About this document
Digital Twin for 3D Printer Fault Detection by AdDie ParaMartha is a document available to read on EtoBox.
This document presents a novel approach for fault detection in Fused Deposition Modelling (FDM) 3D printers using a Lightweight Convolutional Neural Network (LCNN) and Digital Twin (DT) technology. The LCNN model effectively monitors and detects faults based on sensor data, achieving a high F1-Score of 0.9981, while the DT environment allows for real-time monitoring and control of the printing process. This research aims to enhance the reliability and efficiency of smart additive manufacturing by minimizing
- Author
- AdDie ParaMartha
- Language
- EN