Journal
MEASUREMENT
Volume 116, Issue -, Pages 495-506Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2017.11.037
Keywords
3D printing (3DP); Cellular structures; Computational intelligence (CI); Mechanical Properties
Funding
- FCT, through IDMEC, under LAETA [UID/EMS/50022/2013]
- Shantou University Scientific Research Fund [NTF 16002]
- International Science AMP
- Technology Collaboration project between China and Israel [2017A050501061]
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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.
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