Because parts are manufactured with a variety of processes, we would like feature recognizers that can support planning within these various manufacturing domains. These manufacturing domains often share common properties, such as similar tools and process capabilities. This raises the question of whether or not feature recognizers can be designed to take advantage of the similarities among these manufacturing domains. In particular, I explore the possibility of designing composable feature recognizers that can be created by using and/or adapting components from existing feature recognizers for related domains. This approach provides an alternative to other approaches including 1) developing a single, large feature recognizer for several domains and 2) developing a new feature recognizer from scratch for each domain. Composable feature recognizers will be smaller and easier to write than the feature recognizers from the first approach since they will be tailored for a smaller family of domains. However, since they will share components with related feature recognizers they will be less redundant and easier to maintain than those from the second approach. In this paper, I investigate a tool-centric approach to the design of composable feature recognizers in which knowledge and reasoning algorithms are structured around the manufacturing equipment. Equipment Module Libraries are developed consisting of manufacturing equipment with associated knowledge and reasoning algorithms. New feature recognizers are constructed by selecting and composing the relevant equipment modules.
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