Deep Learning Approach for Enhanced Transferability and Learning Capacity in Tool Wear Estimation

TL;DR

This paper presents a deep learning method for tool wear estimation that improves transferability and learning speed, validated through milling experiments with varying cutting parameters.

Contribution

A novel deep learning approach for tool wear estimation that enhances transferability and rapid learning compared to traditional methods.

Findings

Outperforms conventional methods in accuracy

Demonstrates high transferability across different cutting conditions

Shows rapid learning capabilities in tool wear estimation

Abstract

As an integral part of contemporary manufacturing, monitoring systems obtain valuable information during machining to oversee the condition of both the process and the machine. Recently, diverse algorithms have been employed to detect tool wear using single or multiple sources of measurements. In this study, a deep learning approach is proposed for estimating tool wear, considering cutting parameters. The model's accuracy and transferability in tool wear estimation were assessed with milling experiments conducted under varying cutting parameters. The results indicate that the proposed method outperforms conventional methods in terms of both transferability and rapid learning capabilities.