Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a methodology for efficiently recognizing both isolated and interacting features in a uniform way. The conventional, graph-based recognition method is combined with hint-based feature recognition to recognize and extract alternative interpretations of interacting features. First all isolated (non-intersecting) features are recognized based on a Manufacturing Face Adjacency Graph. Interacting features are then recognized based on the feature's minimal condition subgraph (MCSG) that is used as a feature hint. Unlike Previous hint-based recognition methods, the MCSGs of all features are defined, generated and completed in a uniform way, independent of the feature type. Hints are defined by an Extended Attributed Adjacency Graph, generated by graph decomposition and completed by adding virtual links, corresponding to entities lost by interactions. An efficient algorithm for generating virtual links is developed. A new classification of feature interactions is also presented.
['Shiming Gao', 'Jami J. Shah']
I am writing a research paper on a idea Automatic recognition of interacting machining features based on minimal condition subgraph
[{"from": "human", "value": "I am writing a research paper on a idea Automatic recognition of interacting machining features based on minimal condition subgraph"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a methodology for efficiently recognizing both isolated and interacting features in a uniform way. The conventional, graph-based recognition method is combined with hint-based feature recognition to recognize and extract alternative interpretations of interacting features. First all isolated (non-intersecting) features are recognized based on a Manufacturing Face Adjacency Graph. Interacting features are then recognized based on the feature's minimal condition subgraph (MCSG) that is used as a feature hint. Unlike Previous hint-based recognition methods, the MCSGs of all features are defined, generated and completed in a uniform way, independent of the feature type. Hints are defined by an Extended Attributed Adjacency Graph, generated by graph decomposition and completed by adding virtual links, corresponding to entities lost by interactions. An efficient algorithm for generating virtual links is developed. A new classification of feature interactions is also presented."}]