designs are selected as

feedback set

• finds best “bread slicer” in a population of 594

C155

DPM – results for bread slicer design

DPM – Summary and ImplicationsTK’s DPM Method1. Find minimum set of solutions (select candidates from candPool with high commonality measure)2. Of these, target candidates that best represents variety in the solution space

Limitations• Had to keep total # of rules small• Pair-wise comparison requires too many evaluations (66 evaluations for a DFS of 12 candidates)

Future Work• multi-criteria evaluation• learning from past “preference models” • use as “guiding” strategy when full space cannot be enumerated

Design ProblemExample Problem Bread SlicerEvaluation Criteria Quality of Concepts

Design Generation and Sampling# of candidates generated 594# of rules in the recipe pool 30

# of candidates in DFS 12Design Evaluation

Recipe Pool1,7,8,10,11,12,13,15,17,20,21,22,23,24,25,26,

28,29,30,31,33,34,35,36,38,39,40,41,42,43

RPS-3,3,14,3,0,6,-3,7,-21,-7,15,3,24,-9,-14,7,

-3,3,-9,0,2,-9,0,3,0,0,9,9,9,-9 Recipe ScoreBest Candidate C155 10,11,15,22,23,29,38,39,40,41,42 67

Worst Candidate C498 1,8,11,13,17,38,39,43 -50

Rule Knowledge MethodThe Method1. Randomly generate 5 – 10 candidate solutions

a) Sum the number of appearances of each rule in candSet and store in Rule Knowledge Matrix2. User evaluates candidates on a 1-5 scale based on likability (similar to DPM)

a) Project candidate scores onto rules and compute an average score for each rule

3. LEARN & GUIDE - Compute probabilities for each rule in Rule Knowledge Matrix and generate a new set of candidates based on these probabilities

4. Repeat 1 - 3

123456789

10111213...

n=154

Srfr

1 2 3 4 5 6 7 8 9 10 11 12 13 . . . n=154

23

8.623

LEARN

Rule Knowledge Method

Exploit – target rules that have consistently been rated highly in past

Explore – target rules with a low frequency value, f

B – tuner to control explore vs. exploit

Calculating probabilities

Probabilities

Rule 5

Rule 6

Rule 7

Pr

Rule 5Rule 6Rule 7

Questions

?

My AD Lab Presentation (Spring 2008)

Design

Transcript of My AD Lab Presentation (Spring 2008)