Intelligent processing feature identification method based on point cloud semantic segmentation

Abstract

The invention discloses an intelligent processing feature recognition method based on point cloud semantic segmentation, and the method comprises the steps: converting a machining feature intelligentrecognition problem of a part CAD model into a semantic segmentation problem of a three-dimensional point cloud model, building a three-dimensional part CAD model library, and carrying out the preprocessing of the three-dimensional part CAD model library, and obtaining a training data set; furthermore, inputting the data set into an improved PointNet semantic segmentation network, enabling the network to take the PointNet semantic segmentation network as a basic framework, and improving the segmentation precision by introducing a residual block structure of a ResNet network; and finally, inputting the various machining features into a detection module, verifying the machining feature types through abnormal point detection and a DBSCAN clustering algorithm, and determining the number of themachining features. According to the intelligent process feature identification method, cross-part type and cross-feature type machining feature extraction is achieved, the problem that special-shaped machining features, free-form surfaces and composite machining features are difficult to recognize is solved, and system integration of full-life-cycle information of enterprise reuse machining features and manufacturing industry computer-aided software is facilitated.

Description

technical field [0001] The invention belongs to the field of intelligent information technology of advanced manufacturing technology, and in particular relates to an intelligent recognition method of processing features based on point cloud semantic segmentation. Background technique [0002] CAD, CAPP and CAM systems play an important role in the fields of product design, process planning and processing simulation. However, data between systems is difficult to convert, transfer and share. Therefore, it is very important to realize the integration of CAD, CAPP and CAM systems. This will reduce the design and manufacturing time of small batch-customized parts products, and enable enterprises to have stronger agile manufacturing capabilities in a highly competitive market. Machining features refer to specific shapes on mechanical parts, including holes, free-form surfaces, steps and keyways, etc. The automatic recognition technology of machining features has become a bridge fo...

AI Technical Summary

Problems solved by technology

[0003] Over the past three decades, researchers have proposed different types of feature recognition techniques to identify processing features, including graph-based methods, neural network-based methods, volumetric decomposition-based methods, cell-based methods, hint-based method, rule-based method and mixed-based method, these methods generally need to establish a large number of complex rules, and at the same time, the method has poor versatility in the face of different types of processing features. For the identification of compound processing features, special-shaped processing features and free-form surface features Difficulties exist; recently, a method that takes three-dimensional voxels as input and uses 3D-CNN to identify processing features, but only achieves certain results in the identification of single features and simple part models

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