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Export Reference (APA) 
Panda, B. N., Leite, M., Biswal, B. B., Niu, X. & Garg, A. (2018). Experimental and numerical modelling of mechanical properties of 3D printed honeycomb structures. Measurement. 116, 495-506
Export Reference (IEEE) 
B. N. Panda et al.,  "Experimental and numerical modelling of mechanical properties of 3D printed honeycomb structures", in Measurement, vol. 116, pp. 495-506, 2018
Export BibTeX 
@article{panda2018_1716230309770,
	author = "Panda, B. N. and Leite, M. and Biswal, B. B. and Niu, X. and Garg, A.",
	title = "Experimental and numerical modelling of mechanical properties of 3D printed honeycomb structures",
	journal = "Measurement",
	year = "2018",
	volume = "116",
	number = "",
	doi = "10.1016/j.measurement.2017.11.037",
	pages = "495-506",
	url = "https://www.sciencedirect.com/science/article/pii/S0263224117307467?via%3Dihub"
}
Export RIS 
TY  - JOUR
TI  - Experimental and numerical modelling of mechanical properties of 3D printed honeycomb structures
T2  - Measurement
VL  - 116
AU  - Panda, B. N.
AU  - Leite, M.
AU  - Biswal, B. B.
AU  - Niu, X.
AU  - Garg, A.
PY  - 2018
SP  - 495-506
SN  - 0263-2241
DO  - 10.1016/j.measurement.2017.11.037
UR  - https://www.sciencedirect.com/science/article/pii/S0263224117307467?via%3Dihub
AB  - In recent years, 3-D printing experts have laid emphasis on designing and printing the cellular structures, since the key advantages (high strength to weight ratio, thermal and acoustical insulation properties) offered by these structures makes them highly versatile to be used in aerospace and automotive industries. In the present work, an experimental study is firstly conducted to study the effects of the design parameters (wall thickness and cell size) on the mechanical properties i.e yield strength and modulus of elasticity (stiffness) of honeycomb cellular structures printed by fused deposition modelling (FDM) process. Further, three promising numerical modelling methods based on computational intelligence (CI) such as genetic programming (GP), automated neural network search (ANS) and response surface regression (RSR) were applied and their performances were compared while formulating models for the two mechanical properties. Statistical analysis concluded that the ANS model performed the best followed by GP and RSR models. The experimental findings were validated by performing the 2-D, 3-D surface analysis on formulated models based on ANS.
ER  -