Tool Wear Prediction in CNC Turning Operations using Ultrasonic Microphone Arrays and CNNs

TL;DR

This paper presents a new approach combining ultrasonic microphone arrays and CNNs to predict tool wear in CNC turning, enhancing acoustic signal analysis for accurate maintenance predictions.

Contribution

It introduces a novel method integrating ultrasonic sensors and deep learning to improve tool wear prediction accuracy in CNC machining.

Findings

High-frequency acoustic emissions are effectively enhanced using beamforming.

The CNN model accurately predicts the Remaining Useful Life of cutting tools.

The approach demonstrates potential for improved predictive maintenance in CNC operations.

Abstract

This paper introduces a novel method for predicting tool wear in CNC turning operations, combining ultrasonic microphone arrays and convolutional neural networks (CNNs). High-frequency acoustic emissions between 0 kHz and 60 kHz are enhanced using beamforming techniques to improve the signal- to-noise ratio. The processed acoustic data is then analyzed by a CNN, which predicts the Remaining Useful Life (RUL) of cutting tools. Trained on data from 350 workpieces machined with a single carbide insert, the model can accurately predict the RUL of the carbide insert. Our results demonstrate the potential gained by integrating advanced ultrasonic sensors with deep learning for accurate predictive maintenance tasks in CNC machining.