A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling

Measurement ◽  
2020 ◽  
Vol 166 ◽  
pp. 108186 ◽  
Author(s):  
Yuqing Zhou ◽  
Bintao Sun ◽  
Weifang Sun
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Monitoring Method ◽  
Tool Condition ◽  
Kernel Extreme Learning Machine ◽  
Binary Differential Evolution ◽  
Learning Machine

Tool condition monitoring system based on support vector machine and differential evolution optimization

2016 ◽  
Vol 231 (5) ◽  
pp. 805-813 ◽  
Author(s):  
Guo F Wang ◽  
Qing L Xie ◽  
Yan C Zhang
Keyword(s):  
Support Vector Machine ◽  
Tool Wear ◽  
Differential Evolution ◽  
Condition Monitoring ◽  
Support Vector Machine Classifier ◽  
Tool Condition Monitoring ◽  
Support Vector ◽  
Grid Search ◽  
Tool Condition ◽  
Condition Monitoring System

A tool condition monitoring system based on support vector machine and differential evolution is proposed in this article. In this system, support vector machine is used to realize the mapping between the extracted features and the tool wear states. At the same time, two important parameters of the support vector machine which are called penalty parameter C and kernel parameter [Formula: see text] are optimized simultaneously based on differential evolution algorithm. In order to verify the effectiveness of the proposed system, a multi-tooth milling experiment of titanium alloy was carried out. Cutting force signals related to different tool wear states were collected, and several time domain and frequency domain features were extracted to depict the dynamic characteristics of the milling process. Based on the extracted features, the differential evolution-support vector machine classifier is constructed to realize the tool wear classification. Moreover, to make a comparison, empirical selection method and four kinds of grid search algorithms are also used to select the support vector machine parameters. At the same time, cross validation is utilized to improve the robustness of the classifier evaluation. The results of analysis and comparisons show that the classification accuracy of differential evolution-support vector machine is higher than empirical selection-support vector machine. Moreover, the time consumption of differential evolution-support vector machine classifier is 5 to 12 times less than that of grid search-support vector machine.

Tool Wear Monitoring Using Microphone Signals and Recurrence Quantification Analysis when Drilling Composites

2013 ◽  
Vol 711 ◽  
pp. 239-244 ◽  
Author(s):  
Eshetu D. Eneyew ◽  
M. Ramulu
Keyword(s):  
Condition Monitoring ◽  
Cutting Tool ◽  
Flank Wear ◽  
Recurrence Quantification Analysis ◽  
Tool Condition Monitoring ◽  
Monitoring Method ◽  
Online Tool ◽  
Tool Condition ◽  
Quantification Analysis

The quality of the hole produced during the drilling of composite materials is one of the controlling factors for the resulting joint strength and integrity of the structural component. Quality of the hole depends on the condition of the cutting tool. Continuous cutting tool condition monitoring method is vital to accomplish the desired hole quality. To address this concern, an online tool condition monitoring technique using a simple audio microphone as a sensor is developed and Recurrence Quantification Analysis (RQA) methodology was used as a signal analysis tool to predict the tool condition in terms of flank wear. A series of experimental drilling operation was carried out on uni-directional carbon fiber reinforced plastic (CFRP) composite. It was found that the amplitude of the microphone signal decreases with the increase of the tool flank wear. In addition, from the selected eight RQA output variables, six of them show an increasing trend with the increase of the measured flank wear, whereas, two of them show a decreasing trend with the increase of tool wear. The same trend has been observed in both set of experiments. These results demonstrate that, this novel approach is an effective and economical online tool condition monitoring method.

scholarly journals State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

Aerospace ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 335
Author(s):  
Wei Dai ◽  
Kui Liang ◽  
Bin Wang
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Convolutional Neural Network ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Manufacturing Processes ◽  
State Monitoring ◽  
Monitoring Method ◽  
Tool Condition ◽  
Aerospace Manufacturing

In the aerospace manufacturing field, tool conditions are essential to ensure the production quality for aerospace parts and reduce processing failures. Therefore, it is extremely necessary to develop a suitable tool condition monitoring method. Thus, we propose a tool wear process state monitoring method for aerospace manufacturing processes based on convolutional neural networks to recognize intermediate abnormal states in multi-stage processes. There are two innovations and advantages of the proposed approach: one is that the criteria for judging abnormal conditions are extended, which is more useful for practical application. The other is that the proposed approach solved the influence of feature-to-recognition stability. Firstly, the tool wear level was divided into different state modes according to the probability density interval based on the kernel density estimation (KDE), and the corresponding state modes were connected to obtain the point-to-point control limit. Then, the state recognition model based on a convolutional neural network (CNN) was developed, and the sensitivity of the monitoring window was considered in the model. Finally, open-source datasets were used to verify the feasibility of the proposed method, and the results demonstrated the applicability of the proposed method in practice for tool condition monitoring.

Tool Condition Monitoring Method Based on Generative Adversarial Network for Data Augmentation

2021 ◽  
Author(s):  
Yongqing Wang ◽  
Mengmeng Niu ◽  
Kuo Liu ◽  
Honghui Wang ◽  
Mingrui Shen ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Data Augmentation ◽  
Tool Condition Monitoring ◽  
Generative Adversarial Network ◽  
Monitoring Method ◽  
Data Set ◽  
Learning Network ◽  
Adversarial Network ◽  
Tool Condition ◽  
Deep Learning Network

Abstract In the process of parts processing, due to the real working conditions and data acquisition equipment, the collected working data of tools are actually limited. Meanwhile, the tool usually works in the normal state, so it is prone to cause the problem of unbalanced data set, which restricts the accuracy of tool condition monitoring. Aiming at this problem, this paper proposes a tool condition monitoring method based on generative adversarial network (GAN) for data augmentation. Specifically, first collect original samples data during processing in different tool conditions, then the collected sample data is input into GAN, and the generator of GAN can generate new samples which has similar distribution with original samples from tool condition signals data, finally the real samples and generated samples are combined to train deep learning network to predict tool conditions. Experimental results show that the proposed method can significantly improve the accuracy of tool condition monitoring. This paper compares and visualizes the impact of the training data set on the classification ability of the deep learning network model. In addition, some traditional methods are used for comparison, and F1 measure is introduced to evaluate the quality of the results. The results show that this method is better than the Adaptive Synthetic Sampling (Adasyn), add-noise, and resampling.

scholarly journals Transfer of Process References between Machine Tools for Online Tool Condition Monitoring

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 282
Author(s):  
Berend Denkena ◽  
Benjamin Bergmann ◽  
Tobias H. Stiehl
Keyword(s):  
Condition Monitoring ◽  
Machine Tools ◽  
Tool Condition Monitoring ◽  
New Method ◽  
False Alarms ◽  
Transfer Of Knowledge ◽  
Monitoring Method ◽  
Detection Rates ◽  
General Process ◽  
Tool Condition

Process and tool condition monitoring systems are a prerequisite for autonomous production. One approach to monitoring individual parts without complex cutting simulations is the transfer of knowledge among similar monitoring scenarios. This paper introduces a novel monitoring method which transfers monitoring limits for process signals between different machine tools. The method calculates monitoring limits statistically from cutting processes carried out on one or more similar machines. The monitoring algorithm aims to detect general process anomalies online. Experiments comprise face-turning operations at five different lathes, four of which were of the same model. Results include the successful transfer of monitoring limits between machines of the same model for the detection of material anomalies. In comparison to an approach based on dynamic time warping (DTW) and density-based spatial clustering of applications with noise (DBSCAN), the new method showed fewer false alarms and higher detection rates. However, for the transfer between different models of machines, the successful application of the new method is limited. This is predominantly due to limitations of the employed process component isolation and differences between machine models in terms of signal properties as well as execution speed.

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