3D-AmplifAI: An Ensemble Machine Learning Approach to Digital Twin Fault Monitoring for Additive Manufacturing in Smart Factories

In the digital age, twin eliminates physical barriers and risks, facilitating seamless activities in both real virtual worlds. context of additive manufacturing, testing 3D printers can be resource-intensive prone to printing issues. This research introduces a twin-based system that employs innovative ensemble 3D-AmplifAI algorithm for fault monitoring printers. The continuously monitors real-time temperature values detects faults prevent potential damage printer. Through an method, combines multiple machine learning models enhance detection environment, developed using Unity, serves as bridge connecting printer world. Comparative evaluations against state-of-the-art algorithms, including Ridge Regression, XGBoost, InceptionTime, Time Series Transformer (TST), Rocket Ridge, Logistic ResNet, demonstrate superior performance terms accuracy, precision, recall, F1-score.