ABSTRACT

Modern industrial production systems are facing the challenge of interconnecting and exploiting the increasing amount of data from smart products, software systems, and IIoT devices. One of the main tasks in this context is the implementation of a data-continuous traceability system, which interconnects the heterogeneous data objects over the entire production flow. While traceability maturity is already high in regulated and process-driven industries, the established traceability solutions cannot be successfully applied to customized and volatile industries. In this research, a graph-based traceability modeling methodology is presented that allows the systematic development of data continuity and integration in manufacturing. Based on the traceability methodology, the graph data model and architecture are specified, which are aligned to the requirements of customized and complex manufacturing systems. The model is implemented and validated based on a Neo4j graph database for the use case of the manufacturing process of automotive electrical systems. This research overcomes the shortcomings of state-of-the-art traceability models by shifting the focus to the relationships between traceability-relevant data objects. The proposed graph-based traceability model is able to capture multi-hierarchical product structures and is less dependent on physical object identification, making it more applicable to customized and complex manufacturing.

摘要

现代工业生产系统面临着互连和利用来自智能产品，软件系统和IIoT设备的越来越多数据的挑战。在这种情况下，主要任务之一是实现数据连续的可追溯性系统，该系统在整个生产流程中互连异构数据对象。尽管在受监管和流程驱动的行业中，可追溯性成熟度已经很高，但是已建立的可追溯性解决方案无法成功地应用于定制和易变的行业。在这项研究中，提出了一种基于图的可追溯性建模方法，该方法允许系统地开发数据连续性并集成到制造中。根据可追溯性方法，指定了图形数据模型和架构，符合定制和复杂制造系统的要求。该模型是根据Neo4j图形数据库针对汽车电气系统制造过程的用例进行实施和验证的。通过将重点转移到与可追溯性相关的数据对象之间的关系，这项研究克服了最新可追溯性模型的缺点。所提出的基于图形的可追溯性模型能够捕获多层次的产品结构，并且对物理对象识别的依赖性较小，从而使其更适用于定制和复杂的制造。通过将重点转移到与可追溯性相关的数据对象之间的关系，这项研究克服了最新可追溯性模型的缺点。所提出的基于图形的可追溯性模型能够捕获多层次的产品结构，并且对物理对象识别的依赖性较小，从而使其更适用于定制和复杂的制造。通过将重点转移到与可追溯性相关的数据对象之间的关系，这项研究克服了最新可追溯性模型的缺点。所提出的基于图形的可追溯性模型能够捕获多层次的产品结构，并且对物理对象识别的依赖性较小，从而使其更适用于定制和复杂的制造。