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A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends
2020
Sensors
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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
2021
Machines
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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
2014
Sensors
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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
2021
Applied Sciences
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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
2020
Applied Sciences
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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]
2008
Robotics Automation and Control
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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
2021
Applied Sciences
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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
2017
Sensors
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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
2022
International Journal of Computing and Digital Systems
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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
2019
Complexity
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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)
2001
International journal of machine tools & manufacture
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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
2021
IEEE Access
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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
2022
Journal of Manufacturing and Materials Processing
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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
2012
Mechanical systems and signal processing
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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
2019
IEEE Access
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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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