Real time fault detection in 3D printers using Convolutional Neural Networks and acoustic signals

TL;DR

This paper presents a contactless, real-time fault detection method for 3D printers using convolutional neural networks to analyze acoustic signals, improving reliability and reducing costs.

Contribution

It introduces a novel approach combining audio signal analysis with CNNs for real-time fault detection in 3D printing, which is scalable and contactless.

Findings

Audio signals reliably detect common faults

CNN-based classification achieves high accuracy

Method is cost-effective and scalable

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.