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The process for accurately estimating product reliability early in the development process can be a difficult and costly task. Traditional methods like Reliability Prediction Models and Life Testing Strategies yield beneficial results when relative information is known about the product that is to be analyzed. When there is minimal information known (prior failure rates…) such a new concept design these above reliability methods have limitations. For these cases a Virtual Testing Strategies have proven to yield valuable results. This paper will demonstrate a reliability analysis procedure for a new automotive concept design. This analysis procedure composes of a mathematical model, model validation, parameter diagram, design of experiment (DOE), response surface, and optimization.

The purpose of this paper is to present a common sense practical method for establishing and demonstrating reliability objectives. In particular, this paper will: describe an operational definition of “useful life”, describe an accelerated laboratory test procedure for demonstrating that products meet the useful life objective, and describe a method for demonstrating correlation between customer usage and laboratory testing.

The era of the vehicle as a standalone non-connected entity is rapidly drawing to a close. The “Box” on four wheels is being opened to multiple communication channels based on consumer demand and expectations of the latest features, functions and content. Concurrently, automotive planners, marketers, engineers, and system architects are subject to increased complexity as a result of the rapidly evolving consumer electronics industry. This paper will discuss many of the limitations of existing automotive business models in attempting to successfully implement and meet consumer expectations. It will also propose a structural framework for automotive planners, marketers, engineers, and system architects to be able to better understand and predict consumer acceptance of electronics, multimedia features, and in-vehicle content access.

This paper describes a front road wheel steer-by-wire system with two actuator motors on the rack and pinion assembly to move the road wheels. Dual actuators are used to provide actuator redundancy and to enhance the fault tolerance capability. When one actuator faults or fails, the other actuator is designed to work independently and maintain full system performance. The paper emphasizes control method to implement the motion control for the front road wheel steer-by-wire system with two actuators on the common load. The proposed dual servo synchronization motion control implements the angle tracking for the road wheel reference input by controlling two actuators synchronously and cooperatively. It includes two servo feedback control loops to track the common reference input. The angular position error between two feedback loops is compensated using a synchronized compensator.

A fixed-base driving simulator with a 14-degree of freedom vehicle dynamics model was used to compare the lane tracking performance of test subjects using a joystick steering controller to that using a conventional steering wheel. Three driving situations were studied: a) straight-line highway driving, b) winding road driving (country road), and c) evasive maneuvering - a double lane change event. In addition, three different joystick force-feedback settings were evaluated: i) linear force feedback, ii) non-linear, speed sensitive force feedback and iii) no force feedback. A conventional steering wheel with typical passenger car force feedback tuning was used for all of the driving events for comparison.

Although not completely understood, rotor distortion due to bolt-up is an issue commonly found in conventional brake rotor design. In this paper, bolt-up is addressed by utilizing optimization and contact analysis methods. These methods give greater insight to the contributing factors that influence bolt-up distortion. In particular, the optimization method defines the approximate geometric shape required for a brake rotor based on optimizing one or more variables. By utilizing the non-linear contact analysis method, the results from the optimization analysis are validated. In general, the results show that bolt-up distortion is not significantly affected by changing design features, variables or combinations of design features and variables. However, significant improvement in bolt-up distortion is noticed when changes are made to the brake rotor and the wheel bearing hub.

Modern automobiles utilize stabilizer bars to increase vehicle roll stiffness. Stabilizer bars are laterally mounted torsional springs which resist vertical displacement of the wheels relative to one another. A stabilizer bar is constructed in such a way that it will meet package constraints and fatigue requirements. In order to design a robust stabilizer bar, Taguchi's “Design of Experiment method” is used. The objective of this paper is to develop a robust stabilizer bar design that will maximize the fatigue life and the roll stiffness while minimizing weight. This study is based on results obtained by CAE analysis.

Software is today one of the most important components of electronic products. The capture and validation of the requirements makes a difference if the product will fulfill the customer's expectations or generate enormous frustration. The correct implementation of software validation makes the Product Development Organization more mature and reliable. Software validation is an opportunity for the product development team to identify if the requirements and customer expectations were achieved. It is also used to identify the risks and possible improvements to the product. Software testing is one element of a brooder topic that is often referred to as verification and validation (V&V). Verification refers to the set of activities that ensure that software correctly implements a specific function. Validation refers to a different set of activities that ensure that the software that has been built is traceable to customer requirements.

Reliability allocation of system objectives for Reliability validation purposes must account for Confidence levels. Misallocating Confidence levels can lead to unrealistic and unmanageable objectives, resulting in increased development times and associated costs. Therefore, it is necessary to correctly model both Reliability and Confidence levels. Unfortunately, modeling for anything more complex than the simplest pass/fail test criteria can become quite complex in a multi-component system. The easiest case to model is time-censored testing with no failures. But time-censored testing with no failures is just a small subset of all viable validation strategies. Given that the validation strategy for each component can be different, trying to isolate a single one-size-fits-all model is extremely difficult. For these complex scenarios, computer simulation provides the best approach to calculating true system performance.