scholarly journals Digital Twin-Driven Tool Wear Monitoring and Predicting Method for the Turning Process

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1438
Author(s):  
Kejia Zhuang ◽  
Zhenchuan Shi ◽  
Yaobing Sun ◽  
Zhongmei Gao ◽  
Lei Wang
Keyword(s):  
Tool Wear ◽  
Real Time ◽  
Cutting Tool ◽  
Cutting Process ◽  
Turning Process ◽  
Digital Twin ◽  
Wear Monitoring ◽  
Key Enabling Technologies ◽  
Tool Wear Classification

Accurate monitoring and prediction of tool wear conditions have an important influence on the cutting performance, thereby improving the machining precision of the workpiece and reducing the production cost. However, traditional methods cannot easily achieve exact supervision in real time because of the complexity and time-varying nature of the cutting process. A method based on Digital Twin (DT), which establish a symmetrical virtual tool system matching exactly the actual tool system, is presented herein to realize high precision in monitoring and predicting tool wear. Firstly, the framework of the cutting tool system DT is designed, and the components and operations rationale of the framework are detailed. Secondly, the key enabling technologies of the framework are elaborated. In terms of the cutting mechanism, a virtual cutting tool model is built to simulate the cutting process. The modifications and data fusion of the model are carried out to keep the symmetry between physical and virtual systems. Tool wear classification and prediction are presented based on the hybrid-driven method. With the technologies, the physical–virtual symmetry of the DT model is achieved to mapping the real-time status of tool wear accurately. Finally, a case study of the turning process is presented to verify the feasibility of the framework.

A Review on Signal Acquisition Methods for Tool Wear Monitoring in Turning Process

2014 ◽  
Vol 984-985 ◽  
pp. 83-93
Author(s):  
D. Rajeev ◽  
D. Dinakaran ◽  
Shanmugam Satishkumar ◽  
Anselm W.A. Lenin
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Economic Loss ◽  
Signal Acquisition ◽  
Tool Wear Monitoring ◽  
Turning Process ◽  
Tool Condition ◽  
On Line ◽  
Wear Monitoring ◽  
Machine Utilization

On-line monitoring of tool wear in turning is vital to increase machine utilization as scrapped components, machine tool breakage and unscheduled downtime result from worn tool usage cause huge economic loss. Several techniques have been developed for monitoring wear levels on the cutting tool on-line. Keeping in to account the difficulties encountered during the implementation of tool condition monitoring (TCM). The signal acquisition is one of the key elements used during the implementation of TCM. This paper provides an in depth coverage of various signal acquisition methods used in TCM.

scholarly journals Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach

2018 ◽  
Vol 2 (4) ◽  
pp. 72 ◽  
Author(s):  
German Terrazas ◽  
Giovanna Martínez-Arellano ◽  
Panorios Benardos ◽  
Svetan Ratchev
Keyword(s):  
Tool Wear ◽  
High Speed ◽  
Big Data Analytics ◽  
Design Development ◽  
Online Tool ◽  
Overall Evaluation ◽  
Intelligent Information ◽  
Key Enabling Technologies ◽  
Processing Techniques ◽  
Tool Wear Classification

The new generation of ICT solutions applied to the monitoring, adaptation, simulation and optimisation of factories are key enabling technologies for a new level of manufacturing capability and adaptability in the context of Industry 4.0. Given the advances in sensor technologies, factories, as well as machine tools can now be sensorised, and the vast amount of data generated can be exploited by intelligent information processing techniques such as machine learning. This paper presents an online tool wear classification system built in terms of a monitoring infrastructure, dedicated to perform dry milling on steel while capturing force signals, and a computing architecture, assembled for the assessment of the flank wear based on deep learning. In particular, this approach demonstrates that a big data analytics method for classification applied to large volumes of continuously-acquired force signals generated at high speed during milling responds sufficiently well when used as an indicator of the different stages of tool wear. This research presents the design, development and deployment of the system components and an overall evaluation that involves machining experiments, data collection, training and validation, which, as a whole, has shown an accuracy of 78 % .

scholarly journals Development of turning process digital twin based on machine learning

2021 ◽  
pp. 32-41
Author(s):  
D. A. Rastorguev ◽  
◽  
A. A. Sevastyanov ◽  
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Networks ◽  
Information Technologies ◽  
Cutting Process ◽  
Turning Process ◽  
Force Load ◽  
Cnc Machine ◽  
Digital Twin ◽  
Artificial Neural

Today, manufacturing technologies are developing within the Industry 4.0 concept, which is the information technologies introduction in manufacturing. One of the most promising digital technologies finding more and more application in manufacturing is a digital twin. A digital twin is an ensemble of mathematical models of technological process, which exchanges information with its physical prototype in real-time. The paper considers an example of the formation of several interconnected predictive modules, which are a part of the structure of the turning process digital twin and designed to predict the quality of processing, the chip formation nature, and the cutting force. The authors carried out a three-factor experiment on the hard turning of 105WCr6 steel hardened to 55 HRC. Used an example of the conducted experiment, the authors described the process of development of the digital twin diagnostic module based on artificial neural networks. When developing a mathematical model for predicting and diagnosing the cutting process, the authors revealed higher accuracy, adaptability, and versatility of artificial neural networks. The developed mathematical model of online diagnostics of the cutting process for determining the surface quality and chip type during processing uses the actual value of the cutting depth determined indirectly by the force load on the drive. In this case, the model uses only the signals of the sensors included in the diagnostic subsystem on the CNC machine. As an informative feature reflecting the force load on the machine’s main motion drive, the authors selected the value of the energy of the current signal of the spindle drive motor. The study identified that the development of a digital twin is possible due to the development of additional modules predicting the accuracy of dimensions, geometric profile, tool wear.

scholarly journals Cutting Tool Wear Monitoring

2015 ◽  
Vol 15 (3) ◽  
pp. 380-384 ◽  
Author(s):  
Jan Madl ◽  
Michal Martinovsky
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Tool Wear Monitoring ◽  
Cutting Tool Wear ◽  
Wear Monitoring

Real-time estimation of machine cutting tool wear

2022 ◽  
pp. 1-14
Author(s):  
Zhi-An Shen ◽  
Jiangfeng Cheng ◽  
Chieh-Tse Tang ◽  
Chun-Liang Lin ◽  
Chia-Feng Juang
Keyword(s):  
Tool Wear ◽  
Real Time ◽  
Cutting Tool ◽  
Time Estimation ◽  
Cutting Tool Wear ◽  
Real Time Estimation

scholarly journals Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 809 ◽  
Author(s):  
Yiting Li ◽  
Qingsheng Xie ◽  
Haisong Huang ◽  
Qipeng Chen
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Detection Accuracy ◽  
Dense Network ◽  
Residual Network ◽  
Detection Model ◽  
Acceleration Sensors ◽  
Wear Monitoring ◽  
The Time Domain ◽  
Monitoring Algorithm

To accurately and efficiently detect tool wear values during production and processing activities, a new online detection model is proposed called the Residual Dense Network (RDN). The model is created with two main steps: Firstly, the time-domain signals for a cutting tool are obtained (e.g., using acceleration sensors); these signals are processed to denoise and segmented to provide a larger number of uniform samples. This processing helps to improve the robustness of the model. Secondly, a new deep convolutional neural network is proposed to extract features adaptively, by combining the idea of a recursive residual network and a dense network. Notably, this method is specifically tailored to the tool wear value detection problem. In this way, the limitations of traditional manual feature extraction steps can be avoided. The experimental results demonstrate that the proposed method is promising in terms of detection accuracy and speed; it provides a new way to detect tool wear values in practical industrial scenarios.

Knife-edge interferometry for cutting tool wear monitoring

2017 ◽  
Vol 50 ◽  
pp. 354-360 ◽  
Author(s):  
Seongkyul Jeon ◽  
Christopher K. Stepanick ◽  
Abolfazl A. Zolfaghari ◽  
ChaBum Lee
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Knife Edge ◽  
Tool Wear Monitoring ◽  
Cutting Tool Wear ◽  
Wear Monitoring

scholarly journals Research on On-line Tool Wear Monitoring Technology Based on GPR

2021 ◽  
Vol 252 ◽  
pp. 01046
Author(s):  
Shan Fan ◽  
Yi Huang ◽  
Haixia Zeng
Keyword(s):  
Tool Wear ◽  
Online Monitoring ◽  
Gaussian Process Regression ◽  
Force Sensor ◽  
Cutting Process ◽  
Tool Wear Monitoring ◽  
Monitoring Technology ◽  
On Line ◽  
Wear Monitoring ◽  
The Impact

At present, many kinds of sensors are used for on-line monitoring of cutting process, tool identification and timely replacement. However, most of the original monitoring signals extracted from the cutting process are time series signals, which contain too much process noise. As the signal noise is relatively low, it is difficult to establish a direct relationship with the tool wear. Therefore, how to obtain the effective information from the online monitoring signal and extract the characteristics that can directly reflect the tool wear from the complex original signal, so as to establish an effective and reliable tool wear monitoring system, is the key and difficult problem in the research of the online monitoring technology of tool wear. Firstly, an experimental platform based on the force sensor for on-line monitoring of tool wear was built, and the signal obtained by the force sensor was used to monitor the tool wear, and the feature information was extracted and fused. The innovation of the project lies in the use of Gaussian process regression (GPR) method to predict the tool wear, the use of feature dimensional rise technology, to reduce the impact of noise, on the premise of ensuring the prediction accuracy, improve the confidence interval of GPR prediction results, improve the stability and reliability of the monitoring process.

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