Aiming at the problems in the current surface roughness prediction methods that interface with the normal machining of machine tools, poor real-time performance, inconvenient sensor installation and high cost, a multi-sensor surface roughness prediction method based on digital twin was proposed. Firstly, the digital twin model of intelligent workshop was established as the only data source for workshop monitoring and surface roughness prediction; secondly, vibration signal was preprocessed and combined with power, energy consumption and cutting parameters to construct joint multi feature vector, and feature fusion was performed by principal component analysis; finally, support vector machine was used to predict surface roughness. The results showed that the average relative error was 4.00% and the maximum error was 0.07 μm, which verified the effectiveness of the method.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Digital Twin-Driven Surface Roughness Prediction Based on Multi-sensor Fusion


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wang, Yi (Herausgeber:in) / Martinsen, Kristian (Herausgeber:in) / Yu, Tao (Herausgeber:in) / Wang, Kesheng (Herausgeber:in) / Zhang, Xiangyu (Autor:in) / Liu, Lilan (Autor:in) / Wu, Fang (Autor:in) / Wan, Xiang (Autor:in)

    Kongress:

    International Workshop of Advanced Manufacturing and Automation ; 2020 ; Zhanjiang, China October 12, 2020 - October 13, 2020



    Erscheinungsdatum :

    2021-01-23


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Digital Twin-Driven Surface Roughness Prediction Based on Multi-sensor Fusion

    Zhang, Xiangyu / Liu, Lilan / Wu, Fang et al. | TIBKAT | 2021


    Sensor Fusion of Camera and Cloud Digital Twin Information for Intelligent Vehicles

    Liu, Yongkang / Wang, Ziran / Han, Kyungtae et al. | IEEE | 2020


    Research on Multi-modal Data Cross-Media Perception Fusion Algorithm Based on Digital Twin

    Wan, Xiang / Liu, Lilan / Feng, Bowen et al. | Springer Verlag | 2021



    SLOSHING PREDICTION SYSTEM USING DIGITAL TWIN

    KIM UE KAN / DOH DEOG HEE / NAM JONG HO et al. | Europäisches Patentamt | 2021

    Freier Zugriff