Transactions of Materials and Heat Treatment

Title：3D countersink hole feature recognition and key parameter extraction based on convolutional neural network

Authors： Shen Dawei Xiang Hua Zhuang Xincun Zhao Zhen

Unit： Institute of Forming Technology & Equipment Shanghai Jiao Tong University National Engineering Research Center of Die & Mold CAD Shanghai Jiao Tong University

KeyWords： countersink hole voxelization convolutional neural network feature recognition parameter extraction

ClassificationCode：TG386

year,vol(issue):pagenumber：2022,47(11):78-86

Abstract：

Process feature recognition and key parameter extraction of fine blanking parts are the key point to realize intelligent process design for fine blanking. Therefore, for the typical fine blanking feature of countersink hole, a model for feature recognition and parameter extraction was constructed with 3D CAD model as input. Then, using the improved adaptive voxelization algorithm, the CAD model of countersink hole generated in batches based on parameter-driven was converted into a voxelized model, and a data set of model sample was established. Furthermore, the two-step method was used by using the voxelization model of process features as input to establish a countersink hole feature recognition model based on 3D convolutional neural network and using the center cross-section image of countersink hole as input to establish a parameter extraction model based on 2D convolutional neural network, respectively, and the classification recognition and parameter extraction for three main types of countersink hole features were realized in turn. The results show that after verification and evaluation, the established model has high accuracy for the recognition of countersink hole feature types and the extraction of key parameters, which can provide strong support for the intelligent process design of fine blanking process.

Funds：

国家自然科学基金资助项目(51875351)

作者简介：沈大为（1998-），男，硕士研究生，E-mail：shdw0120@sjtu.edu.cn；通信作者：赵震（1972-），男，博士，教授，E-mail：zzhao@sjtu.edu.cn

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