Contacts
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Christopher Peter Kohar
Postdoc (PhD)
Education:
- BASc, University of Waterloo, Canada
- PhD, University of Waterloo, Canada
Research interests:
- Crashworthiness and Structural Optimization
- Multi-scale Modelling
- Computational Intelligence
- Crystal Plasticity
- Micromechanics and Homogenization
Summary of work:
Current automotive structures require complex assemblies of various materials and manufacturing processes to achieve lightweight solutions. The manufacturing process to generate these components can greatly influence the macro and micro-mechanical behaviour of these materials and ultimately, the energy absorption characteristics of the geometries in a collision.
Through the use of computational intelligence techniques, such as artificial neural networks and genetic evolution, these automotive structures can be optimized to generate a robust design that takes advantage of the microstructure influences during manufacturing processing for performance. The goal of my research is to develop new technological platforms that utilize new advanced microstructural and multi-scale modeling techniques to achieve weight reductions of 30-40% within structural components for massed production mid-size vehicles.
Trevor Donald Sabiston
Postdoc (PhD)
Education:
- PhD, University of Waterloo, Canada
- BASc, University of Waterloo, Canada
- Certificate in University Teaching, University of Waterloo, Canada
Research interests:
- Composite Micromechanics
- Composite Homogenization Schemes
- Deformation/strain Partitioning
- Interphase-based Modeling Methods
- Fibre Orientation Distributions and Fibre Length Distributions
Summary of work:
In order to satisfy government regulations for fuel economy, along with consumers’ demand for more technology in vehicles, automotive manufacturers need to adopt lightweight materials for vehicle construction. Composite materials offer much higher specific strength and stiffness compared to traditional metallic materials.
In order to incorporate composite materials within automotive structures predictive models are required to aid in the design and validation of composite components. Specifically, a new modelling framework is required for three dimensional structures common in the automotive industry, since most composite models have been developed for two dimensional structures.
For improved manufacturing cycle times, compression molding materials such as sheet molding compound and long fibre thermoplastics are desirable. The performance and response of parts manufactured using these processes are dependent on the fibre orientation and fibre length distribution within the part.
The goal of my research is to develop a framework for modelling three dimensional composite structures, and incorporate fibre orientation distributions to account for the manufacturing processing effects on the mechanical response of automotive composite components.
Waqas Muhammad
Postdoc (PhD)
Education:
- BASc, University of Waterloo, Canada
- MASc, University of Waterloo, Canada
- PhD, University of Waterloo, Canada
Research interests:
- Failure and Fracture of Materials
- Experimental Characterization
- Microstructure and Texture
- Large Plastic Deformations
- Cyclic Plasticity
- Multi-scale Modeling
- Micromechanics and Homogenization
- Phenomenological and Crystal Plasticity Modeling
Summary of work:
The automotive industry is focusing on the use of lightweight materials such as Aluminum and Magnesium alloys in an effort to lower the fuel consumption and reduce harmful carbon emissions. An important step in new material development and design of automotive parts, is to perform finite element (FE) simulations to generate optimized designs exhibiting superior crashworthiness and forming behavior.
In this regard, an accurate prediction of the post necking and fracture behavior of the material is essential for optimizing the impact performance and formability of designed parts. As of now, localization, damage and fracture phenomena are not well understood for precipitation hardened aluminum alloys.
The goal of my research is to study the localization, damage and fracture behavior of 6000 series aluminum alloys through a well-designed experimentation process, with emphasis on developing a physically motivated crystal plasticity-based fracture model applicable to precipitation hardening aluminum alloys.
Jaspreet Singh Nagra
Postdoc (PhD)
Education:
- BASc, Punjab Technical University, India
- MASc, Thapar University, India
Research interests:
- Spectral Methods
- Crystal Plasticity
- Peridynamics
- Modeling Failure and Fracture of Materials
- Micromechanics and Homogenization
- Multi-scale Modeling
- CAD/CAM/CAE
- Design Automation and CNC Toolpath Generation
Summary of work:
Virtual fabrication is a key ingredient for increasing the competitiveness of the industry, by reducing the time from concept to market and by increasing quality and reliability of the final product. Nowadays, in automotive and aerospace industries, an important part of the virtual factory relies on the numerical simulations of aluminum parts using state-of-the-art crystal plasticity techniques. Better understanding of microstructure evolution of aluminum can significantly improve the accuracy of the numerical predictions.
To achieve this, the crystal plasticity model must capture the evolution of 3D microstructural features such as texture, grain shape and grain interactions with deformation. The goals of my research include development of an efficient spectral method based full-field crystal plasticity framework and development of in-house codes for bond-based and state-based Peri dynamic techniques.
Eventually creating a hierarchical multiscale framework (virtual laboratory) that can accurately model the effects of microstructure on fracture and crack propagation in the lab-scale components.
Rama Krushna Sabat
Postdoc (PhD)
Education:
- B. Tech, National Institute of Technology, Rourkela, India
- PhD, Indian Institute of Science, Bangalore, India
Research interests:
- Microstructure and Texture
- Mechanical Properties
- Crystal Plasticity
- Finite element method modelling
- Microscopy
- Severe Plastic Deformation
Summary of work:
The fossil fuel crisis has led to the development of lightweight materials with high specific strength mainly in the automobile and aircraft industries. Magnesium and its alloys are known as the lightest structural materials in the world. However, the formability of magnesium is the most difficult challenge, which limits its applications on an industrial scale.
Hence, novel deformation processes, like equal channel angular and multi-axial processing, have been used to refine the grain size and modify the basal texture, which enhances the formability of the material. Similar studies have been extended to cp titanium and Mg composites.
Currently, I am working on the effects of high strain rate on the formation of nano voids and its subsequent effect on the fracture strain of the aluminum alloys. Further, my interest is to calculate the precipitate shape, size during strain rate jump test in the age hard enable aluminium alloys.
Oxana Skiba
Postdoc (PhD)
Education:
- BSC, Saint Petersburg State University, Russia
- MSC, Saint Petersburg State University, Russia
- PhD, University of Waterloo, Canada
Research interests:
- Crystal Plasticity
- Artificial Intelligence
- Material Modeling
- Multiphysical Dislocation Dynamics Models
Summary of work:
Discrete Dislocation Dynamics models provide a framework to advance the understanding of plasticity. Multiphysical phenomena are often present during plastic deformation.
Two particular examples are the electromechanical behavior of plastically deformed piezoelectric materials and the thermomechanical behavior of metals under high strain rate plastic deformation. Therefore, two new Discrete Dislocation Dynamics models were developed; dislocations are directly modeled as crystallographic line defects in an elastic continuum.
These models are based on the Extended Finite Element Method (XFEM), which is a versatile tool used to analyze discontinuities, singularities, localized deformations, and complex geometries.
Currently, I am working on developing and implementing machine learning techniques for the crystal plasticity models.