Digital Twin Machine Tool
Aims & Scope
This session aims to delve into how to propel high-end machine tools towards comprehensive intelligence through digital twin technology. The core purpose is to bring together experts from industry and academia to jointly analyze how digital twin technology can create dynamic virtual models covering the entire lifecycle for physical machine tools. It will also focus on the practical applications of digital twin technology in addressing the industry's critical problems: that is, significantly reducing costs and enhancing efficiency through virtual commissioning and reliability assessment, and ultimately achieving dynamic performance analysis/evaluation/optimization and in-service performance maintenance of machine tool equipment. The conference strives to go beyond conceptual discussions, promote cross-domain collaboration, jointly formulate industry standards and implementation pathways, accelerate the construction of a new generation of data-driven intelligent machine tool equipment, and provide core impetus for the digital transformation of equipment.
The session will focus on the following points
• Model Construction for Digital Twin Machine Tool
• Consistency of Virtual Real Interaction for Digital Twin Machine Tool
• Reliability Evaluation for Digital Twin Machine Tool
• Dynamic Performance Analysis for Digital Twin Machine Tool
• Service Performance Maintenance for Digital Twin Machine Tool
Session Chairs
Presentations
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ProfessorDalian University of TechnologyTitle: Key Technologies for Data Enablement in Intelligent MachiningAbstract To be confirmed
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Associate ProfessorTsinghua UniversityTitle: Deep learning enhances digital twin of machine tools: a case study on improving grinding accuracyAbstract Improving the machining accuracy of machine tools is a challenge in practice, and digital twins provide a feasible approach. However, a very difficult issue is that some important data cannot be directly measured during the machining process, which significantly affects the implementation of digital twins. In this speech, by taking large shaft grinding as an example, I would introduce an approach using deep learning to enhance the digital twin of machine tools. An enhanced digital twin framework of a grinder is firstly built. Then, the deep neural networks were used to deal with the time-varying characteristics of the core parameters to dynamically correct the twin model. The corrected twin model was further used to generate the compensation instruction to improve the grinding accuracy. The experimental results were given to prove the method. Moreover, related discussions were also provided to help understand that deep learning enhances the digital twin of machine tools.
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ProfessorWuhan University of Science and TechnologyTitle: A Digital Twin-Based Monitoring System for Five-Axis CNC Machining ProcessAbstract With the rapid development of intelligent technology, higher requirements are imposed on the machining accuracy, efficiency, and adaptive capabilities of CNC machine tools in the manufacturing industry. In response to the trend toward high-precision, high-speed, and complex surface machining, establishing a machining process monitoring system and achieving intelligent machine tool processing have become critical measures in the high-end manufacturing field. Traditional CNC machining monitoring systems rely on limited data sources, and their adaptability and machining accuracy fall short of meeting the demands of current complex tasks. To address this issue, this report establishes a digital twin-based monitoring system for five-axis CNC machining processes. This system employs the cutting force and processing system stiffness as key physical quantities for multi-source data acquisition. The collected data are processed through big data analytics and fed back into the digital twin system. By integrating physical models, a feedback mechanism for optimizing the tool axis vector is established, thereby enhancing machining accuracy and efficiency. The constructed digital twin system lays an important foundation for achieving self-perception, self-prediction, and self-adjustment of processing parameters in machine tool operations.
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Associate ProfessorChongqing UniversityTitle: Implementation of precision machine tool thermal error compensation in digital twin systemAbstract The implementation of precision machine tool thermal error compensation in digital twin system has the potential to control the thermal error. However, the challenges faced by the successful implementation are described as follows: The data collection and transfer efficiency is low, and the control accuracy is not deficient. To address these challenges, a hardware design scheme is proposed for the high-performance intelligent gateway node based on the low-power processor architecture of ARM Cortex-A7. Moreover, a new transformer-improved-gate long short-term memory model is proposed, and then is embedded into edge-cloud-fog computing architecture. With the implementation of gear profile grinding machine thermal error compensation in digital twin system architecture, the gear grinding accuracy is improved. Compared with the traditional collection mode, the response delay of the designed intelligent gateway in the acquisition mode is reduced by 40%.
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LecturerBeijing University of TechnologyTitle: Rapid Construction of Digital Twin Models and System Development for Discrete Manufacturing WorkshopsAbstract Discrete manufacturing workshops face challenges like complex resource allocation, uneven equipment loads, and frequent disruptions, leading to dynamic bottlenecks. The Digital Twin Workshop (DTW) system addresses these via high-fidelity modeling, real-time synchronization, and virtual simulation. It enables predictive maintenance, bottleneck detection, dynamic scheduling, and full-process monitoring, supported by integrated manufacturing, process, and equipment databases. Core research focuses on scalable modeling, unified visualization, and flexible process design to enhance production adaptability and performance.