Digital process twins: a modular approach for surface conditioning and process optimization

The age of Industry 4.0 has ushered in a wave of complexity and sustainability concerns for manufacturing products and processes. To address these concerns, industrial and academic efforts have worked towards developing a Digital Engineering (DE) ecosystem, one in which digitized documents connect and manage development for products. Within the DE environment, the Digital Twin (DT) stands as the atomic unit of construction, providing the building blocks upon which a successful DE environment rests. But not all value-informed aspects of a product are physical assets, and thus do not fall under the umbrella of a DT model. One such example is a unit manufacturing process (UMP), where a Digital Shadow provides relevant incoming streams of process data and simulation models exist to predict process outcomes; however, it is not a physical asset. Thus there are numerous challenges and multi-scale complexities associated with generating DTs of UMPs and their sub-scale attributes (machines, tooling, process physics, etc.), and with connecting these back to the DT of manufactured products. To combat this shortcoming currently present in DE, we propose the Digital Process Twin (DPT): a DT-like entity capable of bi-directional monitoring and optimization of UMPs. In this paper we detail what a DPT is, how it is represented, how it fits into the DE environment, and ending with a case study on surface finish milling of the advanced aerospace alloy \(\gamma\) $\gamma$ -TiAl. This paper covers both the theory and practical application of a DPT, giving a well-rounded introduction to this extension of the DE ecosystem.