A hybrid method for recognizing interacting machining features

Abstract

Recognizing interacting features from a design part is a major challenge in the feature recognition problem. It is difficult to solve this problem using a single reasoning approach or artificial intelligence technique. A hybrid method, which is based on feature hints, graph theory and an artificial neural network - ART 2 net - has been proposed to recognize interacting machining features. Through enhancing the concepts of feature hints and graph representation schemes, which were presented in previous work to facilitate the extraction process of interacting features and reduce the searching space of recognition algorithms, a novel set of representations and methodologies to define generic feature hints (F-Loops), the interacting relationships between F-Loops and graph manipulations for F-Loops are developed to deduce potential features with various interacting relationships in a unified way. The obtained potential features are represented as F-Loop Graphs (FLGs), and these FLGs are input into an ART 2 neural network to be classified into different types of features eventually. The advantages of employing the ART 2 network are highlighted through comparing the computational results with another type of neural network, which is commonly utilized in the feature recognition domain. Case studies with complex interacting features show that the developed hybrid method can achieve optimal efficiency by benefiting from the diverse capabilities of the three techniques in the different phases of the recognition approach.