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Today's automotive and electronics technologies are evolving so rapidly that educators and industry are both challenged to re-educate the technological workforce in the new area before they are replaced with yet another generation. In early November 2009 Ford's Product Development senior management formally approved a proposal by the University of Detroit Mercy to transform 125 of Ford's “IC Engine Automotive Engineers” into “Advanced Electric Vehicle Automotive Engineers.” Two months later, the first course of the Advanced Electric Vehicle Program began in Dearborn. UDM's response to Ford's needs (and those of other OEM's and suppliers) was not only at the rate of “academic light speed,” but it involved direct collaboration of Ford's electric vehicle leaders and subject matter experts and the UDM AEV Program faculty.

Current trends in the auto industry requiring tighter dimensional specifications combined with the use of lightweight materials, such as aluminum, are a challenge for the traditional manufacturing processes. The hemming process, a sheet metal bending operation used in the manufacturing of car doors and hoods, poses problems meeting tighter dimensional tolerances. Hemming is the final operation that is used to fasten the outer panel with the inner panel by folding the outer panel over the inner panel. Roll in/out is one of the main quality concerns with hemming, and keeping it under tolerance is a high priority issue for the auto manufacturers. Current hemming process technology, given the mechanical properties of current materials, has reached its saturation limit to deliver consistent dimensional quality to satisfy customers and at the same time meet government standards.

The feeddrive of a CNC machine is a mechatronic system consisting of motor, mechanical transmission devices and worktable. The accuracy and precision of the feeddrive system determines the quality of the final part. Hence close control of the drive system is very important. Accurate modeling of this system and its simulation helps in system design and parametric optimization. In this paper, bond graph based approach has been used to model the feeddrive of a CNC machine. This technique is based on power flow and uses basic component types to develop models for complex multi-physics systems. The governing equations can be algorithmically derived from a visual representation of the system. In this model all linear and nonlinear effects such as backlash, stick-slip friction and cutting forces have been accounted for. The model is also enhanced through the addition of a control loop for position and velocity.

When an electric motor is switched off, an amount of energy, dependent on the design of the motor, can be emitted to the power device. Some of this energy is stored in the capacitors and needs to be discharged when the circuit is shut off. Also, the current through the inductive components changes rapidly, causing a large voltage to be discharged from the circuit. This phenomenon poses potential problems in automotive applications since this energy surge, known generally as a conducted transient, could cause damage to other electrical devices that share the same power grid. Automotive motors generally also have radio frequency interference filtering devices, which are significant contributors to this conducted transient energy.

One of the main objectives of the automotive industry is to build more fuel efficient cars. The most dominant factor, among the many that determine fuel efficiency, is the weight of the vehicle. Any overall attempt to reduce weight involves all areas of the vehicle. Over the years the industry has addressed this need by developing new or modified materials and innovative production processes, combining these with one another and transforming them into viable production solutions. A very successful approach among the many things done is the use of structural metal adhesives, which have brought significant improvements in body shell rigidity and crash behavior. Traditionally, aluminum, steel and other metal parts were joined together with mechanical or thermal methods, such as rivets or resistance welding. But, structural adhesive is now an alternative that engineers consider very seriously. With any adhesive joint, the goal is to achieve as uniform a stress distribution as possible.

Mechatronic systems such as the anti-lock braking system, the powertrain control system, and even domestic items such as a microwave oven are designed such that there is a seamless interaction between the electronic components and the mechanical components that make up these systems. Use of software tools in simulating the behavior of such systems is an important step in their product development cycle. Since the success of a mechatronic product depends heavily on the synergistic interaction of all its components, it is imperative that the software tool used in its design be capable of addressing its multi-domain needs. There are several commercial software tools that are currently available for modeling and simulation of mechatronic systems. Adequacy of all these tools is not the same. Some are adept as a mechanical simulator but do a poorer job of electronic simulation. Others are just the opposite.

Applications of micro-electromechanical systems (MEMS) in automobiles are fairly recent. The two most common examples of MEMS use in automobiles are in crash sensing for airbag deployment, and in manifold absolute pressure sensing. There are, however, several other areas where MEMS devices are expected to replace more traditional technologies within the next few years. MEMS devices/systems (e.g. sensors and actuators) have several vital advantages over more traditional technologies. Because of highly reliable batch processing techniques, large volumes of highly uniform devices can be produced at relatively low unit cost. Since MEMS have virtually no moving parts to wear out, they are extremely reliable and long lasting. With the advent of microprocessor compatibility imposed on many automotive sensor/actuator applications, silicon based MEMS sensors will have a very efficient interaction with the controlling microprocessors.