多学科系统工程过程的生产感知分析

Lukas Kathrein, A. Lüder, Kristof Meixner, D. Winkler, S. Biffl
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引用次数: 15

摘要

柔性制造系统的工业4.0愿景依赖于来自各种工程学科的领域专家的协作,以及产品和生产系统之间关系(PPR知识)知识的明确表示。然而,在多学科系统工程组织中,过程分析和改进传统上集中在一个特定的学科上,而不是集中在几个工作组的协作和他们对产品/ion(即产品和生产过程)的知识交流上。在本文中,我们研究了工程过程的产品/离子感知分析的需求,以改进跨工作组的工程过程。我们引入了产品/离子感知工程过程分析(PPR EPA)方法,以确定所需和提供的PPR知识的差距。为了表示PPR知识,我们通过扩展BPMN 2.0标准,引入产品/离子感知数据处理图(PPR DPM),增加PPR知识分类。我们在一个大型生产系统工程公司的案例研究中评估其贡献。领域专家发现,使用PPR DPM的PPR EPA方法可用于跟踪工程过程中的设计决策,并可作为高级质量保证分析的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Production-Aware Analysis of Multi-disciplinary Systems Engineering Processes
The Industry 4.0 vision of flexible manufacturing systems depends on the collaboration of domain experts coming from a variety of engineering disciplines and on the explicit representation of knowledge on relationships between products and production systems (PPR knowledge). However, in multi-disciplinary systems engineering organizations, process analysis and improvement has traditionally focused on one specific discipline rather than on the collaboration of several workgroups and their exchange of knowledge on product/ion, i.e., product and production processes. In this paper, we investigate requirements for the product/ion-aware analysis of engineering processes to improve the engineering process across workgroups. We introduce a product/ion-aware engineering processes analysis (PPR EPA) method, to identify gaps in PPR knowledge needed and provided. For representing PPR knowledge, we introduce a product/ion-aware data processing map (PPR DPM) by extending the BPMN 2.0 standard, adding PPR knowledge classification. We evaluate the contribution in a case study at a large production systems engineering company. The domain experts found the PPR EPA method using the PPR DPM usable and useful to trace design decisions in the engineering process as foundation for advanced quality assurance analyses.
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