在认知生产中使用 OPC UA、关联数据和 GAIA-X 实现联合学习服务

Q2 Engineering Journal of Machine Engineering Pub Date : 2024-05-19 DOI:10.36897/jme/188618
Christian Friedrich, Stefan Vogt, Franziska Rudolph, Paul Patolla, Jossy Milagros Grützmann, Orlando Hohmeier, Martin Richter, Ken Wenzel, Dirk Reichelt, Steffen Ihlenfeldt
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引用次数: 0

摘要

生产中的价值创造基于不同利益相关者的合作,需要安全和自主的知识交流。如今,知识大多是单独积累的,只能以专有方式进行交流。本文介绍了跨领域和跨公司认知生产价值创造网络中联合服务的示范管道。以协作训练联合机器学习模型为例,预测了工业制造中高端操作资源(高质量切削工具)的机床寿命。从车间到云端,所有与服务相关的信息都采用现有的数字孪生标准和链接数据方法进行结构化处理。特别是,工业 4.0 资产管理外壳(AAS)和 OPC UA 被用于收集和引用操作和工程数据。GAIA-X 连接器通过共享数据空间传输服务相关数据。该解决方案可在数据主权和透明度优先的原则下实现智能分析和决策,因此是未来协作式数据驱动型制造应用的推动者。
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Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive Production
Value creation in production is based on collaboration of different stakeholders and requires the secure and sovereign exchange of knowledge. Today, knowledge has mostly been built up individually and is only exchanged in a proprietary manner. This paper presents an exemplary pipeline for federated services in cross-domain and cross-company value creation networks for cognitive production. On the example of collaboratively training of a federated machine learning model, machine tool lifetime is predicted in industrial manufacturing for high-end operating resources (high-quality cutting tools). From the shop floor to the cloud, all service relevant information is structured using existing digital twin standards and a linked data approach. In particular, the Industry 4.0 Asset Administration Shell (AAS) and OPC UA are used for collecting and referencing operational and engineering data. GAIA-X connectors transfer the service relevant data through a shared data space. The solution enables intelligent analysis and decision-making under the prioritization of data sovereignty and transparency and, therefore, acts as an enabler for future collaborative, data-driven manufacturing applications.
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来源期刊
Journal of Machine Engineering
Journal of Machine Engineering Engineering-Industrial and Manufacturing Engineering
CiteScore
2.70
自引率
0.00%
发文量
36
审稿时长
25 weeks
期刊介绍: ournal of Machine Engineering is a scientific journal devoted to current issues of design and manufacturing - aided by innovative computer techniques and state-of-the-art computer systems - of products which meet the demands of the current global market. It favours solutions harmonizing with the up-to-date manufacturing strategies, the quality requirements and the needs of design, planning, scheduling and production process management. The Journal'' s subject matter also covers the design and operation of high efficient, precision, process machines. The Journal is a continuator of Machine Engineering Publisher for five years. The Journal appears quarterly, with a circulation of 100 copies, with each issue devoted entirely to a different topic. The papers are carefully selected and reviewed by distinguished world famous scientists and practitioners. The authors of the publications are eminent specialists from all over the world and Poland. Journal of Machine Engineering provides the best assistance to factories and universities. It enables factories to solve their difficult problems and manufacture good products at a low cost and fast rate. It enables educators to update their teaching and scientists to deepen their knowledge and pursue their research in the right direction.
期刊最新文献
Fracture Mechanics-Based Modelling of Tool Wear in Machining Ti6Al4V Considering the Microstructure of Cemented Carbide Tools Fuzzy Logic in Risk Assessment of Production Machines Failure in Forming and Assembly Processes Influence of the Substrate Size on the Cooling Behavior and Properties of the DED-LB Process Automatic Detection of Axes for Turning Parts Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive Production
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