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As new technology is introduced into automotive engineering, the level of uncertainty regarding system robustness increases. With it reliability assessment tools that account for such uncertainty is expected to gain increased attention. This can naturally lead to Bayesian-based tools. This paper examines three reliability assessment methodologies that operate in the Bayesian framework. Two of them are geared towards electronic parts and assemblies, with the remaining one being geared towards systems in general. In doing so, they were critiqued in terms of four dimensions: (1) basic architecture, (2) input factors, (3) handling of qualitative data, and (4) failure rate updating mechanisms.

Meeting reliability requirements specified by customers, solely by physical testing can result in serious resource implications. Demonstration of a high reliability at a high statistical confidence level requires a large number of test samples and lengthy test cycles. In this paper we intend to present our reliability management methodology that accounts for both physical testing and non-physical testing activities. In particular, we will focus on how three important product dimensions - namely, function, physical structure, and time-in-service requirements - are systematically examined and tied to validation test planning. The ultimate goal here is intelligent information management.

The paper focuses on various important decisions of verification and testing plans of the product during its design and production stages. In most of the product and process development projects, decisions on verification and testing are ad-hoc or based on traditions. Such decisions never guarantee the performance of the product as planned, during its whole life cycle. We propose an analytical approach to provide the concrete base for such crucial decisions of verification planning. Accordingly, a mathematical model is presented. Also, a case study of an automotive Electro-mechanical product is included to illustrate the application of the model.

This paper introduces the Life Cycle Cost (LCC) optimization model, where LCC is expressed as a function of controllable design parameters. The LCC model is enhanced with the novel concept of considering the target value of the functional characteristic as a decision variable so that it is optimized on the basis of life-cycle considerations. Most of the LCC model in literature considers only one objective at a time. This paper proposes a comprehensive model, which is capable of considering multiple objectives simultaneously. This model, is solved with the help of Goal Programming.

In this paper, a simplified and systematic approach to integrate reliability and durability aspects in design process is presented. A six step process is explained with the help of examples. Two alternatives for gathering means and standard deviations for key parameters are discussed. First a DOE approach based on orthogonal arrays is presented. Second approach is based on Taylor Series expansion. An example of beam design is solved with both of these approaches. The Second example also considers the degradation with time in service.