Date of Award

2020

Document Type

Open Access Master's Report

Degree Name

Master of Science in Mechanical Engineering (MS)

Administrative Home Department

Department of Mechanical Engineering-Engineering Mechanics

Advisor 1

Trisha Sain

Committee Member 1

Gregory M. Odegard

Committee Member 2

Bruce P. Lee

Abstract

Polymeric adhesives are being extensively used to develop light weight and durable Composite Materials which are have been employed in automotive, aeronautical, medical industry. Polymeric adhesives undergo failure due to relative opening sliding of bonded interfaces which requires a need to characterize the fracture mechanism. Numerous experiments are conducted to predict the fracture response for Mode-I, II,III and numerical simulations are employed to inversely calibrate the material parameters governing the constitutive laws. Cohesive zone models are largely employed in fracture mechanics to predict failure of interfaces undergoing large plastic deformations. The objective of this report is to employ the cohesive zone models to predict fracture of polymeric interfaces. Cohesive zone models are governed by traction separation relationships which can be defined both at the continuum level which is macroscale and at the molecular/atomic level. The report is organized into three sections: 1st section is focused on a machine learning approach to characterize the rate dependent response of polymeric adhesive by inverse calibration of a continuum based cohesive zone parameters. The 2nd section aims to develop a stochastic bond kinetics based cohesive law defined at microscopic level to predict the rate dependent fracture. The 3rd section’s objective is to perform material and cohesive zone calibration to characterize the adhesive strength of smart adhesives.

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