加工面点云数据深度学习的加工特征自动识别  

Automatic Recognition of Machining Features Based on Deep Learning of Machining Surface Point Cloud Data

作　　者：高玉龙 张应中[1] GAO Yu-Long;ZHANG Ying-Zhong(School of Mechanical Engineering,Dalian University of Technology,Dalian 116024,China)

机构地区：[1]大连理工大学机械工程学院,大连116024

出　　处：《计算机系统应用》2022年第2期143-149,共7页Computer Systems & Applications

基　　金：国家自然科学基金(51775081,51375069)。

摘　　要：加工特征识别是实现CAD/CAPP/CAM系统集成的关键技术.针对传统基于符号推理加工特征识别模式存在鲁棒性问题,提出一种基于加工面点云数据深度学习的加工特征自动识别方法;基于PointNet点云识别框架,构建了一个面向加工面点云数据的加工特征自动识别卷积神经网络;通过收集CAD模型中的加工特征面集和采样点云,构建了适合该网络学习的三维点云数据样本库.通过样本训练获得加工特征识别器,实现了24类机械加工特征的自动识别,识别准确率达到99%以上,该方法简洁、高效,对有噪音和缺陷的点云数据不敏感,并且对由于特征相交造成加工面破坏仍然具有较好的鲁棒性和识别效果.Machining feature recognition is the key technology to realize the integration of CAD/CAPP/CAM.To tackle the robustness problem of the traditional recognition pattern of machining features based on symbolic reasoning,this study proposes an automatic recognition method of machining features based on deep learning of machining surface point cloud data.Utilizing the PointNet point cloud recognition framework,the study constructs a convolutional neural network(CNN)for automatic recognition of machining features of machining surface point cloud data.By the collection of the machining surface sets from CAD models and sampling of them to form point cloud data,a three-dimensional point cloud data library is constructed which is suitable for the learning of the network framework.A recognizer of machining features can be obtained by the CNN network training,able to automatically recognize 24 kinds of machining features,with the accuracy being higher than 99%.The method is simple,efficient,and insensitive to the point cloud data with noise and defects.Furthermore,it has good robustness and recognition effect for the damage of machining surfaces caused by feature intersection.

关 键 词：加工特征 特征识别 三维点云 卷积神经网络 

分 类 号：TH16[机械工程—机械制造及自动化] TP391.41[自动化与计算机技术—计算机应用技术]