Intelligent Side-Channel Acoustic–Vibration-Based Monitoring for Additive Manufacturing of Smart Composites Using Deep Learning

Abstract

Reliable in situ monitoring is essential for the improvement of process supervision, quality assurance, and machine-state recognition in additive manufacturing of smart composite systems. This study presents a non-invasive acoustic–vibration side-channel monitoring framework for identifying FDM printing cases using experimental recordings from two printers, Bambu Lab A1 mini and Bambu Lab P1P. Four representative printing cases were investigated: simple key, hard key, two keys, and retraction test. Raw acoustic and vibration signals were converted into interval-level statistical features, including six acoustic descriptors and 18 vibration descriptors extracted from the X, Y, and Z axes. A baseline deep neural network (DNN) and an enhanced residual attention deep neural network (RA-DNN) were implemented under acoustic-only, vibration-only, and fused acoustic–vibration input conditions using stratified five-fold cross-validation. The fused acoustic–vibration features achieved the best performance, with the RA-DNN reaching 96.75% accuracy, 96.91% precision, 96.75% recall, and 96.70% F1-score for the A1 mini, and 98.19% accuracy, 98.25% precision, 98.19% recall, and 98.18% F1-score for the P1P. These results indicate that acoustic–vibration side-channel signals can provide effective process signatures for intelligent and quality-aware FDM monitoring.