用声音+CNN实时检测3D打印机故障，省钱又高效。
Real time fault detection in 3D printers using Convolutional Neural Networks and acoustic signals
- 采集打印时的声音信号，用卷积神经网络实时分类
- 初步实验显示能可靠识别喷嘴堵塞、断料等故障
- 无需额外传感器，适合工业场景快速部署
3D打印的可靠性与质量高度依赖于机械故障的及时发现。传统监测方法多依赖视觉检查和硬件传感器，成本高且覆盖范围有限。本文提出一种可扩展、无接触的实时音频信号分析方法，用于检测3D打印机的机械故障。通过采集打印过程中的声学发射信号，利用卷积神经网络实现故障分类，旨在识别喷嘴堵塞、耗材断裂、同步轮打滑等常见故障。研究通过一系列受控实验获取音频数据，并应用先进机器学习模型进行故障检测。同时，回顾了制造与3D打印领域基于音频的故障检测相关文献，以定位本研究的学术位置。初步结果表明，结合机器学习技术分析音频信号，是一种可靠且低成本的实时故障检测手段。
原文摘要 · Abstract (English)
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.
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