Manufacturing Process Classification Based on Distance Rotationally Invariant Convolutions
Manufacturing Process Classification Based on Distance Rotationally Invariant Convolutions
复制标题
基于距离旋转不变卷积的制造过程分类
DOI：
10.1115/1.4056806
复制
发表时间：
2023
影响因子：
3.1
通讯作者：
Rosen, David
中科院分区：
文献类型：
--
作者：
Wang, Zhichao;Rosen, David
给定一个零件设计，制造工艺分类的任务是确定一个合适的制造工艺来制造它。我们之前的研究提出了一个用于制造过程分类的大型数据集，并将卷积神经网络（CNN）与三角网格的热核特征相结合，获得了准确的分类结果。本文构造了一种基于旋转不变形状描述子和神经网络的分类方法，该方法的分类精度优于以往的分类方法。该方法使用点云部件表示，与我们之前工作中使用的三角网格表示形成对比。第一步提取由点云中点间距离组成的旋转不变特征。然后，将提取的形状描述符输入到CNN中进行制造过程分类。此外，我们提供了两种可视化方法来解释神经网络的中间层。最后，对一些模糊实例进行了性能测试，结果表明该方法的性能与预期一致。在本文中，我们只考虑了形状信息，而忽略了非形状信息，如材料和公差。此外，本研究只考虑了需要一个制造过程的零件。我们的工作表明，零件形状属性本身就足以区分所考虑的不同制造工艺。
Given a part design, the task of manufacturing process classification identifies an appropriate manufacturing process to fabricate it. Our previous research proposed a large dataset for manufacturing process classification and achieved accurate classification results based on a combination of a convolutional neural network (CNN) and the heat kernel signature for triangle meshes. In this paper, we constructed a classification method based on rotation invariant shape descriptors and a neural network, and it achieved better accuracy than all previous methods. This method uses a point cloud part representation, in contrast to the triangle mesh representation used in our previous work. The first step extracted rotation invariant features consisting of a set of distances between points in the point cloud. Then, the extracted shape descriptors were fed into a CNN for the classification of manufacturing processes. In addition, we provide two visualization methods for interpreting the intermediate layers of the neural network. Last, the performance of the method was tested on some ambiguous examples and their performances were consistent with expectations. In this paper, we have considered only shape information, while non-shape information like materials and tolerances were ignored. Additionally, only parts that require one manufacturing process were considered in this research. Our work demonstrates that part shape attributes alone are adequate for discriminating between different manufacturing processes considered.