@Article{icces.2023.09242,
AUTHOR = {Zhujiang Wang, Yizhou Wang, Bin Zhai},
TITLE = {Development	of	a	Graded	Lattice Structure Design and	Optimization	 Method	with	Complex	Boundary	Surface	Constraints},
JOURNAL = {The International Conference on Computational \& Experimental Engineering and Sciences},
VOLUME = {26},
YEAR = {2023},
NUMBER = {3},
PAGES = {1--2},
URL = {http://www.techscience.com/icces/v26n3/54062},
ISSN = {1933-2815},
ABSTRACT = {Graded	lattice	structures	(GLS)	are	used	widely	in	the	areas	of	3D	printed	sensors,	personalized	wearable	
devices,	robotics,	energy	absorption,	etc.,	and	have	a	prospective	future	in	the	field	of	personalized	medical	
devices.	 The	 large-scale	 applications	 of	 GLS-based	 personalized	 medical	 devices	 require	 a	 GLS	 design	
method	 that	 could	 handle	 the	 challenges	 caused	 by	 diverse	 boundary	 surface	 constraints	 and	 various	
requirements	of	graded	mechanical	properties	 [1,2],	due	 to	patient-specific	care	needs.	 In	 this	work,	 the	
proposed	 automatic	 seed	 generation	 algorithm-based	 GLS	 design	 approach	 is	 a	 prospective	 solution	 to	
promote	 the	 wide	 application	 of	 GLS-based	 personalized	 medical	 devices	 [3,4].	 The	 core	 idea	 of	 the	
proposed	GLS	design	and	optimization	approach	is	(a)	using	an automatic	point	cloud	generation	algorithm	
to	generate	nonuniform	seed	distributions	inside	a	domain	with	any	shape;	(b)	raw	GLS	is	then	generated	
based	on	the	seed	distribution;	(c)	the	radius	of	the	raw	GLS	is	further	optimized	based	on	finite	element	
truss	analysis.	As	seed	distributions	are	generated	according	to	the	required	boundary	shapes	of	the	target	
GLS	and	the	graded	mechanical	properties	are	optimized	based	on	finite	element	analysis,	the	resulting	GLS	
is	 guaranteed	 to	 satisfy	 the	 requirements	 of	 boundary	 constraints	 and	 graded	 mechanical	 properties.	
Finally,	several	demos	including	a	3D-printed	shoe	sole,	a	bone	scaffold,	and	an	energy	absorber	are	created,	
and	the	results	indicate	that	the	proposed	GLS	design	and	optimization	method	works	very well.},
DOI = {10.32604/icces.2023.09242}
}