We have openings for new PhD students in the Biomimetic Materials Laboratory at McGill University, starting January 2015.

It is important to note that the determination of Stress intensity Factors lead to major problem for researchers, some of them have proposed an analytical method based on linear fracture mechanic by using the theorem de Giffth and also Paris, other have proposed numerical method using finite elememt analysis codes such as Ansys or Abaqus, other researcher have used experimental method.
  However, these methods still need an improvement for given accurate results.
How to develop an analytical method and adopted to FGM structure.

By Ying Li, Martin Kröger, Wing Kam Liu

Modeling and simulation of manufacturing of advanced composite material is a must in today's industry as the use of advance composites is increasing dramatically. The aim is to reduce the number of trial and error by development of a simulation tool to model and predict the manufacturing fundamental phenomena happening simultaneously in different scales. Poromechanics is a multidisciplinary and multiphysics concept which can be used to achieve this goal.

A 3D constitutive model is proposed and verified with experimental data. Tension-torsion coupling effect and tension-compression asymmetry effect is investigate for tube shape memory alloy.

In this paper, a macroscopic model in a thermodynamically-consistent framework for shape memory alloys is proposed.

       A soft ionic conductor can serve as an artificial nerve in an artificial muscle. A polyacrylamide hydrogel is synthesized containing a hygroscopic salt, lithium chloride. Two layers of the hydrogel are used as ionic conductors to sandwich a dielectric elastomer and fabricate a highly stretchable and transparent actuator. When the two layers of the hydrogels are subject to a voltage, the actuator reduces its thickness and expands. An areal strain of 134% is demonstrated.

Smart materials are designed to have a controlled response to external stimuli. Shape-memory polymers (SMPs) are one of the most well known classes of mechanically active smart materials and have experienced an incredible amount of research attention over the last decade. They are able to recover programmed deformations when heated above a thermal transition; however, are generally considered a one-time event. Liquid-crystalline elastomers (LCEs) are another class of actively moving polymers; however, these materials can demonstrate reversible and repeatable shape memory without the need for “re-programming” after each actuation cycle.