ID:
publications-4539
Type:
article
Year:
2017
Authors:
Cai, Yi and Starly, Binil and Cai, Yi and Cohen, Paul H. and Starly, Binil and Cohen, Paul H. and Lee, Yuanβ€Shin and Lee, Yuan-Shin
Title:
Sensor Data and Information Fusion to Construct Digital-twins Virtual Machine Tools for Cyber-physical Manufacturing
Venue/Journal:
Procedia Manufacturing
DOI:
10.1016/j.promfg.2017.07.094
Research type:
Water System:
Technical Focus:
Abstract:
Abstract This paper presents sensor data integration and information fusion to build β€_x009c_digital-twinsβ€_x009d_ 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 β€_x009c_digital-twinsβ€_x009d_ virtual machine for diagnosis and prognosis. This paper presents an integration of manufacturing data and sensory data into developing β€_x009c_digital-twinsβ€_x009d_ 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 β€_x009c_digital-twinsβ€_x009d_ 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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