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To warrant the substitution of traditionally used structural automotive materials with titanium alloys, the material substitutional and redesign advantages must be attainable at a justifiable cost. Typically, during material replacement with such ‘exotic’ aerospace alloys, the initial raw material cost is high; therefore, cost justification will need to be realized from a life-cycle cost standpoint. Part I of this paper highlighted the redesign, fabrication, and validation of an automotive component. Part II details the particulars of constructing the total life-cycle cost model for both prototypes (P1, P2). Considerations in the model include adaptation to a high volume production scenario, availability of near-net size plate/bar stock, etc. Further, response surfaces of fuel costs savings and consequent life-cycle costs (state-variables) are generated against life-cycle duration and unit fuel price (design-variables) to identify profitable operating conditions.

Due to titanium's excellent strength-to-weight ratio and high corrosion resistance, titanium and its alloys have great potential to reduce energy usage in vehicles through a reduction in vehicle mass. The mass of a road vehicle is directly related to its energy consumption through inertial requirements and tire rolling resistance losses. However, when considering the manufacture of titanium automotive components, the machinability is poor, thus increasing processing cost through a trade-off between extended cycle time (labor cost) or increased tool wear (tooling cost). This fact has classified titanium as a “difficult-to-machine” material and consequently, titanium has been traditionally used for application areas having a comparatively higher end product cost such as in aerospace applications, the automotive racing segment, etc., as opposed to the consumer automotive segment.

This paper deals with stability of motion and its criteria for tracking control of intelligent vehicle systems. It deals with general control structure and specification of an optimum range of predefined control parameters for accurate tracking of these vehicle systems. A two degree of freedom (DOF) nonlinear dynamic model is developed to represent their plane motion. This model is further utilized in deriving a linear model that is used to do this stability analysis. Path tracking of the vehicle is attained by controlling the position and orientation errors about a predefined trajectory, which is accomplished by modifying the steering input signal on the basis of error feedbacks to the controller.