Publication | Open Access
Sensor Data and Information Fusion to Construct Digital-twins Virtual Machine Tools for Cyber-physical Manufacturing
329
Citations
20
References
2017
Year
Virtual ManufacturingEngineeringMachining Characteristics ProfilesDigital TwinningDigital ManufacturingSmart ManufacturingSensor Data IntegrationAutomated ManufacturingVirtual SensorSystems EngineeringDigital TwinIndustrial InformaticsSensor DataComputer EngineeringManufacturing SystemsCyber-physical ManufacturingCyber ManufacturingMachine ToolsAutomationTechnology
Virtual machine tools enable safe, cost‑effective simulation of machine tool behavior, yet accurately emulating physical tools remains difficult, and they also provide diagnostic traces when physical tools fail. This study aims to integrate manufacturing and sensor data to construct digital‑twins virtual machine tools that enhance accountability and capabilities in cyber‑physical manufacturing. By deploying sensors to capture machine‑specific features and fusing the data to extract machining characteristic profiles, the authors model digital‑twins virtual machine tools, illustrated with a 3‑axis vertical milling machine. The developed technique serves as a building block for cyber‑physical manufacturing development.
This paper presents sensor data integration and information fusion to build “digital-twins” virtual machine tools for cyber-physical manufacturing. Virtual machine tools are useful for simulating machine tools’ capabilities in a safe and cost-effective way, but it is challenging to accurately emulate the behavior of the physical tools. When a physical machine tool breaks down or malfunctions, engineers can always go back to check the digital traces of the “digital-twins” virtual machine for diagnosis and prognosis. This paper presents an integration of manufacturing data and sensory data into developing “digital-twins” virtual machine tools to improve their accountability and capabilities for cyber-physical manufacturing. The sensory data are used to extract the machining characteristics profiles of a digital-twins machine tool, with which the tool can better reflect the actual status of its physical counterpart in its various applications. In this paper, techniques are discussed for deploying sensors to capture machine-specific features, and analytical techniques of data and information fusion are presented for modeling and developing “digital-twins” virtual machine tools. Example of developing the digital-twins of a 3-axis vertical milling machine is presented to demonstrate the concept of modeling and building a digital-twins virtual machine tool for cyber-physical manufacturing. The presented technique can be used as a building block for cyber-physic manufacturing development.
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