Abstract
This paper presents a developed model of a Digital Twin (DT) for a fused deposition modeling (FDM) printer, real-time defect detection, and proposed frameworks for preventing cyber-attacks in real-time. It also highlights a model predictive control (MPC) algorithm for controlling the material feed based on a real-time feedback system. The system is designed and developed based on DT, and MPC with integrated machine learning (ML) algorithms to establish real-time process control and enhance the safety and reliability of the physical plant. ML algorithm is used for anomaly detection based on the convolutional neural network (CNN) model. The developed system can be practically utilized in smart manufacturing industries as well as cyber-physical systems-based plants. The work is novel and original as this type of DT and cyber physical systems (CPS) are very new to additive manufacturing (AM) industries. There are several conceptual models in the literature and there is a critical need for such implemented working systems.
Recommended Citation
M. H. Ali et al., "Development of Digital Twin for FDM Printer with Preventive Cyber-Attack and Control Algorithms," IEEE Access, Institute of Electrical and Electronics Engineers, Jan 2024.
The definitive version is available at https://doi.org/10.1109/ACCESS.2024.3516827
Department(s)
Mechanical and Aerospace Engineering
Publication Status
Open Access
Keywords and Phrases
Additive Manufacturing; Cyber-Physical System; Cybersecurity; Digital Twin; Machine Learning; Model Predictive Control; Smart Manufacturing
International Standard Serial Number (ISSN)
2169-3536
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2025 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jan 2024
