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A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends

Mustafa Kuntoğlu, Abdullah Aslan, Danil Yurievich Pimenov, Üsame Ali Usca, Emin Salur, Munish Kumar Gupta, Tadeusz Mikolajczyk, Khaled Giasin, Wojciech Kapłonek, Shubham Sharma
2020 Sensors  
As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.  ...  The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness.  ...  [60] implemented neural networks and hidden Markov models as a comparative study in monitoring of TW.  ... 
doi:10.3390/s21010108 pmid:33375340 fatcat:hzfp6uj4vjhd3fvdrlftnz6ybi

A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques

Lorenzo Colantonio, Lucas Equeter, Pierre Dehombreux, François Ducobu
2021 Machines  
Machine (SVM), Self-Organizing Map (SOM), Hidden Markov Model (HMM), and Convolutional Neural Network (CNN).  ...  In turning operations, the wear of cutting tools is inevitable.  ...  125 [32] “ Hidden Markov Model-based tool wear monitoring in turning” Wang et al., 2002 HMM 20 [46] “ A comparative evaluation of neural networks and hidden Markov models for  ... 
doi:10.3390/machines9120351 fatcat:rpvai6pcdbdxhjracyrrbgusci

Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model

Guofeng Wang, Yinwei Yang, Zhimeng Li
2014 Sensors  
Currently, many models such as support vector machine ( SVM) [1,2], artificial neural networks (ANN) [3-6], conditional random field (CRF) [7], hidden Markov model (HMM) [8,9], etc. have been proposed  ...  Wang and Feng [7] proposed a linear chain conditional random field (CRF) model and utilized it for online tool condition monitoring.  ...  Acknowledgments This project is supported by National Natural Science Foundation of China (51175371), National Science and Technology Major Projects (2014ZX04012-014) and Tianjin Science and Technology  ... 
doi:10.3390/s141121588 pmid:25405514 pmcid:PMC4279551 fatcat:ugefulvjjvd3jhqdnwxz52xyai

Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network

Qun Wang, Hengsheng Wang, Liwei Hou, Shouhua Yi
2021 Applied Sciences  
neural networks, and monitoring network architecture and modeling methods.  ...  This paper can be a guide for the researchers and manufacturers in the area of tool wear monitoring for explaining the latest trends and requirements.  ...  [49] created a semi-hidden Markov model considering tool wear continuity index to monitor tool wear. This model reduced the computational cost and improved the recognition accuracy. Hua et al.  ... 
doi:10.3390/app112412041 fatcat:opqedwno7ffvreoqmugiommceu

Tool Wear Monitoring for Complex Part Milling Based on Deep Learning

Xiaodong Zhang, Ce Han, Ming Luo, Dinghua Zhang
2020 Applied Sciences  
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.  ...  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.  ...  Acknowledgments: Thanks are due to Tao Li for assistance with the experiments and data processing. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app10196916 fatcat:y75or5k72jenhpyw44tyqd7gdq

On-line Cutting Tool Condition Monitoring in Machining Processes Using Artificial Intelligence [chapter]

Antonio J., Rubn Morales-Menndez, J.R. Alique
2008 Robotics Automation and Control  
end milling process in HSM: (1) Artificial Neural Network, and (2) Hidden Markov Models.  ...  Two techniques will be applied using Artificial Neural Networks and Hidden Markov Models.  ... 
doi:10.5772/5833 fatcat:ng2h52rplbhtnjyf6qdsu2nxyq

Tool Health Monitoring Using Airborne Acoustic Emission and Convolutional Neural Networks: A Deep Learning Approach

Muhammad Arslan, Khurram Kamal, Muhammad Fahad Sheikh, Mahmood Anwar Khan, Tahir Abdul Hussain Ratlamwala, Ghulam Hussain, Mohammed Alkahtani
2021 Applied Sciences  
The paper provides a novel approach to monitoring the tool health of a computer numeric control (CNC) machine for a turning process using airborne acoustic emission (AE) and convolutional neural networks  ...  The proposed approach provides promising results for tool health monitoring of a turning process using airborne acoustic emission.  ...  Acknowledgments: The authors are thankful to the National University of Sciences and Technology, University of Management and Technology and GIK Institute for providing necessary technical and financial  ... 
doi:10.3390/app11062734 fatcat:6irunfuxqfco7es5ncqz2rbcai

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

Rui Zhao, Ruqiang Yan, Jinjiang Wang, Kezhi Mao
2017 Sensors  
Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data.  ...  As one kind of neural network, LSTM incorporates representation learning and model training together, which require no additional domain knowledge.  ...  All authors have read and approved the final manuscript. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s17020273 pmid:28146106 pmcid:PMC5336098 fatcat:iqglywvrl5c3jnyszwl37jiabi

Tool Wear Prediction in Milling: A Comparative Analysis Based on Machine Learning and Deep Learning Approaches

Pooja V Kamat, Satish Kumar, Shruti Patil, Rekha Sugandhi, Anish Nargund
2022 International Journal of Computing and Digital Systems  
This paper presents a comparative approach to tool wear monitoring using the clustering machine learning technique of K-Nearest Neighbour (k-NN) and deep learning technique of Convolutional Neural Network  ...  The milling machine's cutting tool is a vital asset; its breakdown results in unplanned downtime, which reduces industrial efficiency.Tool-Wear Monitoring(TWM) is one of the primary goals of the manufacturing  ...  Also, the models can be reused as part of transfer learning for monitoring tool wear in case of other manufacturing processes such as drilling and turning.  ... 
doi:10.12785/ijcds/110112 fatcat:uvmzzaoehfd5boxzs245q4ilxe

Intelligent Turning Tool Monitoring with Neural Network Adaptive Learning

Maohua Du, Peixin Wang, Junhua Wang, Zheng Cheng, Shensong Wang
2019 Complexity  
The results show that the correct recognition rate of BP network model after samples training is 92.59%, which can more accurately and intelligently monitor the tool wear state.  ...  Tool state monitoring is a key technology in intelligent manufacturing. But it is still in a research stage and lacks general adaptability for different machining conditions.  ...  Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper.  ... 
doi:10.1155/2019/8431784 fatcat:kghbsgqqxbdgvh66ys5vr7n2nq

Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs)

Huseyin M. Ertunc, Kenneth A. Loparo, Hasan Ocak
2001 International journal of machine tools & manufacture  
Monitoring of tool wear condition for drilling is a very important economical consideration in automated manufacturing.  ...  These techniques use hidden Markov models (HMMs), commonly used in speech recognition.  ...  Acknowledgements The authors would like to thank John Gorse and Karl Ziegler from Eaton MTC for providing technical support and access to their laboratoy for the experimental part of our work.  ... 
doi:10.1016/s0890-6955(00)00112-7 fatcat:mgkofohqxfh3viysdx5npn5sli

Research on Fusion Monitoring Method of Turning Cutting Tool Wear Based on Particle Filter Algorithm

Jun Wang, Tingting Zhou
2021 IEEE Access  
Compared with 2D CNN, one-dimensional Convolution Neural Network (1D CNN) is suitable for the pattern recognition in the tool wear monitoring.  ...  In recent years, deep learning approaches represented by the Convolution Neural Network (CNN) has been well developed and gradually applied for the tool wear monitoring.  ... 
doi:10.1109/access.2021.3086667 fatcat:zwre3ujs6fb4bmbbvzbqz7cope

Analysis of Spindle AE Signals and Development of AE-Based Tool Wear Monitoring System in Micro-Milling

Bing-Syun Wan, Ming-Chyuan Lu, Shean-Juinn Chiou
2022 Journal of Manufacturing and Materials Processing  
In analyzing both signals on tool wear monitoring in micro-cutting, a feature selection algorithm and hidden Markov model (HMM) were also developed to verify the effect of both signals on the monitoring  ...  and analyzed in this study for micro tool wear monitoring.  ...  [27] presented work on tool wear estimation during the turning of Inconel 718 by the fusion of cutting force, AE, and vibration signal features along with artificial-neural-network-based machine learning  ... 
doi:10.3390/jmmp6020042 fatcat:a6iysq3gyvfr3epigm52p7vrx4

CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks

D.A. Tobon-Mejia, K. Medjaher, N. Zerhouni
2012 Mechanical systems and signal processing  
The proposed method is applied on a benchmark of condition monitoring data gathered during several cuts of a CNC tool. Simulation results are obtained and discussed at the end of the paper.  ...  The present paper is a contribution on the assessment of the health condition of a Computer Numerical Control (CNC) tool machine and the estimation of its Remaining Useful Life (RUL).  ...  Mixture of Gaussians Hidden Markov Models The MoG-HMM is primarily a Hidden Markov Model.  ... 
doi:10.1016/j.ymssp.2011.10.018 fatcat:vekikyzzjbgqtefo3o2zyopuwm

Soft computing techniques for surface roughness prediction in hard turning: a literature review

Kang He, Mengdi Gao, Zhuanzhe Zhao
2019 IEEE Access  
First, the characteristics of hard turning and surface roughness are introduced, and a framework of the soft computing techniques is presented. Then, the three key areas are surveyed thoroughly.  ...  The objective of this paper is to survey the current state of the soft computing techniques for surface roughness prediction in hard turning.  ...  [52] proposed a hybrid model to evaluate surface roughness in hard turning using a Bayesian inference-based hidden Markov model and least-squares support vector machine (HMM-SVM).  ... 
doi:10.1109/access.2019.2926509 fatcat:erhafaj6urc3vny7hjqu3epume
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