【摘要】 针对传统的基于规则推理的特征识别算法灵活性低及基于卷积神经网络的三维模型特征识别算法计算量大的问题，提出一种基于深度图像的加工特征识别算法。该算法通过提取三维几何模型的深度图像，使用基于深度学习的目标检测算法获取二维深度图像中的加工特征信息，利用NX/Open API和NVIDIA推出的通用并行计算架构在UG NX12.0平台上开发了基于该方法的原型系统，在由数千个注射模电极零件构成的模型库中，随机选取部分零件进行验证。结果表明，针对电极零件的典型加工特征，识别正确率达到90%以上，该算法提高了加工特征识别的灵活性与效率。更多还原

【Abstract】 Aiming at the problems of low flexibility of the traditional feature recognition algorithm based on rule reasoning and large computation of the 3D model feature recognition algorithm based on convolution neural network, a processing feature recognition algorithm based on depth image was proposed. The algorithm extracted the depth image of the 3D geometric model, and used the target detection algorithm based on depth learning to obtain the processing feature information in the 2D depth image. A prototype system based on this method was developed on the UG NX12.0 platform using the NX/Open API and the compute unified device architecture launched by NVIDIA. Some parts were randomly selected from the model library composed of thousands of injection mould electrode parts for verification. The results showed that the recognition accuracy of typical machining features of electrode parts could reach more than 90%, and the algorithm effectively improved the flexibility and efficiency of machining feature recognition.更多还原