Abstract

Big-data mining brings new challenges and opportunities for engineering design, such as customer-needs mining, sentiment analysis, knowledge discovery, etc. At the early phase of conceptual design, designers urgently need to synthesize their own internal knowledge and wide external knowledge to solve design problems. However, on the one hand, it is time-consuming and laborious for designers to manually browse massive volumes of web documents and scientific literature to acquire external knowledge. On the other hand, how to extract concepts and discover meaningful concept associations automatically and accurately from these textual data to inspire designers’ idea generation? To address the above problems, we propose a novel data-driven concept network based on machine learning to capture design concepts and meaningful concept combinations as useful knowledge by mining the web documents and literature, which is further exploited to inspire designers to generate creative ideas. Moreover, the proposed approach contains three key steps: concept vector representation based on machine learning, semantic distance quantification based on concept clustering, and possible concept combinations based on natural language processing technologies, which is expected to provide designers with inspirational stimuli to solve design problems. A demonstration of conceptual design for detecting the fault location in transmission lines has been taken to validate the practicability and effectiveness of this approach.

References

References
1.
Goldschmidt
,
G.
, and
Sever
,
A. L.
,
2011
, “
Inspiring Design Ideas With Texts
,”
Des. Stud.
,
32
(
2
), pp.
139
155
. 10.1016/j.destud.2010.09.006
2.
Relich
,
M.
, and
Pawlewski
,
P.
,
2018
, “
A Case-Based Reasoning Approach to Cost Estimation of New Product Development
,”
Neurocomputing
,
272
, pp.
40
45
. 10.1016/j.neucom.2017.05.092
3.
Howard
,
T. J.
,
Culley
,
S. J.
, and
Dekoninck
,
E.
,
2008
, “
Describing the Creative Design Process by the Integration of Engineering Design and Cognitive Psychology Literature
,”
Des. Stud.
,
29
(
2
), pp.
160
180
. 10.1016/j.destud.2008.01.001
4.
Li
,
Y.
,
Wang
,
J.
,
Li
,
X.
, and
Zhao
,
W.
,
2007
, “
Design Creativity in Product Innovation
,”
Int. J. Adv. Manuf. Technol.
,
33
(
3–4
), pp.
213
222
. 10.1007/s00170-006-0457-y
5.
Chen
,
L.
,
Shi
,
F.
,
Han
,
J.
, and
Childs
,
P. R. N.
,
2017
, “
A Network-Based Computational Model for Creative Knowledge Discovery Bridging Human-Computer Interaction and Data Mining
,”
Proceedings of the IDETC/CIE
,
Cleveland, OH
,
Aug. 6–9, 2017
,
ASME Paper No. DETC2017-67228
.
6.
Kenett
,
Y. N.
, and
Faust
,
M.
,
2019
, “
A Semantic Network Cartography of the Creative Mind
,”
Trends Cognit. Sci.
,
23
(
4
), pp.
271
274
. 10.1016/j.tics.2019.01.007
7.
Lu
,
R.
,
Jin
,
X.
,
Zhang
,
S.
,
Qiu
,
M.
, and
Wu
,
X.
,
2018
, “
A Study on Big Knowledge and Its Engineering Issues
,”
IEEE Trans. Knowl. Data Eng.
,
31
(
9
), pp.
1630
1644
. 10.1109/TKDE.2018.2866863
8.
Hao
,
J.
,
Yan
,
Y.
,
Gong
,
L.
,
Wang
,
G.
, and
Lin
,
J.
,
2014
, “
Knowledge Map-Based Method for Domain Knowledge Browsing
,”
Decis. Support Syst.
,
61
, pp.
106
114
. 10.1016/j.dss.2014.02.001
9.
Shi
,
F.
,
Chen
,
L.
,
Han
,
J.
, and
Childs
,
P.
,
2017
, “
A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval
,”
ASME J. Mech. Des.
,
139
(
11
), p.
111402
. 10.1115/1.4037649
10.
Song
,
B.
,
Luo
,
J.
, and
Wood
,
K.
,
2018
, “
Data-Driven Platform Design: Patent Data and Function Network Analysis
,”
ASME J. Mech. Des.
,
141
(
2
), p.
021101
. 10.1115/1.4042083
11.
Chan
,
J.
,
Dow
,
S. P.
, and
Schunn
,
C. D.
,
2015
, “
Do the Best Design Ideas (Really) Come From Conceptually Distant Sources of Inspiration?
,”
Des. Stud.
,
36
(
Supplement C
), pp.
31
58
. 10.1016/j.destud.2014.08.001
12.
Chan
,
J.
,
Fu
,
K.
,
Schunn
,
C.
,
Cagan
,
J.
,
Wood
,
K.
, and
Kotovsky
,
K.
,
2011
, “
On the Benefits and Pitfalls of Analogies for Innovative Design: Ideation Performance Based on Analogical Distance, Commonness, and Modality of Examples
,”
ASME J. Mech. Des.
,
133
(
8
), p.
081004
. 10.1115/1.4004396
13.
Fu
,
K.
,
Chan
,
J.
,
Cagan
,
J.
,
Kotovsky
,
K.
,
Schunn
,
C.
, and
Wood
,
K.
,
2013
, “
The Meaning of “Near” and “Far”: The Impact of Structuring Design Databases and the Effect of Distance of Analogy on Design Output
,”
ASME J. Mech. Des.
,
135
(
2
), p.
021007
. 10.1115/1.4023158
14.
Kwon
,
H.
,
Park
,
Y.
, and
Geum
,
Y.
,
2018
, “
Toward Data-Driven Idea Generation: Application of Wikipedia to Morphological Analysis
,”
Technol. Forecast. Soc. Change
,
132
, pp.
56
80
. 10.1016/j.techfore.2018.01.009
15.
Georgiev
,
G. V.
, and
Georgiev
,
D. D.
,
2018
, “
Enhancing User Creativity: Semantic Measures for Idea Generation
,”
Knowledge Based Syst.
,
151
, pp.
1
15
. 10.1016/j.knosys.2018.03.016
16.
Han
,
J.
,
Shi
,
F.
,
Chen
,
L.
, and
Childs
,
P. R. N.
,
2018
, “
The Combinator—A Computer-Based Tool for Creative Idea Generation Based on a Simulation Approach
,”
Des. Sci.
,
4
, p.
E11
. 10.1017/dsj.2018.7
17.
He
,
B.
,
Song
,
W.
, and
Wang
,
Y. G.
,
2015
, “
Computational Conceptual Design Using Space Matrix
,”
ASME J. Comput. Inf. Sci. Eng.
,
15
(
1
), p.
011004
. 10.1115/1.4029062
18.
Cash
,
P.
, and
Štorga
,
M.
,
2015
, “
Multifaceted Assessment of Ideation: Using Networks to Link Ideation and Design Activity
,”
J. Eng. Des.
,
26
(
10–12
), pp.
391
415
. 10.1080/09544828.2015.1070813
19.
Han
,
J.
,
Shi
,
F.
,
Chen
,
L.
, and
Childs
,
P. R. N.
,
2018
, “
A Computational Tool for Creative Idea Generation Based on Analogical Reasoning and Ontology
,”
AI EDAM Artif. Intell. Eng. Des. Anal. Manuf.
,
32
(
4
), pp.
462
477
. 10.1017/S0890060418000082
20.
Jonson
,
B.
,
2005
, “
Design Ideation: The Conceptual Sketch in the Digital Age
,”
Des. Stud.
,
26
(
6
), pp.
613
624
. 10.1016/j.destud.2005.03.001
21.
Pahl
,
G.
, and
Beitz
,
W.
,
2007
,
Engineering Designing: A Systematic Approach
, 3rd ed.,
Springer
,
London
.
22.
Cheong
,
H.
,
Li
,
W.
,
Cheung
,
A.
,
Nogueira
,
A.
, and
Iorio
,
F.
,
2017
, “
Automated Extraction of Function Knowledge From Text
,”
ASME J. Mech. Des.
,
139
(
11
), p.
111407
. 10.1115/1.4037817
23.
Patterson
,
K.
,
Nestor
,
P. J.
, and
Rogers
,
T. T.
,
2007
, “
Where Do You Know What You Know? The Representation of Semantic Knowledge in the Human Brain
,”
Nat. Rev. Neurosci.
,
8
(
12
), pp.
976
987
. 10.1038/nrn2277
24.
Kenett
,
Y.
,
2018
, “Going the Extra Creative Mile: The Role of Semantic Distance in Creativity – Theory, Research, and Measurement,”
The Cambridge Handbook of the Neuroscience of Creativity
,
R. E.
Jung
, and
O.
Vartanian
, eds.,
Cambridge University Press
,
Cambridge
, pp.
233
248
.
25.
Bréchemier
,
M.-L.
,
Garcin
,
B.
,
Bendetowicz
,
D.
,
Levy
,
R.
,
Urbanski
,
M.
,
Foulon
,
C.
,
Thiebaut de Schotten
,
M.
,
Volle
,
E.
, and
Rosso
,
C.
,
2018
, “
Two Critical Brain Networks for Generation and Combination of Remote Associations
,”
Brain
,
141
(
1
), pp.
217
233
. 10.1093/brain/awx294
26.
Mednick
,
S.
,
1962
, “
The Associative Basis of the Creative Process
,”
Psychol. Rev.
,
69
(
3
), pp.
220
232
. 10.1037/h0048850
27.
Wen
,
K. M.
,
Zeng
,
Y.
,
Li
,
R. X.
, and
Lin
,
J. Q.
,
2012
, “
Modeling Semantic Information in Engineering Applications: A Review
,”
Artif. Intell. Rev.
,
37
(
2
), pp.
97
117
. 10.1007/s10462-011-9221-2
28.
Song
,
B.
,
Yan
,
B.
,
Triulzi
,
G.
,
Alstott
,
J.
, and
Luo
,
J.
,
2019
, “
Overlay Technology Space Map for Analyzing Design Knowledge Base of a Technology Domain: The Case of Hybrid Electric Vehicles
,”
Res. Eng. Des.
,
30
(
3
), pp.
405
423
. 10.1007/s00163-019-00312-w
29.
Luo
,
J.
,
Yan
,
B.
, and
Wood
,
K.
,
2017
, “
InnoGPS for Data-Driven Exploration of Design Opportunities and Directions: The Case of Google Driverless Car Project
,”
ASME J. Mech. Des.
,
139
(
11
), p.
111416
. 10.1115/1.4037680
30.
Song
,
B.
, and
Luo
,
J.
,
2017
, “
Mining Patent Precedents for Data-Driven Design: The Case of Spherical Rolling Robots
,”
ASME J. Mech. Des.
,
139
(
11
), p.
111420
. 10.1115/1.4037613
31.
Taura
,
T.
,
Yamamoto
,
E.
,
Fasiha
,
M. Y. N.
,
Goka
,
M.
,
Mukai
,
F.
,
Nagai
,
Y.
, and
Nakashima
,
H.
,
2012
, “
Constructive Simulation of Creative Concept Generation Process in Design: A Research Method for Difficult-to-Observe Design-Thinking Processes
,”
J. Eng. Des.
,
23
(
4
), pp.
297
321
. 10.1080/09544828.2011.637191
32.
Wang
,
P.
,
Wijnants
,
M. L.
, and
Ritter
,
S. M.
,
2018
, “
What Enables Novel Thoughts? The Temporal Structure of Associations and Its Relationship to Divergent Thinking
,”
Front. Psychol.
,
9
, p.
1771
. 10.3389/fpsyg.2018.01771
33.
Kenett
,
Y. N.
,
2019
, “
What Can Quantitative Measures of Semantic Distance Tell Us About Creativity?
,”
Curr Opin Behav Sci
,
27
, pp.
11
16
. 10.1016/j.cobeha.2018.08.010
34.
Srinivasan
,
V.
,
Song
,
B.
,
Luo
,
J.
,
Subburaj
,
K.
,
Elara
,
M. R.
,
Blessing
,
L.
, and
Wood
,
K.
,
2018
, “
Does Analogical Distance Affect Performance of Ideation?
,”
ASME J. Mech. Des.
,
140
(
7
), p.
071101
. 10.1115/1.4040165
35.
Jin
,
J.
,
Liu
,
Y.
,
Ji
,
P.
, and
Kwong
,
C. K.
,
2019
, “
Review on Recent Advances in Information Mining From Big Consumer Opinion Data for Product Design
,”
ASME J. Comput. Inf. Sci. Eng.
,
19
(
1
), p.
010801
. 10.1115/1.4041087
36.
Tang
,
M.
,
Jin
,
J.
,
Liu
,
Y.
,
Li
,
C. P.
, and
Zhang
,
W. W.
,
2019
, “
Integrating Topic, Sentiment, and Syntax for Modeling Online Reviews: A Topic Model Approach
,”
ASME J. Comput. Inf. Sci. Eng.
,
19
(
1
), p.
011001
. 10.1115/1.4041475
37.
Wang
,
J.
,
Das
,
S.
,
Rai
,
R.
, and
Zhou
,
C.
,
2018
, “
Data-Driven Simulation for Fast Prediction of Pull-Up Process in Bottom-Up Stereo-Lithography
,”
Comput.-Aided Des.
,
99
, pp.
29
42
. 10.1016/j.cad.2018.02.002
38.
Tao
,
F.
,
Qi
,
Q.
,
Liu
,
A.
, and
Kusiak
,
A.
,
2018
, “
Data-Driven Smart Manufacturing
,”
J. Manuf. Syst.
,
48
, pp.
157
169
. 10.1016/j.jmsy.2018.01.006
39.
Wang
,
M.
, and
Chen
,
W.
,
2015
, “
A Data-Driven Network Analysis Approach to Predicting Customer Choice Sets for Choice Modeling in Engineering Design
,”
ASME J. Mech. Des.
,
137
(
7
), p.
071410
. 10.1115/1.4030160
40.
Goucher-Lambert
,
K.
,
Moss
,
J.
, and
Cagan
,
J.
,
2018
, “
Inspired Internal Search: Using Neuroimaging to Understand Design Ideation and Concept Generation With Inspirational Stimuli
,”
Proceedings of the IDETC/CIE
,
Quebec City, Quebec, Canada
,
Aug. 26–29, 2018
,
ASME Paper No. DETC2018-85690
.
41.
Zhou
,
G.
, and
Huang
,
J. X.
,
2017
, “
Modeling and Learning Distributed Word Representation With Metadata for Question Retrieval
,”
IEEE Trans. Knowl. Data Eng.
,
29
(
6
), pp.
1226
1239
. 10.1109/TKDE.2017.2665625
42.
Zhang
,
C.
,
Kwon
,
Y. P.
,
Kramer
,
J.
,
Kim
,
E.
, and
Agogino
,
A. M.
,
2017
, “
Concept Clustering in Design Teams: A Comparison of Human and Machine Clustering
,”
ASME J. Mech. Des.
,
139
(
11
), p.
111414
. 10.1115/1.4037478
43.
Mikolov
,
T.
,
Chen
,
K.
,
Corrado
,
G.
, and
Dean
,
J.
,
2013
, “
Efficient Estimation of Word Representations in Vector Space
,”
arXiv preprint, arXiv:1301.3781
. arXiv:1301.3781
44.
Hu
,
K.
,
Wu
,
H.
,
Qi
,
K.
,
Yu
,
J.
,
Yang
,
S.
,
Yu
,
T.
,
Zheng
,
J.
, and
Liu
,
B.
,
2018
, “
A Domain Keyword Analysis Approach Extending Term Frequency-Keyword Active Index With Google Word2Vec Model
,”
Scientometrics
,
114
(
3
), pp.
1031
1068
. 10.1007/s11192-017-2574-9
45.
Rodriguez
,
A.
, and
Laio
,
A.
,
2014
, “
Clustering by Fast Search and Find of Density Peaks
,”
Science
,
344
(
6191
), pp.
1492
1496
. 10.1126/science.1242072
46.
Manning
,
C.
, and
Schütze
,
H.
,
1999
,
Foundations of Statistical Natural Language Processing
,
MIT Press
,
Cambridge, MA
.
47.
Garretson
,
I. C.
,
Mani
,
M.
,
Leong
,
S.
,
Lyons
,
K. W.
, and
Haapala
,
K. R.
,
2016
, “
Terminology to Support Manufacturing Process Characterization and Assessment for Sustainable Production
,”
J. Cleaner Prod.
,
139
, pp.
986
1000
. 10.1016/j.jclepro.2016.08.103
48.
Lopez B
,
C. E.
,
Zheng
,
X.
, and
Miller
,
S. R.
,
2017
, “
Linking Creativity Measurements to Product Market Favorability: A Data-Mining Approach
,”
Proceedings of the IDETC/CIE
,
Cleveland, OH
,
Aug. 6–9, 2017
,
ASME Paper No. DETC2017-67622
.
49.
Kaufman
,
J. C.
,
Baer
,
J.
,
Cole
,
J. C.
, and
Sexton
,
J. D.
,
2008
, “
A Comparison of Expert and Nonexpert Raters Using the Consensual Assessment Technique
,”
Creat. Res. J.
,
20
(
2
), pp.
171
178
. 10.1080/10400410802059929
50.
Goncalo
,
J. A.
,
Flynn
,
F. J.
, and
Kim
,
S. H.
,
2010
, “
Are Two Narcissists Better Than One? The Link Between Narcissism, Perceived Creativity, and Creative Performance
,”
Pers. Soc. Psychol. Bull.
,
36
(
11
), pp.
1484
1495
. 10.1177/0146167210385109
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