Machine learning-based detection of layer-shifting in FFF 3D printing using MEMS vibration and acoustic sensors

Layer shifting is one of the most critical machine-induced defects in fused filament fabrication (FFF) three-dimensional (3D) printing, primarily caused by belt drive slippage along the x-axis. This defect leads to dimensional inaccuracies and structural misalignments in printed parts. The present study aims to develop a machine learning (ML)-based framework supported by sensor data to detect layer shifting at an early stage of the printing process. A Cartesian FFF 3D printer was equipped with two low-cost MEMS sensors, an ADXL335 accelerometer and a MAX4466 microphone, mounted near the x-axis stepper motor to capture vibration and acoustic signals under normal and faulty conditions. Using a Taguchi L9 design, 54 specimens were printed with controlled variations. The acquired signals were sampled at 1,600 Hz, and five time-domain features (Root Mean Square, Mean, Standard Deviation, Peak-to-Peak and Crest Factor) were extracted. Eight ML classifiers (SVM, RF, GB, LR, DT, KNN, NB and XGBoost) were trained and evaluated for both sensor data sets using accuracy, precision, recall, F1 score and ROC-AUC. Among the models tested, Random Forest demonstrated superior performance on accelerometer data with 97.43% accuracy, 97.71% precision, 97.31% F1 score and a ROC-AUC of 0.9951, indicating high fault detection reliability. This study presents a novel approach that combines vibration and acoustic sensing with ML-based classification to identify machine-related defects in FFF printing. It further proposes a conceptual real-time feedback framework for future integration into intelligent, fault-tolerant 3D printing systems.