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Laser cladding is a method of material deposition through which a powdered or wire feedstock material is melted and consolidated by use of a laser to coat part of a substrate. Determining the parameters to fabricate the desired clad bead geometry for various configurations is problematic as it involves a significant investment of raw materials and time resources, and is challenging to develop a predictive model. The goal of this research is to develop an experimental methodology that minimizes the amount of data to be collected, and to develop a predictive model that is accurate, adaptable, and expandable. To develop the predictive model of the clad bead geometry, an integrated five-step approach is presented. From the experimental data, an artificial neural network model is developed along with multiple regression equations.

Contemporary manufacturing systems are still evolving. The system elements, layouts, and integration methods are changing continuously, and ‘collaborative robots’ (CoBots) are now being considered as practical industrial solutions. CoBots, unlike traditional CoBots, are safe and flexible enough to work with humans. Although CoBots have the potential to become standard in production systems, there is no strong foundation for systems design and development. The focus of this research is to provide a foundation and four tier framework to facilitate the design, development and integration of CoBots. The framework consists of the system level, work-cell level, machine level, and worker level. Sixty-five percent of traditional robots are installed in the automobile industry and it takes 200 hours to program (and reprogram) them.

Successful manufacture of Carbon Fibre Reinforced Polymers (CFRP) by Long-Fibre Reinforced Thermoplastic (LFT) processes requires knowledge of the effect of numerous processing parameters such as temperature set-points, rotational machinery speeds, and matrix melt flow rates on the resulting material properties after the final compression moulding of the charge is complete. The degree to which the mechanical properties of the resulting material depend on these processing parameters is integral to the design of materials by any process, but the case study presented here highlights the manufacture of CFRP by LFT as a specific example. The material processing trials are part of the research performed by the International Composites Research Centre (ICRC) at the Fraunhofer Project Centre (FPC) located at the University of Western Ontario in London, Ontario, Canada.

The paper describes a ‘virtual motorsports’ event developed by the University of Windsor Vehicle Dynamics and Control Research Group. The event was a competitive project-based component of a Vehicle Dynamics course offered by the University's Department of Mechanical, Automotive, & Materials Engineering. The simulated race was developed to provide fourth year automotive engineering students with design and race experience, similar to that found in Formula SAE®or SAE Baja®, but within the confines of a single academic semester. The project, named ‘Formula463’, was conducted entirely within a virtual environment, and encompassed design, testing, and racing of hi-fidelity virtual vehicle models. The efficacy of the Formula463 program to provide students with a design experience using model based simulation tools and methods has been shown over the past two years. All of the software has been released under a General Public License and is freely available on the authors website.

Light-weighting fiber composite materials introduced to reduce vehicle mass and known as innovative materials research activities since they provide high specific stiffness and strength compared to contemporary engineering materials. Nonetheless, there are issues related automation strategies and handling methods. Material handling of flexible textile/fiber components is a process bottleneck and it is currently being performed by setting up multi-stage manual operations for hand layups. Consequently, the long-term research objective is to develop semi-automated pick and place processes for flexible materials utilizing collaborative robots within the process. The immediate research is to experimentally validate innovatively designed grippers for efficient material pick and place tasks.

The fuel called E-85 can be burned effectively in engines similar to the engines currently mass-produced for use with gasoline. Since the ethanol component of this fuel is produced from crops such as corn and sugar cane, the fuel is almost fully renewable. The different physical and chemical properties of E-85, however, do require certain modifications to the common gasoline engine. The Windsor - St. Clair team has focused their attention to modifications that will improve fuel efficiency and reduce tailpipe emissions. Other modifications were also performed to ensure that the vehicle would still operate with the same power and driveability as its gasoline counterpart.

Hybrid Vehicles have gained momentum in the automotive industry. The joint action of power sources and energy storage systems for energizing the vehicle improves the vehicle's fuel economy while reducing its pollutant emissions and noise levels, challenging automotive designers to optimize vehicle's cost, weight and control. The marketing success of hybrid vehicles significantly depends on the selection, integration and cost of the energy systems. The internal combustion engine, dominant of the vehicle market, has been the “option of choice” for auxiliary power unit of the hybrid vehicle, although other power sources as fuel cells, Stirling engines and gas turbines have been employed as well [1]. This document is focused in the application of Stirling engines as the power source for automobile propulsion.

This study provides an overview of a novel method for evaluating in-vehicle speech intelligibility using the Speech Intelligibility Index (SII). The approach presented is based on a measured speech signal evaluated at the sentence Speech Reception Threshold (sSRT) in a simulated driving environment. In this context, the impact of different band importance functions in the evaluation of the SII using the Hearing in Noise Test (HINT) in a driving simulator is investigated.

Neural networks are flexible modeling tools that can be used in conjunction with multi-body dynamics models to better predict nonlinear behaviour of components. This paper focuses on a process that incorporates a neural network model of a nonlinear damping force into a single degree of freedom mass-spring-damper model. Software tools and their interaction are specified. The verification of this process is the focal point of this paper and is a necessary step before further correlation studies can be performed on more complex component representations.

The development of a collision severity model can serve as an important tool in understanding the requirements for devising countermeasures to improve occupant safety and traffic safety. Collision type, weather conditions, and driver intoxication are some of the factors that may influence motor vehicle collisions. The objective of this study is to use artificial neural networks (ANNs) to identify the major determinants or contributors to fatal collisions based on various driver, vehicle, and environment characteristics obtained from collision data from Transport Canada. The developed model will have the capability to predict similar collision outcomes based on the variables analyzed in this study. A multilayer perceptron (MLP) neural network model with feed-forward back-propagation architecture is used to develop a generalized model for predicting collision severity. The model output, collision severity, is divided into three categories - fatal, injury, and property damage only.

The paper describes a closed-loop vehicle simulation environment developed to support a virtual vehicle design and testing methodology, proposed for the University of Windsor Formula SAE team. Virtual prototyping and testing were achieved through co-simulation of Matlab/Simulink® and Carsim®. The development of the required hybrid-control driver and vehicle models are described. The proposed models were validated with in vehicle test data. The proposed methods have shown to be effective and robust in predicting driver response, while controlling the vehicle within the developed simulation environment.