Tool condition monitoring in metal cutting: A neural network approach

Abstract

This paper discusses the application of neural network-based pattern recognition techniques for monitoring the metal-cutting process. The specific application considered is in-process monitoring of the condition of the cutting tool. Tool condition monitoring is an important prerequisite for successful automation of the metal cutting process. In this paper, we demonstrate the application of supervised and unsupervised neural network paradigms to pattern recognition of sensor signal features. The supervised technique used is backpropagation and the unsupervised technique used is adaptive resonance theory (ART). The results support the premise that, despite excellent classification accuracy by both networks, the unsupervised system holds greater promise in a real world setting. The advantages are discussed and a framework for exploiting them in tool condition monitoring systems is presented.

On-Line and Indirect Tool Wear Monitoring in Turning With Artificial Neural Networks: A Review of More Than a Decade of Research

“…In [23], the use of ART networks and MLP networks leads to comparable results for a classification. However, the ART networks are supposed to have the following advantages:…”

An introduction to neural networks and their applications in manufacturing

“…One of the attempts to use NNs for this task is reported by Burke and Rangwala (1991). Here the researchers first selected some of the signal components from a number of raw input data which are more sensitive to tool ware, but less sensitive to process noise.…”

Multiple-objective optimisation of machining operations based on neural networks

“…For example, Rangwala and Dornfeld [13] apply neural networks for learning and optimisation of machining operations. Rangwala and Dornfeld [14], Burke and Rangwala [15] propose neural network paradigms for tool wear monitoring.…”