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Experimental and numerical modelling of mechanical properties of 3D printed honeycomb structures

机译:3D印刷蜂窝结构力学性能的实验性和数值模型

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摘要

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.
机译:近年来,3-D印刷专家在这些结构提供的主要优点(高强度为重量比,热和声学绝缘性能)的关键优势(高强度为重量比,热和声学绝缘性能)使它们具有高度通用的通用性,以便在航空航天中使用和汽车工业。在本作工作中,首先进行实验研究,以研究设计参数(壁厚和电池尺寸)对由融合沉积建模印刷的蜂窝细胞结构的屈服强度和弹性模量(刚度)的影响( FDM)过程。此外,应用了基于计算智能(CI)的三种有前途的数值建模方法,例如遗传编程(GP),自动神经网络搜索(ANS)和响应表面回归(RSR),并将其性能进行比较,同时为两种机械制定模型特性。统计分析得出结论,ANS模型表现最佳,后跟GP和RSR模型。通过基于ANS的配制模型进行2-D,3-D表面分析来验证实验结果。

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