Development of an AI-ML-Based Expert System for Extrusion-Based Manufacturing Processes: A Comprehensive Framework for Process Optimization, Defect Detection, and Real-Time Control
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In this study, we developed and tested an artificial intelligence–machine learning based expert system called AIMES. The AIMES expert system was designed to be used in conjunction with extrusion-based manufacturing systems, including Fused Deposition Modeling (FDM), Single-Screw Polymer Extrusion Systems, and Hot-Melt Extrusion (HME) Systems. The system consists of three main modules: a knowledge acquisition module, a hybrid inference engine that utilizes both rule based systems and machine learning subsystems, and a real-time closed-loop feedback control interface. To evaluate the performance of the different machine learning algorithms included in the hybrid inference engine, four types of machine learning were tested. These include ANN (Artificial Neural Network), CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), and XGBoost (Gradient Boosting Ensemble Learning Algorithm). Based upon these tests, it can be concluded that all four machine learning algorithms performed well, but the best-performing algorithm was XGBoost. We also tested AIMES using in-situ measurements and found that AIMES detected clogs in the nozzle before they caused damage or produced defective products 96.8% of the time. Furthermore, the testing revealed that AIMES detected clogs as quickly as 50 milliseconds after they occurred.
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