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With the development of intelligent factories, collaborative robotic arms are becoming the critical equipment for flexible production lines, and digital twin is the best way to realize intelligent and digitized collaborative robotic arm. This paper designs and develops a collaborative robotic arm monitoring system based on digital twin and proposes a six-dimensional system architecture. Key technologies such as equipment twin modeling for the assembly process, multi-source heterogeneous data acquisition, and digital twin–driven real-time monitoring are studied. The system is driven by twin models and real-time data, and based on the key physical characteristics of the collaborative robotic arm. The model and data are effectively integrated to realize real-time monitoring of the assembly process and provide support for intelligent decision-making. The effectiveness and feasibility of the system are verified by running the application on the UR5 and UR10 collaborative robotic arm assembly test benches. [ABSTRACT FROM AUTHOR]

To address issues in discrete manufacturing workshops, such as the difficulty for management personnel to coordinate workshop production and the challenge of visualizing and supervising a massive amount of temporary data, this paper proposes a service-oriented digital-twin-system framework for discrete workshops using the industrial IoT platform as the system-service platform to solve the problems of the opaque monitoring of operators in discrete workshops, the low interactivity of 2D monitoring systems, and the difficulty of the visual monitoring of workshop data. Firstly, the current situation of intelligent manufacturing workshop-monitoring demand in the context of new-generation information technology is analyzed, and a six-dimensional digital-twin-workshop-monitoring architecture is proposed, whereby a discrete workshop monitoring system based on the digital twin is constructed with IoT as the service platform. We will conduct research on the construction of virtual workshops for the system development process, twin data collection based on edge computing gateways, and dynamic monitoring of the production process. Finally, through the application of this system framework in a movable-arm-production workshop, the more intelligent human–machine interaction process of browsing and controlling workshop information, such as the equipment layout and production processes in the virtual workshop, has been realized. This includes data acquisition based on edge-computing gateways, dynamic real-time monitoring of the production process, etc., which provides a reference for realizing the visual monitoring of the discrete workshop. [ABSTRACT FROM AUTHOR]

In the manufacturing process, digital twin technology can provide real-time mapping, prediction, and optimization of the physical manufacturing process in the information world. In order to realize the complete expression and accurate identification of and changes in the real-time state of the manufacturing process, a digital twin framework of incremental learning driven by stream data is proposed. Additionally, a novel method of stream data-driven equipment operation state modeling and incremental anomaly detection is proposed based on the digital twin. Firstly, a hierarchical finite state machine (HFSM) for the manufacturing process was proposed to completely express the manufacturing process state. Secondly, the incremental learning detection method driven by stream data was used to detect the anomaly of the job process data, so as to change the job status in real time. Furthermore, the F1 value and time consumption of the proposed algorithm were compared and analyzed using a general dataset. Finally, the method was applied to the practical case development of a welding manufacturer's digital twin system. The flexibility of the proposed model is calculated by the quantitative method. The results show that the proposed state modeling and anomaly detection method can help the system realize job state mapping and state change quickly, effectively, and flexibly. [ABSTRACT FROM AUTHOR]

For the welding cell in the manufacturing process of large excavation motor arm workpieces, a system framework, based on a digital twin welding robot cell, is proposed and constructed in order to optimize the robotic collaboration process of the welding workstation with digital twin technology. For the automated welding cell, combined with the actual robotic welding process, the physical entity was digitally modeled in 3D, and the twin welding robot operating posture process beats and other data were updated in real time, through real-time interactive data drive, to achieve real-time synchronization and faithful mapping of the virtual twin as well as 3D visualization and monitoring of the system. For the robot welding process in the arc welding operation process, a mathematical model of the kinematics of the welding robot was established, and an optimization method for the placement planning of the initial welding position of the robot base was proposed, with the goal of smooth operation of the robot arm joints, which assist in the process simulation verification of the welding process through the virtual twin scenario. The implementation and validation process of welding process optimization, based on this digital twin framework, is introduced with a moving arm robot welding example. [ABSTRACT FROM AUTHOR]

Digital twin (DT) is a key enabling technology to realize cyber-physical system (CPS), which can truly perceive, map, and predict the operating state of physical entity. Through analyzing the generality of the testing process of large and complex equipment components (LCEC), a five-dimensional framework of DT-based test process management (DTTPM) is proposed, which comprises physical layer, network layer, data layer, model layer, and service layer. In order to realize the visualization and enhance the controllability, security, and information transparency of LCEC in the testing process, three key technologies are elaborated in detail as follows: (1) the construction of the twin semantic model of the testing process in model layer, (2) the synchronization method of twin model and physical entity based on real-time data, (3) and human–computer interaction–based visual monitoring of authenticity and safety coordination. Through the case of the vibration test for the crowned blade of a steam turbine by blade tip-timing measurement, the feasibility and flexibility of DTTPM are demonstrated. [ABSTRACT FROM AUTHOR]