MEAM Ph.D. Thesis Defense: “Mechanics-Informed Optimization for Enhanced Adhesion and Toughness”

Structural design optimization has long played a crucial role in engineering, often with the goal of creating stiff and lightweight structures for aerospace and other applications. However, optimizing structures against failure is also crucial and has been less explored. Failure at interfaces is particularly challenging in design optimization as they involve high local stress concentrations and singular stresses. This thesis will demonstrate routes to integrate mechanics models and optimization schemes to engineer interfaces with improved adhesion and toughness. Specifically, four distinct structural design cases are considered: adhesive fibrils, shear lap joints, architected adhesive joints, and frames with locally tunable stiffness. The computational framework uses finite element analysis with multiple optimization methods, including gradient and heuristic-based techniques, as well as machine learning-based approaches. We show that performance can be improved by formulating optimization schemes and objective functions based on the principles of interface mechanics and failure. Optimal designs are determined via these computational schemes and validated via experiments on several different material systems. For example, the force capacity of a lap joint was enhanced by a factor of 2.4x by optimizing the thickness profile of the joint. The versatility of the computational and optimization schemes that have been developed enables them to be extended to other scenarios where performance can be improved by optimizing geometry and structure to control stresses.