MECHANICAL BEHAVIOR OF CHIRAL AND NON-CHIRAL ARCHITECTED SOLIDS DESIGNED BY ARTIFICIAL INTELLIGENCE

Abstract

An artificial intelligence technique is adopted to create a series of chiral or non-chiral materials in two dimensions identified by their geometric features In order to characterize the effective elastic and viscoelastic properties of the chiral samples corresponding finite element models are constructed Effective Young’s modulus Poisson’s ratio bulk modulus biaxial shear modulus pure shear modulus and simple shear modulus are calculated to monitor the effects of microstructural parameters It is found that negative effective Poisson’s ratio can be achieved in certain microstructural settings When the ratio of l to r is 1 there is without negative Poisson’s ratio (NPR) phenomenon But when the ratio of l to r is 1 5 the normalized angle ratio is 0 8 and the ratio of l to r is 2 the normalized angle ratio is 0 8 they appear NPR phenomenon Tension-bending deformation coupling is quantified in chiral materials Hierarchical effects on the effective properties are analyzed When the rank changes from 1 to 3 it can increase its Young’s modulus The Young’s modulus changes from 17 5 kPa to 19 02 kPa and Poisson’s ratio becomes -0 26 to -0 36 For negative thermal expansion coefficient (NTEC) structures we discuss that the curvature of skeleton effects on TEC and PR When the structures are already under NTEC situation greater curvature can cause more negative TEC and PR The NTEC decreases from -1 49x10^6 to -9 6x10^5 K^-1 and Poisson’s ratio decreases -0 72 to -0 98 while curvature increase from 0 09 to 0 2 cm^-1 In addition we analyze non-monotonic time-dependent Poisson’s ratio under creep in re-entrant cell honeycombs Crack propagation in re-entrant cell honeycomb composites is investigated via the phase-field modeling When the Poisson’s ratio is close to 0 5 it can decrease the crack propagation On the contrary when the Poisson’s ratio is close to -1 it can increase the crack propagation

Date of Award	2020
Original language	English
Supervisor	Yun-Che Wang (Supervisor)