Anomaly detection in 3D printing: deep learning and traditional machine learning approaches with IMU and acoustic data

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Abstract Fused deposition modeling (FDM) is a widely used additive manufacturing (AM) process valued for its versatility in rapid prototyping and its 2.5D part-generation capabilities. However, the quality and mechanical properties of FDM-printed parts are highly sensitive to variations in process parameters, such as material properties, temperature, and printing speed, leading to challenges in maintaining consistent part performance. This study addresses real-time sensor-based anomaly detection in FDM by using time-series data collected from a printing device. We compared two approaches: traditional handcrafted feature extraction methods and deep learning (DL) models, which automatically extract features by transforming signals into images suitable for machine vision algorithms. Specifically, we designed and evaluated bespoke hybrid models that combine convolutional neural networks (CNNs) and long short-term memory (LSTM) units (CNN-LSTM) to monitor the FDM process by utilizing acoustic and vibration signals for anomaly detection. Experimental results show that while traditional machine learning methods, particularly support vector machines (SVMs), achieved slightly higher raw classification metrics, statistical analysis confirmed that these differences were not significant. Moreover, CNN-LSTM models demonstrate notable advantages in terms of computational efficiency, robustness to noise, and future scalability, making them strong candidates for real-time and industrial monitoring applications.