Turning Tool Wear Estimation Based on the Calculated Parameter Values of the Thermodynamic Subsystem of the Cutting System

Today, modern metalworking centers are not yet able to reliably assess the degree of wear of the tool used in metal cutting. Despite the fact that a large number of methods for monitoring the service life of the tool have been developed, this issue still remains a difficult task that needs to be solved. Idea: The article proposes a new, previously unused method for estimating the power of a cutting wedge in metalworking. The aim of the study is to develop a method for indirectly estimating the tool wear rate based on a consistent model of intersystem communication that describes the force, thermal and vibration reactions of the cutting process to the shaping movements of the tool. Research methods: The study consists of experiments on a measuring stand and a homemade measuring complex. It also uses the Matlab mathematical software package for processing and graphical interpretation of data obtained during experiments. The results show that the proposed method of estimating the current tool wear is applicable for the interpretation of experimental data. Statistically, the modified Voltaire operator of the second kind models the temperature more accurately; at the peak, this method is three times more accurate than the other.

New Tool Wear Estimation Method of the Milling Process Based on Multisensor Blind Source Separation

Timely and effective identification and monitoring of tool wear is important for the milling process. However, traditional methods of tool wear estimation have run into difficulties due to under small samples with less prior knowledge. This article addresses this issue by employing a multisensor tool wear estimation method based on blind source separation technology. Stationary subspace analysis (SSA) technology is applied to transform multisensor signals to stationary and nonstationary sources without prior information of signals. Ten dimensionless time-frequency indices of the nonstationary signal are extracted to train least squares support vector regression (LS-SVR) to obtain a tool wear estimation model for small samples. The analysis and comparison of one benchmark tool wear dataset and tool wear experiments verify the feasibility and effectiveness of the proposed method and outperform other two current methods.

Tool wear estimation in turning of Inconel 718 based on wavelet sensor signal analysis and machine learning paradigms

In the last years, hard-to-machine nickel-based alloys have been widely employed in the aerospace industry for their properties of high strength, excellent resistance to corrosion and oxidation, and long creep life at elevated temperatures. As the machinability of these materials is quite low due to high cutting forces, high temperature development and strong work hardening, during machining the cutting tool conditions tend to rapidly deteriorate. Thus, tool health monitoring systems are highly desired to improve tool life and increase productivity. This research work focuses on tool wear estimation during turning of Inconel 718 using wavelet packet transform (WPT) signal analysis and machine learning paradigms. A multiple sensor monitoring system, based on the detection of cutting force, acoustic emission and vibration acceleration signals, was employed during experimental turning trials. The detected sensor signals were subjected to WPT decomposition to extract diverse signal features. The most relevant features were then selected, using correlation measurements, in order to be utilized in artificial neural network based machine learning paradigms for tool wear estimation.