Successfully fulfilling customers’ needs with world class products whilst remaining competitive and profitable are a major driver for the aerospace industry. The 21st Century is placing ever increasing pressure upon manufacturers to deliver high complexity, technologically enabled products to instantaneously fulfill a desired purpose at the point of use. To meet such stringent criteria, companies must find ways to continuously improve, reduce waste and accelerate the product development process whilst innovating.

This paper presents a multiple case study approach of turbine blade manufacturing part families which has been used to further develop a manufacturing knowledge reuse method that is being developed in partnership with a high tech aerospace company for application within a PLM environment. This method is currently being explored within the company so as to accelerate the design-make process to enable earlier availability of, and easier access to, manufacturing knowledge, thus bringing about better product performance.

The contents of the paper presents a methodical approach to the study of a number of products in an effort to ascertain how the complex interrelationships between design knowledge and manufacturing knowledge change across part families and, consequently, how they affect a developed feature knowledge relationship structure (FKRS) that maps design, manufacture and inspection viewpoints of product knowledge. Utilizing the FKRS, a pragmatic way has been developed in which people-to-people knowledge can be captured and shared to facilitate a reduction in the associated lead-time for information and knowledge retrieval and reuse. For this to be more widely applicable to different types of turbine blade it is necessary to widen the scope of the research. Four case studies are presented showing the aspects that constitute a part family and how knowledge varies across the products being studied. The FKRS is applied to the captured manufacturing knowledge in an effort to prove that it can represent and model multi-context knowledge across part families.

The results have shown that the approach provides a basis for the representation of complex relationship viewpoints for product features and is valid for a number of manufacturing part families.

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