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Parameter and tolerance design are key steps in ensuring development of robust products. When input-output relationship is available in the form of a transfer function, the parameter design and tolerance design are carried out using computer experiments. In this paper transfer function of a filter circuit is used to demonstrate an approach to computer-based robust engineering. In the parameter design stage, the problem of optimizing the filter circuit efficiency is formulated as a non-linear optimization problem using the transfer function. For tolerance design stage, loss function is used to select the optimum tolerance values for the filter circuit components. The results obtained show an improvement in robustness over a previous study that used a heuristic approach.

Lean Six Sigma is an approach that is gaining momentum both in manufacturing and service industries. Design for Lean Six Sigma (DFLSS) is an outgrowth of the DFSS and Lean Six Sigma approaches. The essence of DFLSS is to ensure design quality and predictability during the early design phases and the approach employs a structured integrated product development methodology and a comprehensive set of robust tools to drive product quality, innovation, faster time to market, and lower product costs. When it comes to automotive Product Development, applying lean principles and DFSS together becomes more of a challenge within the existing PD system. While the benefits of DFLSS present an attractive proposition in a fiercely competitive market it brings its own challenges as to how to deploy it for maximum benefits. This paper examines the challenges, potential and opportunities for DFLSS in the automotive industry and presents a vision for integrating it in to the Product Development System.

Metamodeling has been widely used in place of complex numerically intensive simulations to perform design reliability assessment and optimization. Due to cost and time constraints, most complex simulations can only afford a limited number of runs with a relatively large number of factors. The accuracy of a metamodel is affected by the degree of the underlying non-linearity, the sample size, the sampling strategy, and the type of the metamodel. In this study, the effect of the DOE optimality criteria on the accuracy of the Kriging metamodel is investigated under scarce sampling plans. Uniformity optimization is performed using some of the most popular uniformity measures, such as Centered Discrepancy (CL2), Maximin, and Entropy criteria. Case studies consist of eight analytical closed-form functions drawn mostly from real engineering applications with five to seven factors each and various degrees of non-linearity.

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.