Digital manufacturing aims to make manufacturing more productive, resilient, and competitive by improving production efficiency and enhancing product quality. To achieve this, this paper proposes a novel digital twin framework for representing complex industrial machines, materials, and products in manufacturing production lines. In the framework, digital twins comprise a physical twin, a virtual twin, and digital threads interconnecting these. The physical twin incorporates relevant physical entities in manufacturing production lines, such as production machines, a material, or a product, as well as additional attached sensors needed for measuring the properties of the physical twin. The virtual twin, which contains the description of physical twin, such as the product’s properties, and AI models use the measurements collected from the production machine or the sensors in the physical twin to optimize production efficiency and ensure products consistency/quality. The digital threads provide for bidirectional communication using industrial protocols between the physical and virtual entities, and also between the AI model(s) and the orchestration component of the virtual twin. The paper also proposes a digital twin-based platform for digital manufacturing. The platform supports the creation and lifecycle management of digital twins for the production machines, materials, and products in each production line. In addition, the platform for digital manufacturing supports the development and management of digital manufacturing solutions that enhance the productivity and resiliency of entire production lines. The paper presents a case study from the composite airframe manufacturing sector that includes a sample framework-based implementation of a digital twin of an airframe part product. This product digital twin incorporates sensors that measure the temperature and viscosity of the composite product and AI model that used these real-time measurements to predict the product quality and reduce its curing time, ensuring both the product quality and production efficiency.
Gunaratne et al. (Thu,) studied this question.