Digital twin-enabled 3D printer fault detection for smart additive manufacturing

被引:12
|
作者
Rachmawati, Syifa Maliah [1 ]
Putra, Made Adi Paramartha [1 ,2 ]
Lee, Jae Min [1 ]
Kim, Dong Seong [1 ]
机构
[1] Dept IT Convergence Engn, 61 Daehak Ro, Gumi 39177, South Korea
[2] STMIK Primakara, Informat Engn, Denpasar 80226, Indonesia
关键词
Additive manufacturing; Deep learning; Digital twin; Fault detection; FDM printer; CHALLENGES; MANAGEMENT; SYSTEM;
D O I
10.1016/j.engappai.2023.106430
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Early failure detection is required for Fused Deposition Modelling (FDM) 3D printers to reduce material waste. Typically, such systems are created based on images captured during printing or sensor data for tracking the extruder's movement. This work presents a novel approach to sensor data-driven fault diagnosis, utilizing Artificial Intelligence (AI) technology to investigate the temperature imbalance in the extruder and printing surface. First, a Lightweight Convolutional Neural Network (LCNN) is proposed to detect faults from sensory data. The model's architecture concatenates the CNN layer to extract additional features, improving the model's performance while maintaining a lightweight configuration suitable for real-time monitoring systems. Second, the concept of Digital Twin (DT) technology for FDM 3D printer fault detection is introduced. The DT creates a virtual representation of a physical object, and its functionality is validated by examining the network's latency and System Overhead (SO) as the number of clients increases. The simulation results show that the proposed LCNN with a DT environment can effectively monitor, detect, and control the physical workplace with an F1-Score of 0.9981 and an average latency of 995.4253 ms. Additionally, this research contributes to the development of future technologies for virtual condition monitoring of 3D printer abnormalities, which will be essential for intelligent and autonomous factories.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] Digital Twin-Enabled Machine Learning for Smart Manufacturing
    Jain, Sanjay
    Narayanan, Anantha
    [J]. SMART AND SUSTAINABLE MANUFACTURING SYSTEMS, 2023, 7 (01): : 111 - 128
  • [2] Digital twin-enabled reconfigurable modeling for smart manufacturing systems
    Zhang, Chenyuan
    Xu, Wenjun
    Liu, Jiayi
    Liu, Zhihao
    Zhou, Zude
    Duc Truong Pham
    [J]. INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2021, 34 (7-8) : 709 - 733
  • [3] Digital twin-enabled error and uncertainty mapping for 3D scanning
    Sepahi-Boroujeni, Saeid
    Khameneifar, Farbod
    [J]. Precision Engineering, 2024, 88 : 527 - 539
  • [4] Digital twin-enabled error and uncertainty mapping for 3D scanning
    Sepahi-Boroujeni, Saeid
    Khameneifar, Farbod
    [J]. PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY, 2024, 88 : 527 - 539
  • [5] Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems
    Liu, Chao
    Le Roux, Leopold
    Korner, Carolin
    Tabaste, Olivier
    Lacan, Franck
    Bigot, Samuel
    [J]. JOURNAL OF MANUFACTURING SYSTEMS, 2022, 62 : 857 - 874
  • [6] Digital Twin-Enabled Health Prognostics for Smart Manufacturing Systems Under Uncertain Operating Conditions
    Yang, Hanbo
    Feng, Chuanfeng
    Jiang, Gedong
    Mei, Xuesong
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2024,
  • [7] Digital twin-enabled smart industrial systems: a bibliometric review
    Ciano, Maria Pia
    Pozzi, Rossella
    Rossi, Tommaso
    Strozzi, Fernanda
    [J]. INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2021, 34 (7-8) : 690 - 708
  • [8] Digital twin-enabled smart facility management: A bibliometric review
    Hakimi, Obaidullah
    Liu, Hexu
    Abudayyeh, Osama
    [J]. FRONTIERS OF ENGINEERING MANAGEMENT, 2024, 11 (01) : 32 - 49
  • [9] Digital twin-enabled smart facility management: A bibliometric review
    Obaidullah Hakimi
    Hexu Liu
    Osama Abudayyeh
    [J]. Frontiers of Engineering Management, 2024, 11 : 32 - 49
  • [10] Federated Learning-Enabled Digital Twin for Smart Additive Manufacturing Industry
    Putra, Made Adi Paramartha
    Rachmawati, Syifa Maliah
    Alief, Revin Naufal
    Ahakonye, Love Allen Chijioke
    Gohil, Augustin
    Kim, Dong-Seong
    Lee, Jae-Min
    [J]. 2023 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION, ICAIIC, 2023, : 806 - 811