Feature recognition from CNC part programs for milling operations
Feature recognition from CNC part programs for milling operations
复制标题
DOI：
10.1007/s00170-013-5275-4
复制
发表时间：
2013-09
期刊：
影响因子：
--
通讯作者：
Xianzhi Zhang;Aydin Nassehi;S. Newman
中科院分区：
文献类型：
--
作者：
Xianzhi Zhang;Aydin Nassehi;S. Newman
自从基于特征的计算机辅助系统在生产中普遍使用以来，特征识别已经成为获取包含特定工程意义的特征的主要方法。在特征识别中，从底层元素中提取工程意义，并将其封装成特征，以便于工艺规划、制造和检测等各种工程任务。由于特征的不同分类和不同的应用领域，出现了许多不同的特征识别方法。这些特征识别方法通常基于来自计算机辅助设计系统的零件设计模型。提出了一种新的基于计算机数控加工零件程序的特征识别方法。该方法使用特征识别算法，通过分析刀具更换、主轴转速、进给速度、原材料、刀具几何形状和刀具路径来集成数控零件程序，以识别制造工艺方案。它对从车间提取工艺知识并将其表示为制造特征级数据的能力具有重大影响。本文主要研究二维特征的识别，但也可以扩展到更复杂的特征。通过对典型铣削特征的实例分析，验证了该方法的有效性。以两个样件为例说明了该方法的有效性和有效性。此外，将该方法与传统的特征识别方法进行了比较，并讨论了零件程序特征识别的具体问题。
Since the use of feature-based computer-aided systems became common in production, feature recognition has been a primary method to obtain features that contain specific engineering significance. In feature recognition, engineering significance is extracted from low-level elements and encapsulated into features to facilitate the various engineering tasks including process planning, manufacture and inspection. Due to the various classifications of features and their versatile application areas, there have been many different feature recognition approaches. These feature recognition methods are typically based on the part design models from computer-aided design systems. In this research, a new feature recognition method from computer numerical control (CNC) part programs for milling components is proposed. This approach uses feature recognition algorithms to integrate CNC part programs through the analysis of tool changes, spindle speeds, feed rates, raw material, tool geometry and tool paths to identify the manufacturing process plan. It has a major influence with the ability to extract process knowledge from the shop floor and represent it into a manufacturing feature-level data. This paper focuses on the recognition of 2½D features, but it can be extended to more complex features. Case studies are used to validate the use of the proposed method on typical milling features. Two sample parts are used to illustrate the efficacy and efficiency of the method. In addition, the proposed method is compared against traditional feature recognition techniques, and issues particular to feature recognition from part programs are discussed.