MFPointNet: A Point Cloud-Based Neural Network Using Selective Downsampling Layer for Machining Feature Recognition

Abstract

Machining feature recognition is a research hotspot in recent years. A point cloud is a geometry data representation format of three-dimensional (3D) models. The use of point cloud-based convolutional neural networks (CNNs) for machining feature recognition has received increasing research attention. However, these point cloud-based networks usually have large complexity size and training time. In this paper, a selective downsampling-based point neural network for machining feature recognition is proposed. Firstly, a machining feature dataset called MFDataset is constructed and contains 33 feature types. Secondly, a selective downsampling algorithm of the input points is presented, which drops out unimportant points while keeping the important ones. In single-machining feature recognition, MFPointNet is proposed by utilizing the selective downsampling of the input points. In multi-machining feature recognition, the segmentation part of the MFPointNet is adopted with the selective downsampling algorithm to segment and recognize multiple features. Compared with other point cloud-based networks, experimental results show that MFPointNet reduces the computational complexity without losing the recognition accuracy basically. MFPointNet is more robust to model complexity when more machining feature points are input to the network. Moreover, several intersecting feature models validate the segmentation performance of MFPointNet.