Many machining monitoring systems based on artificial intelligence (AI) process models
have been successfully developed in the past for optimising, predicting or controlling …

Nowadays, in the international scientific community of machine learning, there exists an
enormous discussion about the use of black-box models or explainable models; especially …

Tool failure may result in losses in surface finish and dimensional accuracy of a finished
part, or possible damage to the work piece and machine. This paper presents a Fuzzy …

A naïve Bayes classifier method for tool condition monitoring is described. End-milling tests
were performed at different spindle speeds and the cutting force was measured using a …

The main scope of this paper is to present a summary of the monitoring methods, signal
analysis and diagnostic techniques for tool wear and failure monitoring that have been …

This study analyzed the sound signals obtained from the micromilling process for microtool
wear monitoring. Various spans of spectral features were created by analyzing sound …

Acoustic emission (AE) signals collected from different locations might provide various
sensitivities to tool wear condition. Studies for tool wear monitoring using AE signals from …

In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm
for tool performance and degradation detection is investigated. The FRM is developed …

Nickel-based alloys are those of materials that are maintaining their strength at high
temperature. This feature makes these alloys a suitable candidate for power generation …

Monitoring tool wear in machining processes is one of the critical factors in reducing
downtime and maximizing profitability and productivity. A worn out tool can deteriorate the …