Search Results

Carbon fiber sheet molding compound (SMC) is an attractive material for automotive lightweighting applications, but several issues present themselves when adapting a process developed for glass fiber composites to instead use carbon fibers. SMC is a discontinuous fiber material, so individual carbon fiber tows must be chopped into uniform rovings before being compounded with the resin matrix. Rotary chopping is one such method for producing rovings, but high wear rates are seen when cutting carbon fibers. Experiments were performed to investigate the wear progression of cutting blades during rotary carbon fiber chopping. A small rotary chopper with a polyurethane (PU) backing and thin, hardened steel blades was used to perform extended wear tests (120,000 chops, or until failure to reliably chop tows) to simulate the lifespan of blades during composite material production.

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 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 following paper presents an outline of the current state of driver modeling along with the various methods that are employed in their development. In recent years, vehicle manufacturers have implemented various systems that, in some manner, improve the operation of their vehicles. Many of these systems include an electronically controlled device which is capable of making decisions based on the immediate conditions affecting the vehicle. Much of the influence to develop such systems stems from the issue of safety: in emergency situations the control device is capable of making a decision quicker than the driver and thus reduces the potential for some form of collision. Another motivating factor behind these systems is to improve fuel efficiency, specifically in regard to hybrid vehicles where more than one form of propulsion is used and such devices can aid the driver to operate in a more efficient manner.

Though the purpose of a vehicle's suspension is multi-faceted and complex, the fundamentals may be simply stated: the suspension exists to provide the occupants with a tolerable ride, while simultaneously ensuring that the tires maintain good contact with the ground. At the root of the familiar ride/handling compromise, is the problem that tuning efforts which improve either grip or handling are generally to the detriment of the other. This study seeks to set forth a clear means for examining the familiar ride/handing compromise, by first exploring the key ideas of these terms, and then by describing the development of content-rich metrics to permit a direct optimization strategy. For simplicity, the optimization problem was examined in a unilateral manner, where heave (vertical; z-axis) behaviour is examined in isolation, though the methods described herein may be extended to pitch and roll behaviour as well.

A finite element model was developed to assess the mechanical behaviour of pultruded glass fibre reinforced polymer (GFRP) box sections with embedded steel rods; the performance of these virtual specimens was compared with that of extruded aluminium sections with comparable dimensions. All specimens were tested in transverse loading and in torsion. A parametric study was carried out in order to optimize the stiffness of the GFRP-steel hybrid specimens. It was found that the modes of deformation exhibited by the GFRP-steel hybrid specimens were highly tunable. The hybrid members had sufficient bending stiffness when compared with equivalent extruded aluminium members; however, the technology will likely require further development to ensure adequate shear stiffness.

Rally cars are among the most technologically advanced and complex race cars, with intercooled forced induction, adaptive all wheel drive and high-feature engine management being standard features for open class racers in all major North American Rally series. This high level of technology and complexity places additional burden on the service crews and mechanics charged with the task of preparing and repairing the vehicles during the competition. As such, it is of great importance that the brief service stops (thirty minutes per FIA regulation 17.2.2 [2008]) be executed as efficiently as possible. In the pursuit of valuable seconds, rally mechanics have shown a great deal of ingenuity, creating tools and procedures which are unique to the sport. One such innovation is the peg-style jack stand.

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.