关键词: 全息信息, 深度学习, 图像化处理, 特征识别, 过滤式提取

Abstract: Aiming at the information integration problems for machining features of various part models based on model definition（MBD）， a holographic information extraction method of machining features was proposed based on multi-level extraction architecture. Through the analysis of structural characteristics of parts， the machining features were classified with the simplest features that had manufacturing semantics and could not be split. Based on elaborating the extraction strategy， a machining feature classifier was built by deep learning image recognition technology. Based on the characteristics of MBD model information annotation， the machining feature topology structure was quickly located and extracted. A multi-view capture dimensionality reduction method was used to make the machining feature color image. And then a comprehensive analysis method for multi-angle image recognition of machining features was designed. Based on the query views， the annotation information of the MBD models was filtered， and a double-layer filtering extraction method for machining feature geometric information was constructed. Finally， a holographic information extraction software for machining features was established， and experimental results of key parts of marine diesel engines show the effectiveness of the method. 

Key words:  , holographic information, deep learning, image processing, feature recognition, filter extraction