부산대학교 도서관

In machining feature recognition, geometric elements generated in a three-dimensional computer-aided design model are identified. This technique is used in manufacturability evaluation, process planning, and tool path generation. Here, we propose a method of recognizing 16 types of machining features using descriptors, often used in shape-based part retrieval studies. The base face is selected for each feature type, and descriptors express the base face’s minimum, maximum, and equal conditions. Furthermore, the similarity in the three conditions between the descriptors extracted from the target face and those from the base face is calculated. If the similarity is greater than or equal to the threshold, the target face is determined as the base face of the feature. Machining feature recognition tests were conducted for two test cases using the proposed method, and all machining features included in the test cases were successfully recognized. Moreover, we have compared the proposed method with the latest artificial neural network for test cases 3 and 4. As a result, the proposed method demonstrated a significantly higher recognition performance, with F1 scores of 0.94 and 1.0 for test cases 3 and 4, respectively, compared to the latest artificial neural networks (each with F1 scores of 0.86 and 0.49).