Titlebook: Computational Intelligence Techniques for New Product Design; Kit Yan Chan,C.K. Kwong,Tharam S. Dillon Conference proceedings 2012 Springe

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A Nonlinear Fuzzy Regression for Developing Manufacturing Process Models,oved and their production cost can be reduced. To address these concerns, one common method is to develop manufacturing process models that relate the quality requirements of a new product to the variables of manufacturing processes. Based on the models, proper settings of process parameters and the

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Rule Extraction from Experimental Data for Manufacturing Process Design,perimental data sets must first be processed and/or analyzed in order to extract patterns, useful information or knowledge. The development of effective and efficient methods for deriving knowledge from these data is important as the knowledge extracted from the data not only has to have a high pred

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Conclusion and Future Work,e and understanding ‘consumer needs’, while engineering personnel may be concerned only with ascertaining whether the engineering requirements can be met satisfactorily. Product designers are concerned with the product characteristics and appearance of the new product while manufacturing personnel a

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Computational Intelligence Techniques for New Product Design978-3-642-27476-3Series ISSN 1860-949X Series E-ISSN 1860-9503

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https://doi.org/10.1057/9781137347312p between each individual customer requirement and design attributes of a product is available, the product development teams can maximize the overall customer satisfaction with the new products by optimizing their design attribute settings.

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