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Abstract

Facing the challenges posed by increasingly complex, dynamic, and unforeseen requirements, the design process is grappling with the critical issue of ensuring sustained product satisfaction amid changing demands. This paper introduces an approach for evaluating design adaptability, considering potential future requirements. Entropy serves as a crucial indicator to quantify design effort and the Markov process is employed to simulate potential requirement changes. The information contents of design requirements and design solutions are defined based on information entropy theory, and the design adaptability of a design candidate is evaluated by calculating the extra design effort for satisfying the design requirements, which is the difference in information content between the design candidate and design requirements. Moreover, a simulation method for requirement evolution is proposed, which integrates information entropy theory and the Markov process to accommodate potential future requirements. The general design adaptability of design solutions is then calculated based on conditional entropy, taking into account the evolving design requirements. Finally, the effectiveness of the proposed approach is validated through a case study involving the design and evaluation of a hybrid additive manufacturing device.

References

1.
Roucoules
,
L.
, and
Anwer
,
N.
,
2021
, “
Coevolution of Digitalisation, Organisations and Product Development Cycle
,”
CIRP Ann.
,
70
(
2
), pp.
519
542
.
2.
Vaneker
,
T.
,
Bernard
,
A.
,
Moroni
,
G.
,
Gibson
,
I.
, and
Zhang
,
Y.
,
2020
, “
Design for Additive Manufacturing: Framework and Methodology
,”
CIRP Ann.
,
69
(
2
), pp.
578
599
.
3.
Fu
,
K. K.
,
Yang
,
M. C.
, and
Wood
,
K. L.
,
2016
, “
Design Principles: Literature Review, Analysis, and Future Directions
,”
ASME J. Mech. Des.
,
138
(
10
), p.
101103
.
4.
Lee
,
M.
,
Hwang
,
D.
,
Lee
,
Y.
,
Choi
,
B.
, and
Park
,
W.
,
2021
, “
Using Technologically Related Products From Other Domains as Inspirations for Technology-Push Product Concept Generation
,”
ASME J. Mech. Des.
,
143
(
1
), p.
011402
.
5.
Regenwetter
,
L.
,
Nobari
,
A. H.
, and
Ahmed
,
F.
,
2022
, “
Deep Generative Models in Engineering Design: A Review
,”
ASME J. Mech. Des.
,
144
(
7
), p.
071704
.
6.
Ramani
,
K.
,
Ramanujan
,
D.
,
Bernstein
,
W. Z.
,
Zhao
,
F.
,
Sutherland
,
J.
,
Handwerker
,
C.
,
Choi
,
J.-K.
,
Kim
,
H.
, and
Thurston
,
D.
,
2010
, “
Integrated Sustainable Life Cycle Design: A Review
,”
ASME J. Mech. Des.
,
132
(
9
). p.
091004
7.
Wang
,
B.
,
Tao
,
F.
,
Fang
,
X.
,
Liu
,
C.
,
Liu
,
Y.
, and
Freiheit
,
T.
,
2021
, “
Smart Manufacturing and Intelligent Manufacturing: A Comparative Review
,”
Engineering
,
7
(
6
), pp.
738
757
.
8.
Zhang
,
G.
,
Raina
,
A.
,
Brownell
,
E.
, and
Cagan
,
J.
,
2023
, “
Artificial Intelligence Impersonating a Human: The Impact of Design Facilitator Identity on Human Designers
,”
ASME J. Mech. Des.
,
145
(
5
), p.
051404
.
9.
Chong
,
L.
,
Raina
,
A.
,
Goucher-Lambert
,
K.
,
Kotovsky
,
K.
, and
Cagan
,
J.
,
2023
, “
The Evolution and Impact of Human Confidence in Artificial Intelligence and in Themselves on AI-Assisted Decision-Making in Design
,”
ASME J. Mech. Des.
,
145
(
3
), p.
031401
.
10.
Raina
,
A.
,
Cagan
,
J.
, and
McComb
,
C.
,
2023
, “
Learning to Design Without Prior Data: Discovering Generalizable Design Strategies Using Deep Learning and Tree Search
,”
ASME J. Mech. Des.
,
145
(
3
), p.
031402
.
11.
Mabey
,
C. S.
,
Salmon
,
J. L.
, and
Mattson
,
C. A.
,
2023
, “
Agent-Based Product-Social-Impact-Modeling: A Systematic Literature Review and Modeling Process
,”
ASME J. Mech. Des.
,
145
(
11
), p.
110801
.
12.
Malshe
,
A. P.
,
Bapat
,
S.
,
Rajurkar
,
K. P.
,
Liu
,
A.
, and
Linares
,
J.-M.
,
2023
, “
Exploring the Intersection of Biology and Design for Product Innovations
,”
CIRP Ann.
,
72
(
2
), pp.
569
592
.
13.
Zhang
,
J.
,
Gao
,
J.
,
Simeone
,
A.
, and
Gu
,
P.
,
2023
, “
Game Analysis of Product Specifications for Design Optimisation Using Big Sales Data
,”
J. Eng. Des.
,
34
(
10
), pp.
844
864
.
14.
Cong
,
J.
,
Chen
,
C.-H.
,
Meng
,
X.
,
Xiang
,
Z.
, and
Dong
,
L.
,
2023
, “
Conceptual Design of a User-Centric Smart Product-Service System Using Self-Organizing Map
,”
Adv. Eng. Inform.
,
55
, p.
101857
.
15.
Liu
,
A.
,
Zhang
,
D.
,
Wang
,
Y.
, and
Xu
,
X.
,
2022
, “
Knowledge Graph With Machine Learning for Product Design
,”
CIRP Ann.
,
71
(
1
), pp.
117
120
.
16.
Rau
,
H.
,
Wu
,
J.-J.
, and
Procopio
,
K. M.
,
2023
, “
Exploring Green Product Design Through TRIZ Methodology and the Use of Green Features
,”
Comput. Ind. Eng.
,
180
, p.
109252
.
17.
Zhou
,
J.
,
Xiahou
,
T.
, and
Liu
,
Y.
,
2021
, “
Multi-objective Optimization-Based TOPSIS Method for Sustainable Product Design Under Epistemic Uncertainty
,”
Appl. Soft Comput.
,
98
, p.
106850
.
18.
Chen
,
E.
,
Li
,
H.
,
Cao
,
H.
, and
Wen
,
X.
,
2021
, “
An Energy Consumption Prediction Approach of Die Casting Machines Driven by Product Parameters
,”
Front. Mech. Eng.
,
16
(
4
), pp.
868
886
.
19.
Lv
,
Y.
,
Li
,
C.
,
He
,
J.
,
Li
,
W.
,
Li
,
X.
, and
Li
,
J.
,
2022
, “
Energy Saving Design of the Machining Unit of Hobbing Machine Tool With Integrated Optimization
,”
Front. Mech. Eng.
,
17
(
3
), p.
38
.
20.
Brunoe
,
T. D.
,
Soerensen
,
D. G. H.
, and
Nielsen
,
K.
,
2021
, “
Modular Design Method for Reconfigurable Manufacturing Systems
,”
Proc. CIRP
,
104
, pp.
1275
1279
.
21.
Kim
,
J.
,
Saidani
,
M.
, and
Kim
,
H. M.
,
2021
, “
Designing an Optimal Modular-Based Product Family Under Intellectual Property and Sustainability Considerations
,”
ASME J. Mech. Des.
,
143
(
11
), p.
112002
.
22.
Green
,
E.
,
Estrada
,
S.
,
Gopalakrishnan
,
P. K.
,
Jahanbekam
,
S.
, and
Behdad
,
S.
,
2022
, “
A Graph Partitioning Technique to Optimize the Physical Integration of Functional Requirements for Axiomatic Design
,”
ASME J. Mech. Des.
,
144
(
5
), p.
051402
.
23.
Chen
,
L.
,
Wei
,
N.
,
Zheng
,
Y.
, and
Xi
,
J.
,
2024
, “
Variation Analysis Method Based on Product Feature Information Network
,”
ASME J. Mech. Des.
,
146
(
6
), p.
061706
.
24.
Gu
,
P.
,
Hashemian
,
M.
, and
Nee
,
A. Y. C.
,
2004
, “
Adaptable Design
,”
CIRP Ann.
,
53
(
2
), pp.
539
557
.
25.
Gu
,
P.
,
Xue
,
D.
,
Peng
,
Q.
, and
Zhang
,
J.
,
2024
,
Adaptable Design: Methods and Applications
,
Springer Singapore
,
Singapore
.
26.
Li
,
Y.
,
Xue
,
D.
, and
Gu
,
P.
,
2008
, “
Design for Product Adaptability
,”
Concurr. Eng.
,
16
(
3
), pp.
221
232
.
27.
Fletcher
,
D.
,
Brennan
,
R. W.
, and
Gu
,
P.
,
2010
, “
A Method for Quantifying Adaptability in Engineering Design
,”
Concurr. Eng.
,
17
(
4
), pp.
279
289
.
28.
Cheng
,
Q.
,
Zhang
,
G.
,
Liu
,
Z.
,
Gu
,
P.
, and
Cai
,
L.
,
2011
, “
A Structure-Based Approach to Evaluation Product Adaptability in Adaptable Design
,”
J. Mech. Sci. Technol.
,
25
(
5
), pp.
1081
1094
.
29.
Shannon
,
C. E.
,
1948
, “
A Mathematical Theory of Communication
,”
Bell Syst. Tech. J.
,
27
(
3
), pp.
379
423
.
30.
Sun
,
Z.
,
Wang
,
K.
,
Chen
,
Y.
,
Xue
,
D.
, and
Gu
,
P.
,
2021
, “
Information Entropy Method for Product Adaptable Design Evaluation
,”
Chin. J. Eng. Des.
,
28
(
1
), p.
13
.
31.
Ulrich
,
K.
,
1995
, “
The Role of Product Architecture in the Manufacturing Firm
,”
Res. Pol.
,
24
(
3
), pp.
419
440
.
32.
Sun
,
Z.
,
Wang
,
K.
, and
Gu
,
P.
,
2023
, “
Information Entropy Approach to Design Adaptability Evaluation
,”
CIRP Ann.
,
72
(
1
), pp.
97
100
.
33.
Ngo
,
T. D.
,
Kashani
,
A.
,
Imbalzano
,
G.
,
Nguyen
,
K. T. Q.
, and
Hui
,
D.
,
2018
, “
Additive Manufacturing (3D Printing): A Review of Materials, Methods, Applications and Challenges
,”
Compos. Part B
,
143
, pp.
172
196
.
34.
Gibson
,
I.
,
Rosen
,
D.
,
Stucker
,
B.
, and
Khorasani
,
M.
,
2021
, “Hybrid Additive Manufacturing,”
Additive Manufacturing Technologies
,
Springer Cham
,
Switzerland
, pp.
347
366
.
35.
Tang
,
J.
,
Yang
,
Z.
, and
Deng
,
J.
,
2022
, “
3D Printer (Ender-3 S1)
,” China Patent No. CN307384893S.
36.
Yang
,
J.
, and
Luo
,
W.
, 2021, “
Carving Machine
,” China Patent No. CN306811127S.
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