Tool wear estimation method in milling process using air turbine spindle rotation-control system equipped with disturbance force observer

2018 ◽  
Vol 1 (4) ◽  
pp. 384
Author(s):  
Takanori Yazawa ◽  
Kohei Shimazaki ◽  
Shotaro Matsuguchi ◽  
Tatsuki Otsubo ◽  
Tomonori Kato ◽  
...  
Keyword(s):  
Control System ◽  
Tool Wear ◽  
Estimation Method ◽  
Milling Process ◽  
Spindle Rotation ◽  
Air Turbine ◽  
Tool Wear Estimation

Tool wear estimation method in milling process using air turbine spindle rotation-control system equipped with disturbance force observer

2018 ◽  
Vol 1 (4) ◽  
pp. 384 ◽  
Author(s):  
Tomonori Kato ◽  
Tatsuki Otsubo ◽  
Kohei Shimazaki ◽  
Shotaro Matsuguchi ◽  
Yusuke Okamoto ◽  
...  
Keyword(s):  
Control System ◽  
Tool Wear ◽  
Estimation Method ◽  
Milling Process ◽  
Spindle Rotation ◽  
Air Turbine ◽  
Tool Wear Estimation

Study on Tool Wear In-Process Estimation for Ball End Mill Using Rotation Control Air Turbine Spindle

2017 ◽  
Vol 749 ◽  
pp. 94-100 ◽  
Author(s):  
Yusuke Okamoto ◽  
Takanori Yazawa ◽  
Tomonori Kato ◽  
Kazuya Nishida ◽  
Shinya Moriyama ◽  
...  
Keyword(s):  
Tool Wear ◽  
Linear Relationship ◽  
Estimation Method ◽  
High Hardness ◽  
Air Pressure ◽  
Supply Pressure ◽  
Air Turbine ◽  
Tool Wear Estimation ◽  
Ultra Precision ◽  
Relationship Of

This paper introduces a method for in-process tool wear estimation of an air turbine spindle, which is equipped with a rotation control system for ultra-precision milling. Previous investigations revealed that the pressure of the compressed air for supply that is used to control the rotational speed and tool wear at the time when steady wear occurs, maintains a linear relationship when processing SKD61 steel. In addition, the extent to which the supply pressure changed was reduced after chipping occurred. Therefore, the possibility exists that the tool wear can be estimated by obtaining the supply pressure during processing. The purpose of this paper is to propose the evaluation of an in-process tool wear estimation method, and to evaluate its validity. An estimation method is necessary as this would allow the amount of tool wear to be estimated and abnormal wear of occurrence to be detected. Because of the linear relationship between the air pressure and the amount of tool wear, the latter can be estimated by plotting the approximately linear relationship of the tool wear as a function of the air pressure. The proposed estimation method for processing the results obtained for SKD61, is capable of estimating the relative error of the measured value within 0.2 against the estimated value at the time. Furthermore, the occurrence of abnormal wear is determined from the amount of change in the supply pressure. Thus, SKD11 steel was processed for the proposed estimation method to verify whether it is valid for cutting high hardness steel. As a result, for SKD11 the estimation method produced estimation results similar to those obtained for SKD61. Therefore, the suggested estimation method is likely to be effective for high-hardness steel cutting.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chen Gao ◽  
Sun Bintao ◽  
Heng Wu ◽  
Mengjuan Peng ◽  
Yuqing Zhou
Keyword(s):  
Tool Wear ◽  
Blind Source Separation ◽  
Source Separation ◽  
Estimation Method ◽  
Milling Process ◽  
Small Samples ◽  
Support Vector ◽  
Dimensionless Time ◽  
Time Frequency ◽  
Tool Wear Estimation

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 Monitoring of High Speed Milling Based on Vibratory Signal Processing

2014 ◽  
Vol 565 ◽  
pp. 36-45
Author(s):  
Hadjadj Abdechafik ◽  
Kious Mecheri ◽  
Ameur Aissa
Keyword(s):  
Signal Processing ◽  
Tool Wear ◽  
Monitoring System ◽  
High Speed ◽  
Milling Process ◽  
Machining Performance ◽  
High Speed Milling ◽  
Tool Wear Estimation ◽  
On Line ◽  
Wear Monitoring

The objective of this study is to develop a process of treatment of the vibratory signals generated during a horizontal high speed milling process without applying any coolant in order to establish a monitoring system able to improve the machining performance. Thus, many tests were carried out on the horizontal high speed centre (PCI Météor 10), in given cutting conditions, by using a milling cutter with only one insert and measured its frontal wear from its new state that is considered as a reference state until a worn state that is considered as unsuitable for the tool to be used. The results obtained show that the first harmonic follow well the evolution of frontal wear, on another hand a wavelet transform is used for signal processing and is found to be useful for observing the evolution of the wavelet approximations through the cutting tool life. The power and the root mean square (RMS) values of the wavelet transformed signal gave the best results and can be used for tool wear estimation. All this features can constitute the suitable indicators for an effective detection of tool wear and then used for the input parameters of an on-line monitoring system. Nevertheless we noted the remarkable influence of the machining cycle on the quality of measurements by the introduction of a bias on the signal; this phenomenon appears in particular in horizontal milling and in the majority of studies is ignored

Feature-Based Tool Condition Monitoring in a Gear Shaving Application

2008 ◽  
Author(s):  
A. J. Brzezinski ◽  
Y. Wang ◽  
D. K. Choi ◽  
X. Qiao ◽  
J. Ni
Keyword(s):  
Tool Wear ◽  
Condition Monitoring ◽  
Feature Fusion ◽  
Estimation Method ◽  
Principal Component ◽  
Time Frequency ◽  
Tool Condition ◽  
Tool Wear Estimation ◽  
Artificial Neural Network Ann ◽  
Feature Dimension

Condition monitoring (CM) is an effective way to improve the tool life of a cutting tool. However, CM techniques have not been applied to monitor tool wear in an industrial gear shaving application. Therefore, this paper introduces a novel, sensor-based, data-driven, tool wear estimation method for monitoring gear shaver tool condition. The method is applied on an industrial gear shaving machine and used to differentiate between four different tool wear conditions (new, slightly worn, significantly worn, and broken). This research focuses on combining, expanding, and implementing CM techniques in an application where no previous work has been done. In order to realize CM, this paper discusses each aspect of CM, beginning with data collection and pre-processing. Feature extraction (in the time, frequency, and time-frequency domains) is then explained. Furthermore, feature dimension reduction using principal component analysis (PCA) is described. Finally, feature fusion using a multi-layer perceptron (MLP) type of artificial neural network (ANN) is presented.

Sensor-Less Tool Wear Estimation Based on Change in Value of Identified Parameter in Cutting Force Prediction Model

2020 ◽  
Author(s):  
Kazuki Kaneko ◽  
Isamu Nishida ◽  
Ryuta Sato ◽  
Keiichi Shirase
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Flank Wear ◽  
Estimation Method ◽  
Continuous Change ◽  
Cnc Machine ◽  
Cutting Force Prediction ◽  
Force Prediction ◽  
Milling Operation ◽  
Tool Wear Estimation

Abstract Several methods have been proposed to detect tool wear in milling operation using AE (Acoustic Emission) signals or cutting force signals. However, these methods require additional sensors such as an AE sensor or a dynamometer, which incurs additional costs. For this reason, a simple tool life estimation method based on machining time is used. In this study, a sensor-less tool wear estimation method is proposed. In this method, the parameters required for the cutting force prediction are identified continuously from the spindle motor torque signal, which can be monitored within the computer numerical controlled (CNC) machine. The tool wear progress can be estimated by the continuous change in the identified parameters during milling operation. To identify the parameters continuously, a real-time virtual milling simulation is performed in parallel with a physical milling operation. In the experimental results, it was confirmed that the identified parameter corresponding to the edge force component has linear relationship with the flank wear width of cutting edge. Thus the flank wear can be estimated without any additional sensor.

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