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This is the first of two papers evaluating the performance of general-purpose feature detection techniques for geometric models. In this paper, six different methods are described to identify sets of faces that bound depression and protrusion faces. Each algorithm has been implemented and tested on eight components from the National Design Repository. The algorithms studied include previously published general-purpose feature detection algorithms such as the single-face inner-loop and concavity techniques. Others are improvements to existing algorithms such as extensions of the two-dimensional convex hull method to handle curved faces as well as protrusions. Lastly, new algorithms based on the three-dimensional convex hull, minimum concave, visible and multiple-face inner-loop face sets are described. These algorithms provide a basis for the comparative analysis that is the subject of the second paper.

Abstract-Today, the manufacturing industry is under pressure to be able to rapidly come up with innovative designs and produce them in much shorter timeframes in order to keep up with growing customer demands and quickly gain new business. One of the ways used to achieve shorter time to markets for new product developments is by using design for manufacturing (DFM) methods to reduce time and energy going into resolving manufacturing based defects. It is also more vital in today's manufacturing industry to make use of DFM methods much earlier in the product development lifecycle in order to prevent potentially known quality defects from happening and save on costs associated with late design changes. This requires enabling a more systemic feedback cycle of production data for the creation of DFM knowledge repositories as well as overcoming some wider knowledge sharing barriers across the organization. This investigation focuses on how the communications of manufacturing knowledge from production data is affected by factors within the overall organization. The investigation is one of the few that consider the influence of organizational factors on the communication of engineering knowledge as well as the related knowledge sharing barriers. The project is carried out empirically with a large UK based aerospace manufacturing company by gathering data primarily from observations and interviews. This paper presents the findings followed by discussions for improving DFM Knowledge Management in the aerospace industry.

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