Jeongin Koo , Soohyun Nam , Hoon-Hee Lee , Dong Yoon Lee
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引用次数: 0
摘要
制造业的数字化转型带来了海量数据,有效的数据利用和分析成为获得竞争优势的关键。然而,制造业数据格式多样、结构复杂,给数据集成和互操作性带来了巨大挑战。本文介绍了基于 ISO 14649 标准衍生的通用流程规划数据模型的智能加工应用数字线程框架。该框架集成了工艺规划阶段使用的各种数据,并实现了加工过程各阶段生成的数据的上下文连接,包括虚拟加工、加工监控以及几何尺寸和公差(GD&T)。通用数据模型是通过将 ISO 14649-1/11 中的 EXPRESS 数据模型解析为 OpenAPI Specification JSON 格式并生成各种编程语言的类而构建的。数字线程的重点是连接和恢复运行数据的上下文,扩展 ISO 14649 数据模型,为各种应用纳入工具和设备信息。监控数据与虚拟加工数据同步,监控参考信息映射到数字线程项目数据。通过参考颤振应用,利用稳定叶图(SLD)、机床和虚拟加工数据中的参数,展示了所提框架的有效性。该框架通过将加工过程各阶段生成的数据进行上下文连接,促进了数据分析和利用,最终支持了基于监控数据的智能应用开发。
The Digital Thread Framework for Implementing Intelligent Machining Applications
The digital transformation of manufacturing industry has led to vast amounts of data, making effective data utilization and analysis crucial for gaining a competitive advantage. However, the diverse formats and complex structures of manufacturing data pose significant challenges to data integration and interoperability. This paper presents a digital thread framework for intelligent machining applications based on a common process planning data model derived from the ISO 14649 standard. The framework integrates various data used in the process planning stage and enables contextual connection of data generated at each stage of the machining process, including virtual machining, machining monitoring, and geometric dimensioning and tolerancing (GD&T). The common data model is constructed by parsing the EXPRESS data model from ISO 14649-1/11 into an OpenAPI Specification JSON format and generating classes in individual programming languages. The digital thread focuses on connecting and restoring the context of operation data, extending the ISO 14649 data model to incorporate tool and equipment information for various applications. The monitoring data is synchronized with the virtual machining data, and the monitoring reference information is mapped to the digital thread project data. The effectiveness of the proposed framework is demonstrated through a reference chattering application, which utilizes parameters from the stability lobe diagram (SLD), machine tool, and virtual machining data. The framework facilitates data analysis and utilization by contextually connecting data generated at each stage of the machining process, ultimately supporting the development of intelligent applications based on monitoring data.