This paper describes an approach for reusing engineering design knowledge. Many previous design knowledge reuse systems focus exclusively on geometrical data, which is often not applicable in early design stages. The proposed methodology provides an integrated design knowledge reuse framework, bringing together elements of best practice reuse, design rationale capture and knowledge based support in a single coherent framework. Best practices are reused through the process model. rationale is supported by product information, which is retrieved through links to design process tasks. Knowledge based methods are supported by a common design data model, which serves as a single source of design data to support the design process. By using the design process as the basis for knowledge structuring and retrieval, it serves the dual purpose of design process capture and knowledge reuse: capturing and formalising the rationale that underpins the design process, and providing a framework through which design knowledge can be stored, retrieved and applied. The methodology has been tested with an industrial sponsor producing high vacuum pumps for the semiconductor industry.

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Understanding the needs and aspirations of a suitable range of users during the product design process is an extremely difficult task. Methods such as ethnographic studies can be used to gain a better understanding of users needs, but they are inherently time consuming and expensive. The time pressures that are evident in the work performed by design consultancies often make these techniques impractical. This paper contains a discussion about the use of 'personas', a method used by designers to overcome these issues. Personas are descriptive models of archetypal users derived from user research. The discussion focuses on two case studies, the first of which examines the use of personas in the car design process. The second examines the use of personas in the field of 'inclusive design', as demonstrated by the HADRIAN system. These case studies exemplify the benefits 'data rich' personas contribute as opposed to 'assumption based' personas.

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Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment.

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