scholarly journals Milling Force Coefficients-based Tool Wear Monitoring for Variable Parameters Milling

2021 ◽  
Author(s):  
Tianhang Pan ◽  
Jun Zhang ◽  
Xing Zhang ◽  
Wanhua Zhao ◽  
Huijie Zhang ◽  
...  
Keyword(s):  
Tool Wear ◽  
Support Vector ◽  
Machining Accuracy ◽  
Force Model ◽  
Machining Parameters ◽  
Tool Wear Monitoring ◽  
Milling Force ◽  
Force Coefficients ◽  
Milling Parameters ◽  
Wear Monitoring

Abstract Tool wear is an important factor that affects the aeronautical structural parts' quality and machining accuracy in the milling process. It is essential to monitor the tool wear in titanium alloy machining. The traditional tool wear features such as root mean square (RMS), kurtosis, and wavelet packet energy spectrum are related to not only the tool wear status but also to the milling parameters, thus monitoring the tool wear status only under fixed milling parameters. This paper proposes a new method of online monitoring of tool wear using milling force coefficients. The instantaneous cutting force model is used to extract the milling force coefficients which are independent of milling parameters. The principal component analysis (PCA) algorithm is used to fuse the milling force coefficients. Furthermore, support vector machine (SVM) model is used to monitor tool wear states. Experiments with different machining parameters were conducted to verify the effectiveness of this method used for tool wear monitoring. The results show that compared to traditional features, the milling force coefficients are not dependent on the milling parameters, and using milling force coefficients can effectively monitor the transition point of cutters from normal wear to severe wear (tool failure).

Tool Wear Monitoring in Milling Processes Based on Time-Frequency Analysis of Acoustic Emission

2011 ◽  
Vol 141 ◽  
pp. 574-577
Author(s):  
Lu Zhang ◽  
Guo Feng Wang ◽  
Xu Da Qin ◽  
Xiao Liang Feng
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Frequency Domain ◽  
Support Vector ◽  
Tool Wear Monitoring ◽  
Machining Processes ◽  
Time Frequency ◽  
Svm Model ◽  
The Status ◽  
Wear Monitoring

Tool wear monitoring plays an important role in the automatic machining processes. Therefore, it is necessary to establish a reliable method to predict tool wear status. In this paper, features of acoustic emission (AE) extracted from time-frequency domain are integrated with force features to indicate the status of tool wear. Meanwhile, a support vector machine (SVM) model is employed to distinguish the tool wear status. The result of the classification of different tool wear status proved that features extracted from time-frequency domain can be the recognize-features of high recognition precision.

Application of Audible Sound Signals for Tool Wear Monitoring and Workpiece Hardness Identification in Gear Milling Using Machine Learning Techniques

2017 ◽  
Author(s):  
Achyuth Kothuru ◽  
Sai Prasad Nooka ◽  
Patricia Iglesias Victoria ◽  
Rui Liu
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Learning Algorithm ◽  
Machining Process ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Process Conditions ◽  
Tool Wear Monitoring ◽  
Audible Sound ◽  
Wear Monitoring

The machining process monitoring, especially the tool wear monitoring, is very critical in modern automated gear machining environment which needs instant detection of cutting tool state and/or process conditions, quick final diagnosis and appropriate actions. It has been realized that the non-uniform hardness of the workpiece material due to the improper heat treatment can cause expedited tool wear and unexpected tool breakage, which greatly increases difficulties and complexities in monitoring the tool conditions in gear cutting. This paper provides a solution to detect the wear conditions of the gear milling cutter in the cutting of workpiece materials with hardness variations using the audible sound signals. In this study, cutting tools and workpieces are prepared to have different flank wear classes and hardness variations respectively. A series of gear milling experiments are operated with a broad range of cutting conditions to collect sound signals. A machine learning algorithm that incorporates support vector machine (SVM) approach coupled with the application of time and frequency domain analysis is developed to correlate observed sound signals’ signatures to specified tool wear classes and workpiece hardness levels. The performance evaluation results of the proposed monitoring system have shown accurate predictions in detecting tool wear conditions and workpiece hardness variations from the sound signals in gear milling.

scholarly journals Tool Wear Monitoring for Complex Part Milling Based on Deep Learning

2020 ◽  
Vol 10 (19) ◽  
pp. 6916 ◽  
Author(s):  
Xiaodong Zhang ◽  
Ce Han ◽  
Ming Luo ◽  
Dinghua Zhang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Tool Wear ◽  
Learning Model ◽  
Cutting Condition ◽  
Force Model ◽  
Tool Wear Monitoring ◽  
Monitoring Method ◽  
Complex Part ◽  
Wear Monitoring

Tool wear monitoring is necessary for cost reduction and productivity improvement in the machining industry. Machine learning has been proven to be an effective means of tool wear monitoring. Feature engineering is the core of the machining learning model. In complex parts milling, cutting conditions are time-varying due to the variable engagement between cutting tool and the complex geometric features of the workpiece. In such cases, the features for accurate tool wear monitoring are tricky to select. Besides, usually few sensors are available in an actual machining situation. This causes a high correlation between the hand-designed features, leading to the low accuracy and weak generalization ability of the machine learning model. This paper presents a tool wear monitoring method for complex part milling based on deep learning. The features are pre-selected based on cutting force model and wavelet packet decomposition. The pre-selected cutting forces, cutting vibration and cutting condition features are input to a deep autoencoder for dimension reduction. Then, a deep multi-layer perceptron is developed to estimate the tool wear. The dataset is obtained with a carefully designed varying cutting depth milling experiment. The proposed method works well, with an error of 8.2% on testing samples, which shows an obvious advantage over the classic machine learning method.

scholarly journals Tool wear monitoring in roughing and finishing processes based on machine internal data

2021 ◽  
Author(s):  
Tiandong Xi ◽  
Igor Medeiros Benincá ◽  
Sebastian Kehne ◽  
Marcel Fey ◽  
Christian Brecher
Keyword(s):  
Tool Wear ◽  
Interdisciplinary Collaboration ◽  
Numerical Control ◽  
Shop Floor ◽  
Force Model ◽  
Production Environment ◽  
Tool Wear Monitoring ◽  
Statistical Process ◽  
First Case ◽  
Wear Monitoring

AbstractData analytics plays a significant role in the realization of Industry 4.0. By generating context-related persistent datasets, every manufacturing process in real production becomes an experiment. The vision of Internet of Production (IoP) is to enable real-time diagnosis and prediction in smart productions by acquiring datasets seamlessly from different data silos. This requires interdisciplinary collaboration and domain-specific expertise. In this paper, we present a novel tool wear monitoring system for milling process developed in the context of IoP. This system is based on high-frequency data from the numerical control of the production machine without additional sensors. The novelty of this paper lies in the introduction of virtual workpiece quality and fusion of multiple build-in sensor signals and a force model as decision support. This bridges the time gap between quality inspection and production at the shop floor level, establishes an automated statistical process control system, and provides a more plausible prediction of tool lifetime. The monitoring of two different milling processes in a real production environment is exemplary demonstrated in this paper. The first case is a face roughing process with the aim of rapidly removing large amounts of material. The second case is a face finishing operation that follows roughing and aims to achieve the desired surface quality.

Tool Wear Monitoring in Milling Processes Based on Cointegration Modeling

2010 ◽  
Vol 34-35 ◽  
pp. 1746-1751 ◽  
Author(s):  
Yin Hu Cui ◽  
Guo Feng Wang ◽  
Dong Biao Peng
Keyword(s):  
Tool Wear ◽  
Milling Process ◽  
Support Vector ◽  
Tool Wear Monitoring ◽  
Machining Processes ◽  
Machine Model ◽  
Force Signal ◽  
Frequency Components ◽  
Wear Monitoring ◽  
Cointegration Relationship

Tool wear monitoring plays an important role in the automatic machining processes. Therefore, a reliable method is necessary for practical application. In this paper, a new method based on cointegration theory was introduced to extract features from the cutting force signal in the milling process. Cointegration relationship between cutting forces of different directions could be found and the corresponding cointegration vector could also be calculated. In order to improve the reliability of pattern recognition, the cointegration vectors combined with the energy of the high-frequency components of the acoustic emission signals were used as features. Once all the features are extracted, they were trained and tested through a support vector machine model. Experiments were performed to verify this method and the results showed that it could efficiently recognize the tool wear status.

Tool wear monitoring in micromilling using Support Vector Machine with vibration and sound sensors

2021 ◽  
Vol 67 ◽  
pp. 137-151
Author(s):  
Milla Caroline Gomes ◽  
Lucas Costa Brito ◽  
Márcio Bacci da Silva ◽  
Marcus Antônio Viana Duarte
Keyword(s):  
Support Vector Machine ◽  
Tool Wear ◽  
Support Vector ◽  
Tool Wear Monitoring ◽  
Wear Monitoring

Research on Integrated Architecture for Tool Wear Monitoring System of CNC Machine Center

2011 ◽  
Vol 141 ◽  
pp. 429-433
Author(s):  
Qian Ning ◽  
Qing Jian Liu ◽  
Lu Liu
Keyword(s):  
Tool Wear ◽  
Monitoring System ◽  
Integrated System ◽  
Support Vector ◽  
Tool Wear Monitoring ◽  
Cnc Machine ◽  
Machining Processes ◽  
Trend Prediction ◽  
Wear Monitoring ◽  
Estimation System

Cutting tool wear degrades the machining quality and reliability of CNC machine tool significantly in machining processes. Methods for monitoring tool wear online are therefore crucial to implement optimization of the cutting parameters and improvement of manufacturing processes performance. An intelligent tool wear estimation system that integrates condition monitoring, pattern recognition and trend prediction has been presented in this paper. The raw signals contain useful information from several sensors measuring process variables are acquired and analyzed utilizing monitoring units. The obtained feature elements are processed using support vector machine algorithm to identify tool wear degree. The implementation mode and specific functions of the integrated system architecture is detailed described. The experimental results show that the integrated tool wear monitoring system is feasible and effective.

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