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首页> 外文期刊>Journal of Advanced Mechanical Design, Systems, and Manufacturing >Development of model identification methodology based on form recognition for Computer-Aided Process Planning
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Development of model identification methodology based on form recognition for Computer-Aided Process Planning

机译：基于表单识别的模型识别方法在计算机辅助过程规划中的发展

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Computer-Aided Process Planning (CAPP) systems have become essential in manufacturing environments to integrate the information between CAD and CAM systems, and to automatically generate the NC code from the CAD model. Though the future of these systems seems to belong to the use of Artificial Intelligence to create knowledge-based algorithms which emulate human decisions, the CAPP systems based on feature recognition and model matching, which use databases of previously known mechanical components to generate new process plans, are also a very interesting option due to their accuracy and smaller development costs. Many researchers have proposed different kind of feature recognition algorithms before. However, these algorithms are usually application-dependent and require external codes to identify the features of wireframe models. This paper proposes a new methodology for shape recognition and model matching stages which improves the accuracy of the recognition tasks, uses solid models instead of wireframe models and can be successfully applied to any kind of part. The methodology is based on an original coding system that links the geometric information extracted from the CAD model with the features of the part by means of an identification sequence which is detailed in the text. Also, a score system has been created for the model matching stage. The obtained results show that the system presents high accuracy in shape recognition, feature identification and model matching tasks, even when the analyzed part is similar to the ones in the database. In addition, quantitative geometric data is also extracted from the CAD model on behalf of future steps of the CAPP system, such as the NC code generation stage. In contrast to other systems, this methodology can be easily applied to the industry since it makes use of the CAD model only.
机译：在制造环境中，计算机辅助过程计划（CAPP）系统对于集成CAD和CAM系统之间的信息以及从CAD模型自动生成NC代码已变得至关重要。尽管这些系统的未来似乎属于使用人工智能来创建基于知识的算法来模拟人的决策，但是基于特征识别和模型匹配的CAPP系统使用以前已知的机械组件的数据库来生成新的工艺计划由于它们的准确性和较小的开发成本，它们也是一个非常有趣的选择。以前，许多研究人员提出了不同种类的特征识别算法。但是，这些算法通常取决于应用程序，并且需要外部代码来识别线框模型的特征。本文提出了一种用于形状识别和模型匹配阶段的新方法，该方法可以提高识别任务的准确性，使用实体模型代替线框模型，并且可以成功地应用于任何零件。该方法基于原始的编码系统，该系统通过识别序列将从CAD模型提取的几何信息与零件的特征联系起来，该识别序列在本文中有详细介绍。此外，已经为模型匹配阶段创建了一个评分系统。所获得的结果表明，即使分析的部分与数据库中的相似，该系统在形状识别，特征识别和模型匹配任务方面也具有较高的准确性。另外，还代表CAPP系统的未来步骤（例如NC代码生成阶段）从CAD模型中提取了定量几何数据。与其他系统相比，此方法仅可使用CAD模型，因此可轻松应用于行业。

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