Abstract

The design representations of lattice structures are fundamental to the development of computational design approaches. Current applications of lattice structures are characterized by ever-growing demand on computational resources to solve difficult optimization problems or generate large datasets, opting for the development of efficient design representations which offer a high range of possible design variants, while at the same time generating design spaces with attributes suitable for computational methods to explore. In response, the focus of this work is to propose a parametric design representation based on crystallographic symmetries and investigate its implications for the computational design of lattice structures. The work defines design rules to support the design of functionally graded structures using crystallographic symmetries such that the connectivity between individual members in a structure with varying geometry is guaranteed and investigates how to use the parametrization in the context of optimization. The results show that the proposed parametrization achieves a compact design representation to benefit the computational design process by employing a small number of design variables to control a broad range of complex geometries. The results also show that the design spaces based on the proposed parametrization can be successfully explored using a direct search-based method.

References

1.
Greer
,
J. R.
, and
Deshpande
,
V. S.
,
2019
, “
Three-Dimensional Architected Materials and Structures: Design, Fabrication, and Mechanical Behavior
,”
MRS Bull.
,
44
(
10
), pp.
750
757
.
2.
Ashby
,
M. F.
, and
Gibson
,
L. J.
,
1997
,
“Cellular Solids: Structure and Properties
,
Press Syndicate of the University of Cambridge
,
Cambridge, UK
, pp.
175
231
.
3.
Lumpe
,
T. S.
, and
Stankovic
,
T.
,
2021
, “
Exploring the Property Space of Periodic Cellular Structures Based on Crystal Networks
,”
Proc. Natl. Acad. Sci.
,
118
(
7
), p.
e2003504118
.
4.
Bauer
,
J.
,
Meza
,
L. R.
,
Schaedler
,
T. A.
,
Schwaiger
,
R.
,
Zheng
,
X.
, and
Valdevit
,
L.
,
2017
, “
Nanolattices: An Emerging Class of Mechanical Metamaterials
,”
Adv. Mater.
,
29
(
40
), p.
1701850
.
5.
Dong
,
G.
,
Tang
,
Y.
, and
Zhao
,
Y. F.
,
2017
, “
A Survey of Modeling of Lattice Structures Fabricated by Additive Manufacturing
,”
ASME J. Mech. Des.
,
139
(
10
), p.
10
.
6.
Tamburrino
,
F.
,
Graziosi
,
S.
, and
Bordegoni
,
M.
,
2018
, “
The Design Process of Additively Manufactured Mesoscale Lattice Structures: A Review
,”
ASME J. Comput. Inf. Sci. Eng.
,
18
(
4
), p.
040801
.
7.
du Plessis
,
A.
,
Broeckhoven
,
C.
,
Yadroitsava
,
I.
,
Yadroitsev
,
I.
,
Hands
,
C. H.
,
Kunju
,
R.
, and
Bhate
,
D.
,
2019
, “
Beautiful and Functional: A Review of Biomimetic Design in Additive Manufacturing
,”
Addit. Manuf.
,
27
, pp.
408
427
.
8.
Greaves
,
G. N.
,
Greer
,
A. L.
,
Lakes
,
R. S.
, and
Rouxel
,
T.
,
2011
, “
Poisson’s Ratio and Modern Materials
,”
Nat. Mater.
,
10
(
11
), pp.
823
837
.
9.
Gatt
,
R.
,
Mizzi
,
L.
,
Azzopardi
,
J. I.
,
Azzopardi
,
K. M.
,
Attard
,
D.
,
Casha
,
A.
,
Briffa
,
J.
, and
Grima
,
J. N.
,
2015
, “
Hierarchical Auxetic Mechanical Metamaterials
,”
Sci. Rep.
,
5
(
1
), p.
8395
.
10.
Wagner
,
M. A.
,
Lumpe
,
T. S.
,
Chen
,
T.
, and
Shea
,
K.
,
2019
, “
Programmable, Active Lattice Structures: Unifying Stretch-Dominated and Bending-Dominated Topologies
,”
Extreme Mech. Lett.
,
29
, p.
100461
.
11.
Clausen
,
A.
,
Wang
,
F.
,
Jensen
,
J. S.
,
Sigmund
,
O.
, and
Lewis
,
J. A.
,
2015
, “
Topology Optimized Architectures With Programmable Poisson’s Ratio Over Large Deformations
,”
Adv. Mater.
,
27
(
37
), pp.
5523
5527
.
12.
Meeusen
,
L.
,
Candidori
,
S.
,
Micoli
,
L. L.
,
Guidi
,
G.
,
Stankovic
,
T.
, and
Graziosi
,
S.
,
2022
, “
Auxetic Structures Used in Kinesiology Tapes Can Improve Form-Fitting and Personalization
,”
Sci. Rep.
,
12
(
1
), p.
13509
.
13.
Zheng
,
X.
,
Lee
,
H.
,
Weisgraber
,
T. H.
,
Shusteff
,
M.
,
DeOtte
,
J.
,
Duoss
,
E. B.
,
Kuntz
,
J. D.
, et al
,
2014
, “
Ultralight, Ultrastiff Mechanical Metamaterials
,”
Science
,
344
(
6190
), pp.
1373
1377
.
14.
Schaedler
,
T. A.
,
Jacobsen
,
A. J.
,
Torrents
,
A.
,
Sorensen
,
A. E.
,
Lian
,
J.
,
Greer
,
J. R.
,
Valdevit
,
L.
, and
Carter
,
W. B.
,
2011
, “
Ultralight Metallic Microlattices
,”
Science
,
334
(
6058
), pp.
962
965
.
15.
Qin
,
Z.
,
Jung
,
G. S.
,
Kang
,
M. J.
, and
Buehler
,
M. J.
,
2017
, “
The Mechanics and Design of a Lightweight Three-Dimensional Graphene Assembly
,”
Sci. Adv.
,
3
(
1
), p.
e1601536
.
16.
Paulose
,
J.
,
Meeussen
,
A. S.
, and
Vitelli
,
V.
,
2015
, “
Selective Buckling via States of Self-Stress in Topological Metamaterials
,”
Proc. Natl. Acad. Sci.
,
112
(
25
), pp.
7639
7644
.
17.
Overvelde
,
J. T.
,
Shan
,
S.
, and
Bertoldi
,
K.
,
2012
, “
Compaction Through Buckling in 2D Periodic, Soft and Porous Structures: Effect of Pore Shape
,”
Adv. Mater.
,
24
(
17
), pp.
2337
2342
.
18.
Montemayor
,
L.
,
Chernow
,
V.
, and
Greer
,
J. R.
,
2015
, “
Materials by Design: Using Architecture in Material Design to Reach New Property Spaces
,”
MRS Bull.
,
40
(
12
), pp.
1122
1129
.
19.
Stanković
,
T.
, and
Shea
,
K.
,
2020
, “
Investigation of a Voronoi Diagram Representation for the Computational Design of Additively Manufactured Discrete Lattice Structures
,”
ASME J. Mech. Des.
,
142
(
11
), p.
111704
.
20.
Sigmund
,
O.
,
2000
, “
A New Class of Extremal Composites
,”
J. Mech. Phys. Solids
,
48
(
2
), pp.
397
428
.
21.
Andreassen
,
E.
,
Lazarov
,
B. S.
, and
Sigmund
,
O.
,
2014
, “
Design of Manufacturable 3D Extremal Elastic Microstructure
,”
Mech. Mater.
,
69
(
1
), pp.
1
10
.
22.
Stanković
,
T.
,
Mueller
,
J.
, and
Shea
,
K.
,
2017
, “
The Effect of Anisotropy on the Optimization of Additively Manufactured Lattice Structures
,”
Addit. Manuf.
,
17
, pp.
67
76
.
23.
Chen
,
D.
,
Skouras
,
M.
,
Zhu
,
B.
, and
Matusik
,
W.
,
2018
, “
Computational Discovery of Extremal Microstructure Families
,”
Sci. Adv.
,
4
(
1
), p.
eaao7005
.
24.
Bastek
,
J. H.
,
Kumar
,
S.
,
Telgen
,
B.
,
Glaesener
,
R. N.
, and
Kochmann
,
D. M.
,
2022
, “
Inverting the Structure-Property Map of Truss Metamaterials by Deep Learning
,”
Proc. Natl. Acad. Sci.
,
119
(
1
), p.
e2111505119
.
25.
Woldseth
,
R. V.
,
Aage
,
N.
,
Bærentzen
,
J. A.
, and
Sigmund
,
O.
,
2022
, “
On the Use of Artificial Neural Networks in Topology Optimisation
,”
Struct. Multidiscipl. Optim.
,
65
(
10
), p.
294
.
26.
Sunada
,
T.
,
2012
, “
Lecture on Topological Crystallography
,”
Japanese J. Math.
,
7
(
1
), pp.
1
39
.
27.
Rosen
,
J.
,
2008
,
Symmetry Rules: How Science and Nature are Founded on Symmetry
, 1st ed.,
Springer Berlin, Heidelberg
.
28.
O’Keeffe
,
M.
, and
Hyde
,
B. G.
,
2020
,
Crystal Structures: Patterns and Symmetry
,
Dover Publications
,
Mineola, NY
.
29.
Zok
,
F. W.
,
Latture
,
R. M.
, and
Begley
,
M. R.
,
2016
, “
Periodic Truss Structures
,”
J. Mech. Phys. Solids
,
96
, pp.
184
203
.
30.
Bhat
,
C.
,
Kumar
,
A.
,
Lin
,
S.-C.
, and
Jeng
,
J.-Y.
,
2023
, “
Design, Fabrication, and Properties Evaluation of Novel Nested Lattice Structures
,”
Addit. Manuf.
,
68
, p.
103510
.
31.
O’Keefe
,
M.
, and
Hyde
,
B. G.
,
1996
,
Crystal Structures
,
Mineralogical Society of America
,
Washington, DC
.
32.
Wang
,
F.
,
Sigmund
,
O.
, and
Jensen
,
J. S.
,
2014
, “
Design of Materials With Prescribed Nonlinear Properties
,”
J. Mech. Phys. Solids
,
69
, pp.
156
174
.
33.
Aage
,
N.
,
Andreassen
,
E.
,
Lazarov
,
B. S.
, and
Sigmund
,
O.
,
2017
, “
Giga-Voxel Computational Morphogenesis for Structural Design
,”
Nature
,
550
(
7674
), pp.
84
86
.
34.
Wang
,
L.
,
Tao
,
S.
,
Zhu
,
P.
, and
Chen
,
W.
,
2021
, “
Data-Driven Topology Optimization With Multiclass Microstructures Using Latent Variable Gaussian Process
,”
ASME J. Mech. Des.
,
143
(
3
), p.
031708
.
35.
Wang
,
J.
,
Callanan
,
J.
,
Ogunbodede
,
O.
, and
Rai
,
R.
,
2021
, “
Hierarchical Combinatorial Design and Optimization of Non-Periodic Metamaterial Structures
,”
Addit. Manuf.
,
37
, p.
101710
.
36.
Bendsoe
,
M. P.
, and
Sigmund
,
O.
,
2013
,
Topology Optimization: Theory, Methods, and Applications
,
Springer Berlin, Heidelberg
.
37.
Fleron
,
P.
,
1964
, “
Minimum Weight of Trusses
,”
Bygningsstatiske Meddelelser
,
35
(
3
), p.
81
.
38.
Dorn
,
W. S.
,
1964
, “
Automatic Design of Optimal Structures
,”
J. Mec.
,
3
, pp.
25
52
.
39.
Schwarz
,
J.
,
Chen
,
T.
,
Shea
,
K.
, and
Stanković
,
T.
,
2018
, “
Efficient Size and Shape Optimization of Truss Structures Subject to Stress and Local Buckling Constraints Using Sequential Linear Programming
,”
Struct. Multidiscipl. Optim.
,
58
(
1
), pp.
171
184
.
40.
Ning
,
X.
, and
Pellegrino
,
S.
, “
Design of Lightweight Structural Components for Direct Digital Manufacturing
,”
Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA
,
Honolulu, HI
,
Apr. 23–26
, p.
1807
.
41.
Preen
,
R. J.
, and
Bull
,
L.
,
2014
, “
Towards the Evolution of Vertical-Axis Wind Turbines Using Supershapes
,”
Evol. Intell.
,
7
(
3
), pp.
155
167
.
42.
Nessi
,
A.
, and
Stanković
,
T.
, “
Topology, Shape, and Size Optimization of Additively Manufactured Lattice Structures Based on the Superformula
,”
Proceedings of the International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers
,
Quebec City, Quebec, Canada
,
Aug. 26–29
, p. V02AT03A042.
43.
Gielis
,
J.
,
2003
, “
A Generic Geometric Transformation That Unifies a Wide Range of Natural and Abstract Shapes
,”
Am. J. Bot.
,
90
(
3
), pp.
333
338
.
44.
Liu
,
Y.
,
Zhuo
,
S.
,
Xiao
,
Y.
,
Zheng
,
G.
,
Dong
,
G.
, and
Zhao
,
Y. F.
,
2020
, “
Rapid Modeling and Design Optimization of Multi-Topology Lattice Structure Based on Unit-Cell Library
,”
ASME J. Mech. Des.
,
142
(
9
), p.
091705
.
45.
Letov
,
N.
, and
Fiona Zhao
,
Y.
,
2023
, “
Beam-Based Lattice Topology Transition With Function Representation
,”
ASME J. Mech. Des.
,
145
(
1
), p.
1
.
46.
Martínez
,
J.
,
Skouras
,
M.
,
Schumacher
,
C.
,
Hornus
,
S.
,
Lefebvre
,
S.
, and
Thomaszewski
,
B.
,
2019
, “
Star-Shaped Metrics for Mechanical Metamaterial Design
,”
ACM Trans. Graph.
,
38
(
4
), pp.
1
13
.
47.
Torczon
,
V.
,
1997
, “
On the Convergence of Pattern Search Algorithms
,”
SIAM J. Optim.
,
7
(
1
), pp.
1
25
.
48.
Kolda
,
T. G.
,
Lewis
,
R. M.
, and
Torczon
,
V.
,
2003
, “
Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods
,”
SIAM Rev.
,
45
(
3
), pp.
385
482
.
49.
Audet
,
C.
, and
Dennis
,
J. E.
,
2006
, “
Mesh Adaptive Direct Search Algorithms for Constrained Optimization
,”
SIAM J. Optim.
,
17
(
1
), pp.
188
217
.
50.
Audet
,
C.
,
Le Digabel
,
S.
,
Montplaisir
,
V. R.
, and
Tribes
,
C.
,
2022
, “
Algorithm 1027: NOMAD Version 4: Nonlinear Optimization With the Mads Algorithm
,”
ACM Trans. Math. Softw.
,
48
(
3
), pp.
1
22
.
51.
Mladenović
,
N.
, and
Hansen
,
P.
,
1997
, “
Variable Neighborhood Search
,”
Comput. Oper. Res.
,
24
(
11
), pp.
1097
1100
.
52.
Currie
,
J.
,
Wilson
,
D. I.
,
Sahinidis
,
N.
, and
Pinto
,
J.
,
2012
, “
OPTI: Lowering the Barrier Between Open Source Optimizers and the Industrial MATLAB User
,”
Foundations Comput.-Aided Process Operations
,
24
, p.
32
.
53.
Gaspar
,
N.
,
Ren
,
X.
,
Smith
,
C.
,
Grima
,
J.
, and
Evans
,
K.
,
2005
, “
Novel Honeycombs With Auxetic Behaviour
,”
Acta Mater.
,
53
(
8
), pp.
2439
2445
.
54.
Smith
,
C. W.
,
Grima
,
J.
, and
Evans
,
K.
,
2000
, “
A Novel Mechanism for Generating Auxetic Behaviour in Reticulated Foams: Missing Rib Foam Model
,”
Acta Mater.
,
48
(
17
), pp.
4349
4356
.
55.
Dudek
,
K. K.
,
Iglesias Martínez
,
J. A.
, and
Kadic
,
M.
,
2022
, “
Variable Dual Auxeticity of the Hierarchical Mechanical Metamaterial Composed of Re-Entrant Structural Motifs
,”
Phys. Status Solidi B
,
259
(
12
), p.
2200404
.
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