Machining feature recognition using descriptors with range constraints for mechanical 3D models

TL;DR

This paper presents a shape-based method for recognizing 16 types of machining features in 3D CAD models using descriptors with range constraints, improving recognition accuracy over neural network approaches.

Contribution

Introduces a novel descriptor-based technique with range constraints for accurate recognition of machining features in 3D models, outperforming neural network methods.

Findings

Successfully recognized all features in test cases

Demonstrated better performance than neural network methods

Applicable to manufacturability and process planning

Abstract

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. Also, it was confirmed through an additional test that the proposed method in this study showed better feature recognition performance than the latest artificial neural network.