Abstract Metal Additive Manufacturing (AM) has been attracting a continuously increasing attention due to its great advantages compared to traditional subtractive manufacturing in terms of higher design flexibility, shorter development time, lower tooling cost, and fewer production wastes. However, the lack of process robustness, stability and repeatability caused by the unsolved complex relationships between material properties, product design, process parameters, process signatures, post AM processes and product quality has significantly impeded its broad acceptance in the industry. To facilitate efficient implementation of advanced data analytics in metal AM, which would support the development of intelligent process monitoring, control and optimisation, this paper proposes a novel Digital Twin (DT)-enabled collaborative data management framework for metal AM systems, where a Cloud DT communicates with distributed Edge DTs in different product lifecycle stages. A metal AM product data model that contains a comprehensive list of specific product lifecycle data is developed to support the collaborative data management. The feasibility and advantages of the proposed framework are validated through the practical implementation in a distributed metal AM system developed in the project MANUELA. A representative application scenario of cloud-based and deep learning-enabled metal AM layer defect analysis is also presented. The proposed DT-enabled collaborative data management has shown great potential in enhancing fundamental understanding of metal AM processes, developing simulation and prediction models, reducing development times and costs, and improving product quality and production efficiency.

中文翻译：

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用于金属增材制造系统的数字孪生协作数据管理

摘要 金属增材制造（AM）与传统减材制造相比，在设计灵活性更高、开发时间更短、模具成本更低、生产浪费更少等方面具有巨大优势，因此受到越来越多的关注。然而，由于材料特性、产品设计、工艺参数、工艺特征、增材制造后工艺和产品质量之间未解决的复杂关系导致工艺稳定性、稳定性和可重复性的缺乏，这大大阻碍了其在行业中的广泛接受。为了促进金属增材制造中高级数据分析的有效实施，这将支持智能过程监控、控制和优化的发展，本文提出了一种用于金属 AM 系统的新型数字孪生 (DT) 协作数据管理框架，其中 Cloud DT 在不同产品生命周期阶段与分布式 Edge DT 进行通信。开发了包含特定产品生命周期数据的完整列表的金属 AM 产品数据模型，以支持协作数据管理。通过在 MANUELA 项目中开发的分布式金属 AM 系统中的实际实施，验证了所提出框架的可行性和优势。还介绍了基于云和支持深度学习的金属 AM 层缺陷分析的代表性应用场景。提议的支持 DT 的协作数据管理在增强对金属 AM 工艺的基本理解方面显示出巨大潜力，