Development of Digital Twin for FDM Printer With Preventive Cyber-Attack and Control Algorithms

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 based on a real-time feedback system for controlling the material feed. 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.