Automatic formation of rules for feature recognition in solid models
This paper discusses the application of inductive learning techniques for creation of rule sets that could be utilised for Automatic Feature Recognition (AFR) in 3D solid models. AFR techniques are an important tool for achieving a true integration of Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) processes. In particular, AFR systems allow the identification in CAD models of high-level geometrical entities, features that are semantically significant for manufacturing operations. In this paper, a method is proposed to meet the specific requirements imposed by the utilisation of inductive learning for acquisition of feature recognition rules. The method presented in this study is implemented within a prototype feature recognition system and its capabilities are verified on a benchmarking part.
Thank you for your interest. The RULES-5 inductive learning algorithm was chosen for the following reasons:
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The models created have the form of rule sets.
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The source code as well as the developer of the algorithm were available resources.
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At the time of the study, RULES-5 was the latest algorithm of the RULES family of inductive learning algorithm.
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Its performance against other inductive learning algorithms were demonstrated in a separate study.
Emmanuel Brousseau
An interesting presentation, are there any particular features/capabilities of the RULES-5 algorithm that made you choose it over other inductive learning algorithms?