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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.

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.

This research focuses on the response of the Q3, Hybrid III 3-year-old dummy and a child finite element model in a simulated 213 sled test. The Q3 and Hybrid III 3-year old child finite element models were developed by First Technology Safety Systems. The 3-year-old child finite element model was developed by Nagoya University by model-based scaling from the AM50 (50 percentile male) total human model for safety. The child models were positioned in a forward facing, five-point child restraint system using Finite Element Model Builder. An acceleration pulse acquired from an experimental 213 sled test, which was completed following the guidelines outlined in the Federal Motor Vehicle Safety Standard 213 using a Hybrid III 3-year-old dummy, was applied to the seat buck supporting the child restraint seat. The numerical simulations utilizing the Q3, Hybrid III 3-year-old and the child finite element model were conducted using the explicit non-linear finite element code LS-DYNA.

Rollover crash is one of the most serious safety problems for light weight vehicles. In the USA, rollover crashes account for almost one-third of all occupant fatalities in light weight vehicles. Similar statistics are found for other countries. Thus, rollover crashes have received significant attention in recent years. In the USA and Canada, automotive manufacturers are required to comply with the roof strength requirement of “1.5 times the unloaded vehicle weight” to ensure safety in rollover. NHTSA is currently considering a set of countermeasures to improve the rollover safety, where one of the proposals is to increase the roof strength limit to “2.5 times the unloaded vehicle weight”. This increased roof strength limit seemingly has been motivated based on the benchmark study of current vehicle fleet.

This research focuses on the further development of a child finite element model whereby implementation of pediatric cadaver testing observations considering the biomechanical response of the neck of children under tensile and bending loading has occurred. Prior to this investigation, the biomechanical neck response was based upon scaled adult cadaver behaviour. Alterations to the material properties associated with ligaments, intervertebral discs and facet joints of the pediatric cervical spine were considered. No alteration to the geometry of the child neck finite element model was considered. An energy based approach was utilized to provide indication on the appropriate changes to local neck biomechanical characteristics. Prior to this study, the biomechanical response of the neck of the child finite element model deviated significantly from the tensile and bending cadaver tests completed by Ouyang et al.

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.

This research focuses on the injury potential of children seated in forward facing child restraint seats during frontal vehicle crashes. Experimental sled tests were completed in accordance to the Federal Motor Vehicle Safety Standard 213 using a Hybrid III three-year-old dummy in a five point child restraint system. A full vehicle crash test was completed in accordance to the Canadian Motor Vehicle Safety Standard 208 with the addition of a three-year-old Hybrid III crash test dummy, seated behind the passenger seat, restrained in the identical five-point child safety seat. Different child restraint finite element models were developed incorporating a subset of the apparatus used in the two experimental tests and simulated using LS-DYNA.

Automotive steering wheels depend on a structural skeleton made of steel, aluminum, or magnesium to be the basis for the mechanical properties of the finished part. The mechanical properties of concern are the fatigue properties and the crash performance. The purpose of this study was to evaluate the crash and the fatigue performance of a steering wheel skeleton fabricated by high pressure die casting. Two materials were used to produce two groups of wheels with identical geometry. The production part was designed, optimized and fabricated with AM50A magnesium. The production magnesium component met all of the regulatory design and performance requirements. A small sample run was made in a proprietary aluminum - magnesium alloy. The fatigue and crash properties were evaluated empirically. In fatigue testing, the aluminum skeletons displayed a significant improvement, with respect to the magnesium skeletons, in the number of cycles to failure at the loads tested.

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.