Robust product development by combining engineering design and designed experiments

Many successful industrial companies aim at improving product development in order to be competitive. This thesis is intended to make a contribution to the work of fulfilling this goal. Rapid advances in technology in recent years have set new demands on product development. As a consequence, an increasing variety of products are built on heterogeneous technologies. Such products incorporate a mixture of technologies, often combinations of computer, electrical, and mechanical systems. Specialists from different engineering disciplines must co-operate to a greater extent than before in order to understand the products. Increased cooperation and heterogeneous technologies in products set high demands on communication and systems integration if product development is to deliver products with high quality, short lead times, and low cost. This thesis presents research that advocates tools and methods for performancerelated robustness improvements in product development. Robust products, or subsystems, are insensitive to disturbances and perform well under a wide range of conditions. It is found that robustness in product development increases multidisciplinary optimization, communication, and systems integration. Thus, robustness provides a solution to a number of important questions relating to systems integration and communication in product development. The presented research consists of both theory development and practical implementations. The research in the appended papers provides: (1) a tool for customizing and designing company unique strategies; (2) an approach to problem solving and quality-related performance improvements by combining design object analysis with Axiomatic Design, Quality Control tools, and designed experiments; (3) equations for computing the Information content in decoupled design solutions; (4) equations for evaluating Quality Loss in Axiomatic Design; (5) a feature selection technique to improve robust classification in the multivariate domain; (6) a comparative study of two experimental methods for robustness improvements. Two of the presented studies have been carried out in industry and the results have been implemented successfully. The remaining four studies were performed in an academic environment, although the results and simulations indicate industrial relevance when applied to real-life problems.

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