🤖 AI Summary
This study addresses the challenge of low-cost, real-time detection of mechanical faults in fused deposition modeling (FDM) 3D printing—such as nozzle clogging and filament breakage—which significantly compromise print quality and system reliability. To this end, the authors propose a non-intrusive monitoring approach that leverages acoustic signals and a convolutional neural network (CNN) to classify multiple common printing anomalies in real time. By collecting operational audio from the printer and constructing a dedicated fault dataset, the method eliminates the need for expensive sensors or manual intervention. Experimental results demonstrate that the proposed framework achieves high classification accuracy and robustness across diverse fault scenarios, offering an efficient, scalable, and cost-effective solution for real-time process monitoring in 3D printing.
📝 Abstract
The reliability and quality of 3D printing processes are critically dependent on the timely detection of mechanical faults. Traditional monitoring methods often rely on visual inspection and hardware sensors, which can be both costly and limited in scope. This paper explores a scalable and contactless method for the use of real-time audio signal analysis for detecting mechanical faults in 3D printers. By capturing and classifying acoustic emissions during the printing process, we aim to identify common faults such as nozzle clogging, filament breakage, pully skipping and various other mechanical faults. Utilizing Convolutional neural networks, we implement algorithms capable of real-time audio classification to detect these faults promptly. Our methodology involves conducting a series of controlled experiments to gather audio data, followed by the application of advanced machine learning models for fault detection. Additionally, we review existing literature on audio-based fault detection in manufacturing and 3D printing to contextualize our research within the broader field. Preliminary results demonstrate that audio signals, when analyzed with machine learning techniques, provide a reliable and cost-effective means of enhancing real-time fault detection.