Abstract

Generative design tools empowered by recent advancements in artificial intelligence (AI) offer the opportunity for human designers and design tools to collaborate in new, more advanced modes throughout various stages of the product design process to facilitate the creation of higher performing and more complex products. This paper explores how the use of these generative design tools may impact the design process, designer behavior, and overall outcomes. Six in-depth interviews were conducted with practicing and student designers from different disciplines who use commercial generative design tools, detailing the design processes they followed. From a grounded theory-based analysis of the interviews, a provisional process diagram for generative design and its uses in the early-stage design process is proposed. The early stages of defining tool inputs bring about a constraint-driven process in which designers focus on the abstraction of the design problem. Designers will iterate through the inputs to improve both quantitative and qualitative metrics. The learning through iteration allows designers to gain a thorough understanding of the design problem and solution space. This can bring about creative applications of generative design tools in early-stage design to provide guidance for traditionally designed products.

References

1.
Chandrasegaran
,
S. K.
,
Ramani
,
K.
,
Sriram
,
R. D.
,
Horváth
,
I.
,
Bernard
,
A.
,
Harik
,
R. F.
, and
Gao
,
W.
,
2013
, “
The Evolution, Challenges, and Future of Knowledge Representation in Product Design Systems
,”
Comput.-Aided Des.
,
45
(
2
), pp.
204
228
.
2.
Han
,
Y.
, and
Moghaddam
,
M.
,
2021
, “
Analysis of Sentiment Expressions for User-Centered Design
,”
Expert Syst. Appl.
,
171
, p.
114604
.
3.
Fu
,
K.
,
Chan
,
J.
,
Cagan
,
J.
,
Kotovsky
,
K.
,
Schunn
,
C.
, and
Wood
,
K.
,
2012
, “
The Meaning of ‘Near’ and ‘Far’: The Impact of Structuring Design Databases and the Effect of Distance of Analogy on Design Output
,”
Proceedings of the ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
Chicago, IL
,
Aug. 12–15
, pp.
877
888
.
4.
He
,
Y.
,
Camburn
,
B.
,
Luo
,
J.
,
Yang
,
M. C.
, and
Wood
,
K. L.
,
2019
, “
Visual Sensemaking of Massive Crowdsourced Data for Design Ideation
,”
Proceedings of the Design Society: International Conference on Engineering Design
,
Delft, The Netherlands
,
Aug. 5–8
, Cambridge University Press, pp.
409
418
.
5.
Ong
,
B.
,
Danhaive
,
R.
, and
Mueller
,
C.
,
2021
, “
Machine Learning for Human Design: Sketch Interface for Structural Morphology Ideation Using Neural Networks
,”
Proceedings of the International Association for Shell and Spatial Structures (IASS) Symposium 2020/2021.
6.
Camburn
,
B.
,
He
,
Y.
,
Raviselvam
,
S.
,
Luo
,
J.
, and
Wood
,
K.
,
2020
, “
Machine Learning-Based Design Concept Evaluation
,”
ASME J. Mech. Des.
,
142
(
3
), p.
031113
.
7.
Yuan
,
C.
,
Marion
,
T.
, and
Moghaddam
,
M.
,
2022
, “
Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model
,”
ASME J. Mech. Des.
,
144
(
2
), p.
021403
.
8.
Dering
,
M. L.
,
Tucker
,
C. S.
, and
Kumara
,
S.
,
2017
, “
An Unsupervised Machine Learning Approach to Assessing Designer Performance During Physical Prototyping
,”
ASME J. Comput. Inf. Sci. Eng.
,
18
(
1
), p.
011002
.
9.
Primo
,
T.
,
Calabrese
,
M.
,
Del Prete
,
A.
, and
Anglani
,
A.
,
2017
, “
Additive Manufacturing Integration With Topology Optimization Methodology for Innovative Product Design
,”
Int. J. Adv. Manuf. Technol.
,
93
(
1
), pp.
467
479
.
10.
Cui
,
J.
, and
Tang
,
M.
,
2017
, “
Towards Generative Systems for Supporting Product Design
,”
Int. J. Des. Eng.
,
7
(
1
), p.
1
.
11.
Noor
,
A. K.
,
2017
, “
AI and the Future of the Machine Design
,”
Mech. Eng.
,
139
(
10
), pp.
38
43
.
12.
Buonamici
,
F.
,
Carfagni
,
M.
,
Furferi
,
R.
,
Volpe
,
Y.
, and
Governi
,
L.
,
2020
, “
Generative Design: An Explorative Study
,”
Comput.-Aided Des. Appl.
,
18
(
1
), pp.
144
155
.
13.
Caetano
,
I.
,
Santos
,
L.
, and
Leitão
,
A.
,
2020
, “
Computational Design in Architecture: Defining Parametric, Generative, and Algorithmic Design
,”
Front. Archit. Res.
,
9
(
2
), pp.
287
300
.
14.
Lopez
,
C. E.
,
Miller
,
S. R.
, and
Tucker
,
C. S.
,
2018
, “
Exploring Biases Between Human and Machine Generated Designs
,”
ASME J. Mech. Des.
,
141
(
2
), p.
021104
.
15.
Briard
,
T.
,
Segonds
,
F.
, and
Zamariola
,
N.
,
2020
, “
G-DfAM: A Methodological Proposal of Generative Design for Additive Manufacturing in the Automotive Industry
,”
Int. J. Interact. Des. Manuf. IJIDeM
,
14
(
3
), pp.
875
886
.
16.
Song
,
B.
,
Soria Zurita
,
N.
,
Zhang
,
G.
,
Stump
,
G.
,
Balon
,
C.
,
Miller
,
S.
,
Yukish
,
M.
,
Cagan
,
J.
, and
McComb
,
C.
,
2020
, “
Toward Hybrid Teams: A Platform to Understand Human-Computer Collaboration During the Design of Complex Engineered Systems
,” Proceedings of the Design Society: DESIGN Conference, Vol.
1
, pp.
1551
1560
.
17.
Zhang
,
G.
,
Raina
,
A.
,
Cagan
,
J.
, and
McComb
,
C.
,
2021
, “
A Cautionary Tale About the Impact of AI on Human Design Teams
,”
Des. Stud.
,
72
, p.
100990
.
18.
Pillai
,
P. P.
,
Burnell
,
E.
,
Wang
,
X.
, and
Yang
,
M. C.
,
2020
, “
Effects of Robust Convex Optimization on Early-Stage Design Space Exploratory Behavior
,”
ASME J. Mech. Des.
,
142
(
12
), p.
121704
.
19.
Krish
,
S.
,
2011
, “
A Practical Generative Design Method
,”
Comput.-Aided Des.
,
43
(
1
), pp.
88
100
.
20.
Dellermann
,
D.
,
Ebel
,
P.
,
Söllner
,
M.
, and
Leimeister
,
J. M.
,
2019
, “
Hybrid Intelligence
,”
Bus. Inf. Syst. Eng.
,
61
(
5
), pp.
637
643
.
21.
Kazi
,
Rubaiat Habib
,
Grossman
,
Tovi
,
Cheong
,
Hyunmin
,
Hashemi
,
Ali
, and
Fitzmaurice
,
George
,
2017
, “
DreamSketch Early Stage 3D Design Explorations with Sketching and Generative Design
,”
Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology
,
Quebec City, Canada
,
Oct. 20
.
22.
Altavilla
,
S.
, and
Blanco
,
E.
,
2020
, “
Are AI Tools Going to be the New Designers? A Taxonomy for Measuring the Level of Automation of Design Activities
,”
DS 102: Proceedings of the DESIGN 2020 16th International Design Conference
,
Cavtat, Croatia
,
Oct. 26–29
, pp.
81
90
.
23.
Nagy
,
D.
,
Lau
,
D.
,
Locke
,
J.
,
Stoddart
,
J.
,
Villaggi
,
L.
,
Wang
,
R.
,
Zhao
,
D.
, and
Benjamin
,
D.
,
2017
, “
Project discover: an application of generative design for architectural space planning
,”
SIMAUD '17: Proceedings of the Symposium on Simulation for Architecture and Urban Design
,
Toronto, Canada
,
May 22–24
.
24.
Vlah
,
D.
,
Žavbi
,
R.
, and
Vukašinović
,
N.
,
2020
, “
Evaluation of Topology Optimization and Generative Design Tools as Support for Conceptual Design
,”
Proceedings of the Design Society: DESIGN Conference
,
Croatia
.
25.
Alcaide-Marzal
,
J.
,
Diego-Mas
,
J. A.
, and
Acosta-Zazueta
,
G.
,
2020
, “
A 3D Shape Generative Method for Aesthetic Product Design
,”
Des. Stud.
,
66
, pp.
144
176
.
26.
Nordin
,
A.
,
2018
, “
Challenges in the Industrial Implementation of Generative Design Systems: An Exploratory Study
,”
Artif. Intell. Eng. Des. Anal. Manuf.
,
32
(
1
), pp.
16
31
.
27.
Holzer
,
D.
,
Dominik
,
Hough
,
Richard
,
Burry
,
M.
, and
Mark
,
2007
, “
Parametric Design and Structural Optimisation for Early Design Exploration
,”
Int. J. Archit. Comput.
,
5
(
4
), pp.
625
643
.
28.
Phadnis
,
V.
,
Arshad
,
H.
,
Wallace
,
D.
, and
Olechowski
,
A.
,
2021
, “
Are Two Heads Better Than One for Computer-Aided Design?
ASME J. Mech. Des.
,
143
(
7
), p.
071401
.
29.
Zhou
,
J. (J.)
,
Phadnis
,
V.
, and
Olechowski
,
A.
,
2020
, “
Analysis of Designer Emotions in Collaborative and Traditional Computer-Aided Design
,”
ASME J. Mech. Des.
,
143
(
2
), p.
021401
.
30.
Bansal
,
G.
,
Nushi
,
B.
,
Kamar
,
E.
,
Weld
,
D. S.
,
Lasecki
,
W. S.
, and
Horvitz
,
E.
,
2019
, “
Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff
,”
Proc. AAAI Conf. Artif. Intell.
,
33
(
1
), pp.
2429
2437
.
31.
Gyory
,
J. T.
,
Song
,
B.
,
Cagan
,
J.
, and
McComb
,
C.
,
2021
, “
Communication in AI-Assisted Teams During an Interdisciplinary Drone Design Problem
,”
Proceedings of the Design Society: 23rd International Conference on Engineering Design
,
Gothenburg, Sweden
.
32.
Chaudhari
,
A. M.
, and
Selva
,
D.
,
2022
, “
Evaluating Designer Learning and Performance in Interactive Deep Generative Design
,”
Am. Soc. Mech. Eng. Digital Collection.
33.
Li
,
Z.
, and
Seering
,
W.
,
2019
, “
Build Your Firm With Strangers?: Longitudinal Studies on Open Source Hardware Firm Growth
,”
Proceedings of the ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
Anaheim, CA
,
Aug. 18–21
.
34.
Friedman
,
K.
,
2003
, “
Theory Construction in Design Research: Criteria: Approaches, and Methods
,”
Des. Stud.
,
24
(
6
), pp.
507
522
.
35.
Creswell
,
J.
,
Clark
,
V.
,
Gutmann
,
M.
, and
Hanson
,
W.
,
2003
,
Handbook of Mixed Methods in Social and Behavioral Research
,
SAGE Publications, Inc
,
Newbury Park, CA
, pp.
209
240
.
36.
Charmaz
,
K.
,
2008
, “Grounded Theory as an Emergent Method,”
Handbook of Emergent Methods
,
The Guilford Press
,
New York
, pp.
155
170
.
37.
Crang
,
M.
, and
Cook
,
I.
,
2007
,
Doing Ethnographies
,
SAGE Publications Ltd
,
London
.
38.
Thomas
,
D. R.
,
2006
, “
A General Inductive Approach for Analyzing Qualitative Evaluation Data
,”
Am. J. Eval.
,
27
(
2
), pp.
237
246
.
39.
Saldana
,
J.
,
2015
,
The Coding Manual for Qualitative Researchers
,
SAGE Publications Ltd
,
Newbury Park, CA
.
40.
Lauff
,
C. A.
,
Kotys-Schwartz
,
D.
, and
Rentschler
,
M.
,
2018
, “
What Is a Prototype? What Are the Roles of Prototypes in Companies?
ASME J. Mech. Des.
,
140
(
6
), p.
061102
.
41.
Dorst
,
K.
, and
Cross
,
N.
,
2001
, “
Creativity in the Design Process: Co-Evolution of Problem–Solution
,”
Des. Stud.
,
22
(
5
), pp.
425
437
.
42.
Ulrich
,
K. T.
,
Eppinger
,
S. D.
, and
Yang
,
M. C.
,
2019
,
Product Design and Development
,
McGraw Hill Education
,
New York
.
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