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Journal of Intelligent Manufacturing

Erschienen in:

08.10.2019

Real-time machining data application and service based on IMT digital twin

verfasst von: Xin Tong, Qiang Liu, Shiwei Pi, Yao Xiao

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 5/2020

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Abstract

With the development of manufacturing, machining data applications are becoming a key technological component of enhancing the intelligence of manufacturing. The new generation of machine tools should be digitalized, highly efficient, network-accessible and intelligent. An intelligent machine tool (IMT) driven by the digital twin provides a superior solution for the development of intelligent manufacturing. In this paper, a real-time machining data application and service based on IMT digital twin is presented. Multisensor fusion technology is adopted for real-time data acquisition and processing. Data transmission and storage are completed using the MTConnect protocol and components. Multiple forms of HMIs and applications are developed for data visualization and analysis in digital twin, including the machining trajectory, machining status and energy consumption. An IMT digital twin model is established with the aim of further data analysis and optimization, such as the machine tool dynamics, contour error estimation and compensation. Examples of the IMT digital twin application are presented to prove that the development method of the IMT digital twin is effective and feasible. The perspective development of machining data analysis and service is also discussed.

Vorheriger Artikel Supervised feature learning by adversarial autoencoder approach for object classification in dual X-ray image of luggage

Nächster Artikel Multi-bead overlapping model with varying cross-section profile for robotic GMAW-based additive manufacturing

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Titel: Real-time machining data application and service based on IMT digital twin
verfasst von: Xin Tong
Qiang Liu
Shiwei Pi
Yao Xiao
Publikationsdatum: 08.10.2019
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 5/2020
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-019-01500-0

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