Information Flow in Digital Twin for "Detection to Repair" of Defects Using Additive Manufacturing

被引:0
|
作者
Bender, Dylan [1 ]
Anderson, Jordan [2 ]
Gilbert, Mike [2 ]
Barari, Ahmad [1 ]
机构
[1] Ontario Tech Univ, Dept Mech & Mfg Engn, Adv Digital Design Mfg & Metrol Labs AD2MLabs, Oshawa, ON, Canada
[2] Ontario Power Generat, Innovat Dev X Lab, Oshawa, ON, Canada
来源
IFAC PAPERSONLINE | 2024年 / 58卷 / 19期
关键词
Digital Inspection; Digital Manufacturing; Additive Manufacturing; Manufacturing Information Control; Defect Detection and Repair; Integrated Inspection System; INSPECTION; SURFACES; MODEL;
D O I
10.1016/j.ifacol.2024.09.215
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Digitalization in inspection and manufacturing results in a wide range of advantages including the reduction of cost, complexity, and operation time, and increasing the flexibility, level of automation, and the capabilities to gain intelligence. This paper discusses an attractive benefit of digitalization which allows the integration of the information flow and control for the two processes of digital inspection and additive manufacturing. A digital twin of the additive manufacturing process is dynamically updated based on the intermittent inspection data obtained from the workpiece to integrate the information of the digital model for planning and controlling additive manufacturing process. The ultimate objective is to repair highly expensive, and large components in industrial sectors. The developed digital twin for this integrated system includes eight activities are demonstrated through an industrial case study. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
引用
收藏
页码:736 / 741
页数:6
相关论文
共 50 条
  • [21] Hybrid Physical-Virtual Digital Twin System for Additive Manufacturing
    Love, Allen
    Behseresht, Saeed
    Pastrana, Omar Alejandro Valdez
    Sakai, James
    Park, Young Ho
    JOURNAL OF ADVANCED MANUFACTURING SYSTEMS, 2025, 24 (01) : 1 - 20
  • [22] Integrating Machine Learning Model and Digital Twin System for Additive Manufacturing
    Jyeniskhan, Nursultan
    Keutayeva, Aigerim
    Kazbek, Gani
    Ali, Md Hazrat
    Shehab, Essam
    IEEE ACCESS, 2023, 11 : 71113 - 71126
  • [23] Robust Additive Manufacturing Performance through a Control Oriented Digital Twin
    Stavropoulos, Panagiotis
    Papacharalampopoulos, Alexios
    Michail, Christos K.
    Chryssolouris, George
    METALS, 2021, 11 (05)
  • [24] A digital twin for composite parts manufacturing Effects of defects analysis based on manufacturing data
    Zambal, Sebastian
    Eitzinger, Christian
    Clarke, Michael
    Klintworth, John
    Mechin, Pierre-Yves
    2018 IEEE 16TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), 2018, : 803 - 808
  • [25] Pore defects repair of CCF/SiC composites fabricated by additive manufacturing
    Liu, Tianlong
    Xiong, Lijun
    Chen, Zhaofeng
    Lu, Le
    Li, Manna
    Ma, Zhudan
    Yang, Lixia
    Wu, Guoping
    Xing, Yuming
    Wang, Xingpu
    Sun, Ce
    Liu, Kai
    CERAMICS INTERNATIONAL, 2024, 50 (13) : 24358 - 24367
  • [26] Non-destructive detection of critical defects in additive manufacturing
    Baig, Shaharyar
    Jam, Alireza
    Beretta, Stefano
    Shao, Shuai
    Shamsaei, Nima
    SCIENTIFIC REPORTS, 2025, 15 (01):
  • [27] Big data, machine learning, and digital twin assisted additive manufacturing: A review
    Jin, Liuchao
    Zhai, Xiaoya
    Wang, Kang
    Zhang, Kang
    Wu, Dazhong
    Nazir, Aamer
    Jiang, Jingchao
    Liao, Wei-Hsin
    MATERIALS & DESIGN, 2024, 244
  • [28] On the digital twin application and the role of artificial intelligence in additive manufacturing: a systematic review
    Bartsch, Katharina
    Pettke, Alexander
    Hubert, Artur
    Lakaemper, Julia
    Lange, Fritz
    JOURNAL OF PHYSICS-MATERIALS, 2021, 4 (03):
  • [29] 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
    2023 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION, ICAIIC, 2023, : 806 - 811
  • [30] Additive manufacturing and repair by using laser metal deposition
    MORIHASHI R.
    IWASAKI H.
    Yosetsu Gakkai Shi/Journal of the Japan Welding Society, 2021, 90 (02): : 19 - 24