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.

翻译：三维打印过程的可靠性与质量在很大程度上取决于机械故障的及时检测。传统监测方法通常依赖于视觉检查与硬件传感器，这些方法不仅成本高昂且监测范围有限。本文探讨了一种可扩展、非接触式的实时音频信号分析方法，用于检测3D打印机中的机械故障。通过采集并分类打印过程中的声发射信号，我们旨在识别常见故障，如喷嘴堵塞、线材断裂、皮带轮打滑及其他各类机械故障。利用卷积神经网络，我们实现了能够实时进行音频分类以快速检测这些故障的算法。我们的方法包括进行一系列受控实验以收集音频数据，随后应用先进的机器学习模型进行故障检测。此外，我们回顾了制造业和3D打印领域中基于音频的故障检测现有文献，将本研究置于更广泛的领域背景下进行定位。初步结果表明，通过机器学习技术分析音频信号，为增强实时故障检测提供了一种可靠且经济有效的手段。