Department of Physics, Engineering Physics & Astronomy

Department of Physics, Engineering Physics & Astronomy
Department of Physics, Engineering Physics & Astronomy


Kaan Inal,
Associate Professor
NSERC/General Motors Industrial Research Chair in “Integrated Computational
Mechanics for Mass Efficient Automotive Structures"
Director, Computational Mechanics Research Group (CMRG)
Associate Director, Waterloo Centre for Automotive Research (WatCAR)
University of Waterloo, 200 University Ave. West, Waterloo, ON N2L 3G1, Canada

Friday September 15th, 2017
1:30 p.m. Stirling A


Computational Materials Engineering tools capable of integrating microstructure based material and process design with performance driven structural optimization can play significant role in enhancing manufacturing competitiveness. Automotive industry is embracing ICEME tools to adopt aggressive engineering strategies to meet impending fuel economy and vehicle mass targets in a multi-material framework in a sustainable manner. Computational tools can aid the smart use of current materials and accelerate the development of light metals with enhanced formability, crashworthiness, etc. Successful applications of ICEME requires smart combination of experiments with computation at various length scales, for both calibration and validation of the numerical models. This talk presents multi-scale computational frameworks involving coupled micro-scale and macro-scale numerical models for high strength aluminum alloys, Advanced High Strength Steels (AHSS), magnesium alloys and composite materials. For the micro-scale computations, a new 3D finite element analyses based on rate-dependent crystal plasticity theory is developed that incorporates 3D microstructures accurately constructed from 2D electron backscatter diffraction (EBSD) data into finite element analyses. Mechanism based constitutive laws that permit strain hardening and saturation without external adjustment are employed. The macro-scale computations are done with advanced yield functions informed by micro-scale models. The so-called Extended Finite Element Models (X-FEM) and Element Free Galerkin approaches are used. Coupling these models with optimization frameworks based on genetic algorithms and neural networks provide a comprehensive ICEME toolset to satisfy design and performance requirements with materials and processes while meeting cost, mass and performance requirements simultaneously. An illustration of this integrated approach for a component level application with extruded aluminum alloys will be presented highlighting the extreme importance of experimental validation at various length scales for the successful implementation of ICEME in future vehicle lightweighting.