Machine-Learning- and Internet-of-Things-Driven Techniques for Monitoring Tool Wear in Machining Process: A Comprehensive Review
Abstract
:1. Introduction
1.1. Evaluation of Modern TCM
- Early Beginnings: 1950s–1970s
- 1950s: The concept of monitoring tool wear and its impact on machining quality began to gain attention. Researchers focused on understanding the basic wear mechanisms and their effects on the cutting process.
- 1960s: The first experimental studies were conducted to measure tool wear using direct methods, such as optical and microscopy techniques. These studies primarily focused on wear patterns in traditional machining processes like turning and milling.
- 1970s: The emergence of numerical control (NC) machines highlighted the need for more systematic approaches to monitor and manage tool wear. The initial efforts in this era involved simple manual inspections and scheduled maintenance, but the limitations of these approaches led to an increased interest in automated monitoring systems.
- 2.
- Introduction of Sensors and Signal Processing: 1980s–1990s
- 1980s: The development of sensors capable of measuring physical quantities such as force, vibration, and acoustic emissions led to the first generation of automated TCM systems. These sensors were integrated into machining systems to capture data that could be analyzed for signs of tool wear.
- Late 1980s–Early 1990s: Researchers began exploring the use of signal-processing techniques, such as fast Fourier transform (FFT), to analyze sensor data in real time. This marked the beginning of indirect monitoring methods, where the condition of the tool was inferred from patterns in the sensor signals.
- 1990s: The use of artificial intelligence (AI) techniques, including fuzzy logic and rule-based systems, was introduced to enhance decision-making processes in TCM. These early AI applications focused on interpreting sensor data and providing automated alerts for tool replacement.
- 3.
- Advancements in Machine Learning and Early IoT Concepts: 2000s
- Early 2000s: The integration of machine-learning algorithms, such as neural networks and SVMs, into TCM systems became more common. These algorithms were employed to predict tool wear based on historical data and real-time sensor inputs, offering more accurate and reliable monitoring.
- Mid-2000s: The concept of predictive maintenance started gaining traction, with machine-learning models being used to forecast tool failures before they occurred, thus reducing downtime and improving productivity. This era also saw the development of more sophisticated feature extraction techniques, both in time and frequency domains.
- Late 2000s: The idea of connecting machining systems to the Internet for remote monitoring began to surface, laying the groundwork for the IIoT. Early implementations focused on data collection and remote access, with limited real-time processing capabilities.
- 4.
- The Rise of IIoT and Advanced AI Techniques: 2010s
- 2010–2015: The rapid advancement in IoT technologies led to the widespread adoption of smart sensors and connected devices in manufacturing environments. IIoT platforms were developed to enable real-time data collection, processing, and analytics, paving the way for smart manufacturing.
- 2015–2018: Deep-learning techniques, particularly long short-term memory (LSTM) networks, were introduced to handle the complexity of time-series data generated by sensors. These techniques improved the accuracy of tool wear predictions and allowed for a more nuanced analysis of machining processes.
- Late 2010s: The integration of cloud computing with IIoT enabled large-scale data storage and processing, allowing manufacturers to leverage big data analytics for predictive maintenance and tool condition monitoring. This period also saw the emergence of edge computing, where data processing was moved closer to the source (e.g., sensors) to reduce latency and improve real-time decision-making.
- 5.
- Current Trends and Future Directions: 2020s–Present
- 2020–Present: Research has focused on enhancing the interoperability and scalability of TCM systems in Industry 4.0 and 5.0 environments. The combination of edge/fog computing, advanced AI models, and cybersecurity measures is being explored to create more robust and flexible monitoring systems.
- Emerging Focus Areas: With the increasing complexity of manufacturing systems, researchers are now looking into hybrid AI models that combine traditional machine learning with deep learning to handle diverse data sources. The application of transfer learning, where models trained in one domain are adapted to another, is also gaining interest.
- Future Directions: As Industry 5.0 emphasizes human–machine collaboration, future research may explore more intuitive TCM systems that integrate human expertise with AI-driven insights. The development of more sophisticated, self-adapting monitoring systems that can dynamically respond to changing machining conditions is another key area of interest.
2. Data Acquisition
3. TCM Signal and Data Processing
3.1. Signal Processing
3.2. Feature Extraction
3.3. Dimensionality Reduction
4. Advancements in ML and DL for TCM System
4.1. Machine Learning for TCM
4.2. Deep-Learning Models for TCM
4.3. Transfer Learning Models for TCM
- Domain mismatch: Transfer learning assumes that the source and target domains are similar. If there is a significant difference in data distribution or feature space between the two domains, the model may not perform well in the target domain.
- Negative transfer: In some cases, knowledge transferred from the source domain can harm the model’s performance in the target domain. This occurs when the source task is not sufficiently related to the target task, leading to poor generalization.
- Limited data in target domain: While transfer learning can help with limited data in the target domain, it still requires a sufficient amount of labeled data to fine-tune the model effectively. If the target domain has too few labeled examples, the model may not learn the target task adequately.
- Overfitting to the source domain: If the pre-trained model is overly specialized to the source domain, it may overfit and fail to generalize to the target domain, especially if the target domain data are sparse or significantly different.
- Computational complexity: Fine-tuning a pre-trained model, especially large deep-learning models, can be computationally expensive and time-consuming, requiring significant resources for retraining.
- Interpretability: Transfer learning models, particularly deep neural networks, can be complex and difficult to interpret, making it challenging to understand why certain features are transferred and how they influence the target task.
- Dependence on source task quality: The effectiveness of transfer learning heavily depends on the quality and relevance of the source task. If the source model is not well-trained or the task is not closely related to the target task, the benefits of transfer learning may be minimal.
- Hyperparameter tuning: Fine-tuning a pre-trained model often requires the careful adjustment of hyperparameters, such as learning rate, batch size, and regularization methods. Poor tuning can lead to suboptimal performance or convergence issues [99].
4.4. Long Short-Term Memory Networks
4.5. Scalability of ML Algorithms
4.6. Comparative Analysis of Algorithms for TCM
5. Industrial IoT and Its Application
5.1. IoT Structures for TCM
5.2. Interoperability of IoT Devices
5.3. Edge and Fog Computing
5.4. Possibilities of Industrial IoT Application
5.5. Virtual Machining and Its Application
- Simulation of tool wear: Virtual manufacturing models can simulate the wear and tear of cutting tools over time, helping to predict when a tool might fail or require maintenance. This helps in planning maintenance schedules and reducing unexpected downtimes.
- Process optimization: By using virtual simulations, manufacturers can optimize machining parameters (e.g., speed, feed rate, and depth of cut) to minimize tool wear and enhance tool performance. This reduces the need for costly and time-consuming physical trials.
- Testing new tool designs: Virtual manufacturing enables the testing of new tool designs in a simulated environment before they are produced and used in actual machining operations. This helps in refining the design for better performance and longevity.
- Cost reduction: Since virtual manufacturing relies on simulations, it reduces the need for expensive physical experiments and prototypes, saving time and resources.
- Predictive maintenance: By integrating virtual manufacturing with predictive maintenance strategies, manufacturers can use data from simulations to anticipate tool failures and schedule maintenance proactively, thereby extending tool life and improving efficiency.
- Real-time monitoring: Advanced virtual manufacturing systems can be integrated with real-time data from sensors and IoT devices to continuously monitor tool conditions and adjust machining processes dynamically to prevent excessive wear [124].
6. Challenges and Prospects
6.1. Challenges
6.2. Industries and Their Products Contributing to TCM Technology
- Sandvik Coromant
- CoroPlus Tool Guide and Tool Library: A digital solution that provides recommendations on cutting tools and tool assemblies. It assists in tool selection, improving efficiency and accuracy in the machining process [125].
- CoroPlus ProcessControl: A real-time monitoring and control system that helps in optimizing machining processes. It collects and analyzes data from various sensors integrated into the machining tools, ensuring optimal performance and tool longevity [126].
- 2.
- Kennametal
- ToolBOSS: An inventory management system that ensures the right tools are available when needed. It also tracks tool usage, helping to predict wear and schedule maintenance [129].
- 3.
- IScar Metals and Tooling
- Tool Advisor: An intelligent tool selection system that provides recommendations based on the material, operation, and machine. It helps in reducing setup times and improving machining quality [130].
- Smart Factory Solutions: A suite of digital manufacturing tools that includes monitoring systems to track the performance and condition of cutting tools in real time [131].
- 4.
- BIG KAISER Precision Tooling
- Electronic Wear Analyzer (EWA): A precision tool that monitors and adjusts cutting parameters automatically to maintain optimal tool conditions. It is used to reduce downtime and extend tool life [132].
- Digital Boring Heads: Equipped with digital readouts and connectivity features, these tools allow for precise adjustments and real-time monitoring, contributing to improved tool life and process stability [133].
- 5.
- DMG MORI
- CELOS: An integrated platform that connects machines to the digital environment, enabling the real-time monitoring and control of machining processes. It provides data-driven insights to optimize tool performance and maintenance schedules [134].
- DMG MORI Tool Monitoring System (TMS): This system monitors tool wear and breakage in real time, allowing for immediate corrective actions. It is integrated with the machine’s control system to provide a seamless monitoring experience [135].
- 6.
- Siemens
- MindSphere: An industrial IoT platform that connects products, plants, and systems to the digital world. It facilitates data-driven insights for tool condition monitoring and predictive maintenance [136].
- SINUMERIK Edge: A machine tool control system that integrates edge computing capabilities for the real-time monitoring and optimization of machining processes [137].
- 7.
- Hexagon Manufacturing Intelligence
- SFx Asset Management: A cloud-based solution that provides the real-time monitoring of machine and tool conditions. It helps manufacturers optimize tool usage and reduce downtime [138].
- PC-DMIS: A software solution for dimensional measurement that can also be integrated into tool condition monitoring systems to ensure tools remain within tolerances [139].
- 8.
- Marposs
- Tool Touch Verification (TTV): A system that monitors tool condition by verifying the tool’s geometry before and after machining. It helps in detecting wear and preventing tool failures.
- BLÚ: A modular monitoring system that collects data from multiple sensors in real time to provide insights into tool conditions and process stability [140].
- 9.
- Zoller
- TMS Tool Management Solutions: A comprehensive software solution that tracks tool usage, wear, and inventory. It integrates with CNC machines to provide real-time monitoring and predictive maintenance capabilities.
- smartCheck: A tool inspection device that measures tool geometry and condition with a high precision, ensuring tools meet required specifications before use [141].
- 10.
- Makino
- MPmax: A real-time machine and tool monitoring software that tracks tool performance, detects abnormalities, and provides predictive maintenance insights. It is designed to optimize tool life and reduce machine downtime [142].
6.3. Research Teams in the TCM Field
- United States
- Massachusetts Institute of Technology (MIT):Research Focus: MIT has been a leader in the development of advanced manufacturing technologies, including TCM. Their research includes the integration of the IoT and AI for real-time monitoring and predictive maintenance.
- University of California, Berkeley:Research Focus: Berkeley’s research includes the development of machine-learning algorithms for predictive maintenance in manufacturing systems, including TCM.
- 2.
- Germany
- Fraunhofer Institutes:Research Focus: Fraunhofer is a leading research organization in Germany that has made significant contributions to TCM, particularly in the development of sensor technologies and IoT-based monitoring systems.
- RWTH Aachen University:Research Focus: RWTH Aachen is known for its research in manufacturing technology, including advanced TCM systems that leverage AI and digital twins.
- 3.
- Japan
- University of Tokyo:Research Focus: The University of Tokyo has been at the forefront of research in TCM, focusing on the integration of machine learning and IoT in manufacturing.
- Tokyo Institute of Technology:Research Focus: Tokyo Tech has made advancements in the application of AI for the real-time monitoring and control of machining processes.
- 4.
- China
- Tsinghua University:Research Focus: Tsinghua has conducted extensive research on the integration of AI and big data in manufacturing, with a focus on predictive maintenance and TCM.
- Harbin Institute of Technology:Research Focus: This institution is known for its work on sensor fusion and machine-learning algorithms for TCM.
- 5.
- South Korea
- Korea Advanced Institute of Science and Technology (KAIST):Research Focus: KAIST is a leader in smart manufacturing research, including the development of advanced TCM systems using the IoT and AI.
- 6.
- United Kingdom
- University of Sheffield:Research Focus: The University of Sheffield is known for its research on advanced manufacturing technologies, including TCM and the use of AI for predictive maintenance.
- University of Nottingham:Research Focus: Nottingham has researched the application of machine learning for predictive maintenance in manufacturing, including TCM.
6.4. Future Trends
- The integration of edge/fog computing for real-time data processing which requires minimizing latency, optimizing bandwidth usage, and developing scalable edge computing solutions that can handle large volumes of data from numerous sensors.
- Obtaining small datasets in deep learning, because obtaining large labeled datasets for training deep-learning models remains a significant challenge in industrial settings.
- Interoperability and integration of IoT Devices with standardized protocols and interfaces for seamless integration in TCM systems has to be enhanced.
- The development of scalable machine-learning algorithms that can handle big data and complex sensor networks is essential.
- As an extension of the present review process, it is important to explore more on non-contact-type wear data acquisition methods like the acoustic emission and thermal imaging process
- The future industrial era will collaborate with humans to get the work done. Therefore, the product demand will also increase. To achieve this demand, an error-free production process is required. The onboard tool wear prediction system will help achieve zero-downtime production.
- The application of advanced sensor systems, such as AI-assisted sensors, is highly beneficial in meeting the requirements of Industry 4.0. Artificial intelligence methods represent cutting-edge technology and open new avenues in the context of Industry 5.0.
- Various AI methods, such as machine learning, deep learning, and artificial neural networks, will be incorporated into sensor design. These advancements will make CNC machine tool structures smarter compared to conventional machines.
- In the future, there is a need to develop low-cost, in-house sensor systems capable of smartly measuring machining responses at affordable prices.
7. Conclusions
- CNC Machining Operations: In this study, we found that CNC machines are critical in modern manufacturing, and the integrity of cutting tools is vital for the efficiency of these machines. Tool wear monitoring is essential to prevent machine downtime and ensure quality in production.
- Sensor Technologies: The review discusses various sensors like accelerometers, acoustic emission sensors, and cutting force sensors that are used to monitor the condition of tools. These sensors help in collecting real-time data that can be analyzed to predict tool wear and prevent failures.
- Signal-Processing Techniques: Advanced signal-processing techniques like fast Fourier transform, wavelet packet decomposition, and ensemble empirical mode decomposition are used to analyze signals from sensors. These techniques help in identifying patterns that correlate with tool wear.
- Machine Learning and IIoT Integration: The integration of industrial IoT and machine learning into TCM systems enable real-time monitoring and data analysis, providing operators with actionable insights to prevent tool failure and optimize machining processes.
Author Contributions
Funding
Conflicts of Interest
Nomenclature
AE | Acoustic Emission |
CNC | Computer Numerical Control |
CPS | Cyber-Physical Systems |
DAQ | Data Acquisition |
DFT | Discrete Fourier Transform |
DL | Deep Learning |
FFT | Fast Fourier Transform |
HTTP | Hypertext Transfer Protocol |
IIoT | Industrial Internet of Things |
ML | Machine Learning |
MQQT | Message Queuing Telemetry Transport |
OPCUA | Open Platform Communications Unified Architecture |
RMS | Root Mean Square |
STFT | Short-Time Fourier Transform |
SVM | Support Vector Machine |
TCM | Tool Condition Monitoring |
TL | Transfer Learning |
VM | Virtual Machining |
WPD | Wavelet Packet Decomposition |
References
- Industry 5.0 Manufacturing with Human Centricity Ericsson. Available online: https://www.ericsson.com/en/about-us/new-world-of-possibilities/imagine-possible-perspectives/industry5-0-human-centric-manufacturing/ (accessed on 9 July 2024).
- Kious, M.; Ouahabi, A.; Boudraa, M.; Serra, R.; Cheknane, A. Detection process approach of tool wear in high speed milling. Measurement 2010, 43, 1439–1446. [Google Scholar] [CrossRef]
- Xue, H.; Yang, K.; Yang, R. The Shape Characteristic Detection of Tool Breakage in Milling Operations. Int. J. Mach. Tools Manuf. 1997, 37, 1651–1660. [Google Scholar]
- Thakre, A.A.; Lad, A.V.; Mala, K. Measurements of tool wear parameters using machine vision system. Model. Simul. Eng. 2019, 2019, 1876489. [Google Scholar] [CrossRef]
- Kuntoğlu, M.; Aslan, A.; Pimenov, D.Y.; Usca, Ü.A.; Salur, E.; Gupta, M.K.; Mikolajczyk, T.; Giasin, K.; Kapłonek, W.; Sharma, S. A review of indirect tool condition monitoring systems and decision-making methods in turning: Critical analysis and trends. Sensors 2021, 21, 108. [Google Scholar] [CrossRef]
- Mohanraj, T.; Shankar, S.; Rajasekar, R.; Sakthivel, N.R.; Pramanik, A. Tool condition monitoring techniques in milling process-a review. J. Mater. Res. Technol. 2020, 9, 1032–1042. [Google Scholar] [CrossRef]
- Mohamed, A.; Hassan, M.; M’saoubi, R.; Attia, H. Tool Condition Monitoring for High-Performance Machining Systems—A Review. Sensors 2022, 22, 2206. [Google Scholar] [CrossRef] [PubMed]
- Industrial IoT Platform Elements for IoT|CONTACT Software. Available online: https://www.contact-software.com/en/products/industrial-iot-platform-elements-for-iot/ (accessed on 5 July 2024).
- Liu, M.-K.; Tran, Q.M.; Qui, Y.-W.; Chung, C.-H. Chatter Detection in Milling Process Based on Time-Frequency Analysis. 2017. Available online: https://asmedigitalcollection.asme.org/MSEC/proceedings-abstract/MSEC2017/50725/V001T02A025/268554 (accessed on 9 March 2024).
- Tran, M.Q.; Liu, M.K.; Elsisi, M. Effective multi-sensor data fusion for chatter detection in milling process. ISA Trans 2022, 125, 514–527. [Google Scholar] [CrossRef]
- Chung, C.; Tran, M.Q.; Liu, M.K. Estimation of Process Damping Coefficient Using Dynamic Cutting Force Model. Int. J. Precis. Eng. Manuf. 2020, 21, 623–632. [Google Scholar] [CrossRef]
- Srinivasan, V.; Eswaran, C.; Sriraam, A.N. Artificial neural network based epileptic detection using time-domain and frequency-domain features. J. Med. Syst. 2005, 29, 647–660. [Google Scholar] [CrossRef]
- Zhou, Y.; Xue, W. A multisensor fusion method for tool condition monitoring in milling. Sensors 2018, 18, 3866. [Google Scholar] [CrossRef]
- Zhang, X.Y.; Lu, X.; Wang, S.; Wang, W.; Li, W.D. A multi-sensor based online tool condition monitoring system for milling process. Procedia CIRP 2018, 72, 1136–1141. [Google Scholar] [CrossRef]
- Deo, T.Y.; Patange, A.D.; Pardeshi, S.S.; Jegadeeshwaran, R.; Khairnar, A.N.; Khade, H.S. A White-Box SVM Framework and its Swarm-Based Optimization for Supervision of Toothed Milling Cutter through Characterization of Spindle Vibrations. arXiv 2021, arXiv:2112.08421v1. [Google Scholar]
- Naveen Venkatesh, S.; Arun Balaji, P.; Elangovan, M.; Annamalai, K.; Indira, V.; Sugumaran, V.; Mahamuni, V.S. Transfer Learning-Based Condition Monitoring of Single Point Cutting Tool. Comput. Intell. Neurosci. 2022, 2022, 3205960. [Google Scholar] [CrossRef] [PubMed]
- Bajaj, N.S.; Patange, A.D.; Jegadeeshwaran, R.; Kulkarni, K.A.; Ghatpande, R.S.; Kapadnis, A.M. A Bayesian Optimized Discriminant Analysis Model for Condition Monitoring of Face Milling Cutter Using Vibration Datasets. J. Nondestruct. Eval. Diagn. Progn. Eng. Syst. 2022, 5, 2. [Google Scholar] [CrossRef]
- Stuhr, B.; Liu, R. A Flexible Similarity-Based Algorithm for Tool Condition Monitoring. J. Manuf. Sci. Eng. Trans. ASME 2022, 144, 3. [Google Scholar] [CrossRef]
- You, Z.; Gao, H.; Li, S.; Guo, L.; Liu, Y.; Li, J. Multiple Activation Functions and Data Augmentation-Based Lightweight Network for In Situ Tool Condition Monitoring. IEEE Trans. Ind. Electron. 2022, 69, 13656–13664. [Google Scholar] [CrossRef]
- Patil, S.S.; Pardeshi, S.S.; Patange, A.D.; Jegadeeshwaran, R. Deep Learning Algorithms for Tool Condition Monitoring in Milling: A Review. J. Phys. Conf. Ser. 2021, 1969, 012039. [Google Scholar] [CrossRef]
- Schwendemann, S.; Amjad, Z.; Sikora, A. A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines. Comput. Ind. 2021, 125, 103380. [Google Scholar] [CrossRef]
- Pimenov, D.Y.; Gupta, M.K.; da Silva, L.R.R.; Kiran, M.; Khanna, N.; Krolczyk, G.M. Application of measurement systems in tool condition monitoring of Milling: A review of measurement science approach. Measurement 2022, 199, 111503. [Google Scholar] [CrossRef]
- Butler, Q.; Ziada, Y.; Stephenson, D.; Gadsden, S.A. Condition Monitoring of Machine Tool Feed Drives: A Review. J. Manuf. Sci. Eng. 2022, 144, 100802. [Google Scholar] [CrossRef]
- Serin, G.; Sener, B.; Ozbayoglu, A.M.; Unver, H.O. Review of tool condition monitoring in machining and opportunities for deep learning. Int. J. Adv. Manuf. Technol. 2020, 109, 953–974. [Google Scholar] [CrossRef]
- Nasir, V.; Sassani, F. A review on deep learning in machining and tool monitoring: Methods, opportunities, and challenges. Int. J. Adv. Manuf. Technol. 2021, 115, 2683–2709. [Google Scholar] [CrossRef]
- CNC Machine Tool Monitoring by AE Sensors Wikipedia. Available online: https://en.wikipedia.org/wiki/CNC_machine_tool_monitoring_by_AE_sensors (accessed on 29 August 2024).
- Condition Monitoring Wikipedia. Available online: https://en.wikipedia.org/wiki/Condition_monitoring (accessed on 29 August 2024).
- Bhuiyan, M.S.H.; Choudhury, I.A. Review of Sensor Applications in Tool Condition Monitoring in Machining. In Comprehensive Materials Processing; Elsevier: Amsterdam, The Netherlands, 2014; pp. 539–569. [Google Scholar] [CrossRef]
- Dimla, D.; Lister, P. On-line metal cutting tool condition monitoring. Int. J. Mach. Tools Manuf. 2000, 40, 739–768. [Google Scholar] [CrossRef]
- Zhong, Z.W.; Zhou, J.-H.; Win, Y.N. Correlation analysis of cutting force and acoustic emission signals for tool condition monitoring. In Proceedings of the 2013 9th Asian Control Conference (ASCC), Istanbul, Turkey, 23–26 June 2013; pp. 1–6. [Google Scholar]
- Rmili, W.; Serra, R. Tool Wear Monitoring in Turning Process Using Vibration Measurement In Proceedings of the 13th International Congress on Sound and Vibration, Vienna, Austria, 2–6 July 2006.
- Chen, X.; Li, B. Acoustic emission method for tool condition monitoring based on wavelet analysis. Int. J. Adv. Manuf. Technol. 2007, 33, 968–976. [Google Scholar] [CrossRef]
- Pai, P.S.; Rao, P.K.R. Acoustic emission analysis for tool wear monitoring in face milling. Int. J. Prod. Res. 2002, 40, 1081–1093. [Google Scholar] [CrossRef]
- Mu, X.; Xu, C. Tool Wear Monitoring of Acoustic Emission Signals from Milling Processes. In Proceedings of the 2009 First International Workshop on Education Technology and Computer Science, Wuhan, China, 7–8 March 2009; pp. 431–435. [Google Scholar]
- Kothuru, A.; Nooka, S.P.; Liu, R. Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling. Int. J. Adv. Manuf. Technol. 2017, 95, 3797–3808. [Google Scholar] [CrossRef]
- Liu, M.-K.; Tseng, Y.-H.; Tran, M.-Q. Tool wear monitoring and prediction based on sound signal. Int. J. Adv. Manuf. Technol. 2019, 103, 3361–3373. [Google Scholar] [CrossRef]
- Aliustaoglu, C.; Ertunc, H.M.; Ocak, H. Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system. Mech. Syst. Signal Process. 2008, 23, 539–546. [Google Scholar] [CrossRef]
- Zeng, Q.; Qin, Y. Multiscale Modeling of Hybrid Machining Processes. In Hybrid Machining Theory, Methods, and Case Studies; Elsevier: Amsterdam, Netherlands, 2018; pp. 269–298. [Google Scholar] [CrossRef]
- Qin, Y.; Wang, D.; Yang, Y. Integrated cutting force measurement system based on MEMS sensor for monitoring milling process. Microsyst. Technol. 2020, 26, 2095–2104. [Google Scholar] [CrossRef]
- Rizal, M.; Ghani, J.A.; Nuawi, M.Z.; Haron, C.H.C. An embedded multi-sensor system on the rotating dynamometer for real-time condition monitoring in milling. Int. J. Adv. Manuf. Technol. 2018, 95, 811–823. [Google Scholar] [CrossRef]
- Bombiński, S.; Błażejak, K.; Nejman, M.; Jemielniak, K. Sensor Signal Segmentation for Tool Condition Monitoring. Procedia CIRP 2016, 46, 155–160. [Google Scholar] [CrossRef]
- Hassan, M.; Sadek, A.; Attia, M.H.; Thomson, V. A Novel Generalized Approach for Real-Time Tool Condition Monitoring. J. Manuf. Sci. Eng. 2017, 140, 021010. [Google Scholar] [CrossRef]
- Sivarajah, U.; Kamal, M.M.; Irani, Z.; Weerakkody, V. Critical analysis of Big Data challenges and analytical methods. J. Bus. Res. 2017, 70, 263–286. [Google Scholar] [CrossRef]
- Guyon, I.; Nikravesh, M.; Gunn, S.; Zadeh, L.A. (Eds.) Feature Extraction; Springer Nature: Dordrecht, The Netherlands, 2006. [Google Scholar] [CrossRef]
- Tran, M.-Q.; Elsisi, M.; Liu, M.-K. Effective feature selection with fuzzy entropy and similarity classifier for chatter vibration diagnosis. Measurement 2021, 184, 109962. [Google Scholar] [CrossRef]
- Zebari, R.; Abdulazeez, A.; Zeebaree, D.; Zebari, D.; Saeed, J. A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction. J. Appl. Sci. Technol. Trends 2020, 1, 56–70. [Google Scholar] [CrossRef]
- Shi, J.; Li, Y.; Wang, G.; Zhang, M. Milling tool wear monitoring through time-frequency analysis of sensory signals. In Proceedings of the 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), Ottawa, ON, Canada, 20–22 June 2016; pp. 1–5. [Google Scholar]
- Munaro, R.; Attanasio, A.; Del Prete, A. Tool Wear Monitoring with Artificial Intelligence Methods: A Review. J. Manuf. Mater. Process. 2023, 7, 129. [Google Scholar] [CrossRef]
- Paul, S.; Mukhopadhyay, S.; Mandal, S. Quantification of tool wear in micro-milling using Fast Fourier transform of accelerometer data. In Proceedings of the 2019 3rd International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech), Kolkata, India, 29–31 August 2019; pp. 1–3. [Google Scholar]
- Zhou, Y.; Liu, C.; Yu, X.; Liu, B.; Quan, Y. Tool wear mechanism, monitoring and remaining useful life (RUL) technology based on big data: A review. SN Appl. Sci. 2022, 4, 1–24. [Google Scholar] [CrossRef]
- Vibration Analysis: FFT, PSD, and Spectrogram Basics [Free Download]. Available online: https://blog.endaq.com/vibration-analysis-fft-psd-and-spectrogram (accessed on 9 August 2024).
- Chen, C.-K.; Li, Y.-C. Intelligent real-time monitoring of Computer Numerical Control tool wear based on a fractional-order chaotic self-synchronization system. J. Low Freq. Noise Vib. Act. Control 2019, 38, 1555–1566. [Google Scholar] [CrossRef]
- Tran, M.-Q.; Liu, M.-K.; Tran, Q.-V. Milling chatter detection using scalogram and deep convolutional neural network. Int. J. Adv. Manuf. Technol. 2020, 107, 1505–1516. [Google Scholar] [CrossRef]
- Tran, M.-Q.; Liu, M.-K.; Tran, Q.-V.; Nguyen, T.-K. Effective Fault Diagnosis Based on Wavelet and Convolutional Attention Neural Network for Induction Motors. IEEE Trans. Instrum. Meas. 2021, 71, 1–13. [Google Scholar] [CrossRef]
- Jauregui, J.C.; Resendiz, J.R.; Thenozhi, S.; Szalay, T.; Jacso, A.; Takacs, M. Frequency and Time-Frequency Analysis of Cutting Force and Vibration Signals for Tool Condition Monitoring. IEEE Access 2018, 6, 6400–6410. [Google Scholar] [CrossRef]
- Fang, N.; Pai, P.S.; Mosquea, S. Effect of tool edge wear on the cutting forces and vibrations in high-speed finish machining of Inconel 718: An experimental study and wavelet transform analysis. Int. J. Adv. Manuf. Technol. 2011, 52, 65–77. [Google Scholar] [CrossRef]
- Cepstrum Analysis MATLAB & Simulink MathWorks India. Available online: https://in.mathworks.com/help/signal/ug/cepstrum-analysis.html (accessed on 9 August 2024).
- Zhang, B.; Katinas, C.; Shin, Y.C. Robust tool wear monitoring using systematic feature selection in turning processes with consideration of uncertainties. J. Manuf. Sci. Eng. 2018, 140, 081010. [Google Scholar] [CrossRef]
- Jemielniak, K.; Urbański, T.; Kossakowska, J.; Bombiński, S. Tool condition monitoring based on numerous signal features. Int. J. Adv. Manuf. Technol. 2012, 59, 73–81. [Google Scholar] [CrossRef]
- Zhang, C.; Yao, X.; Zhang, J.; Jin, H. Tool condition monitoring and remaining useful life prognostic based on a wireless sensor in dry milling operations. Sensors 2016, 16, 795. [Google Scholar] [CrossRef] [PubMed]
- Chacón, J.L.F.; de Barrena, T.F.; García, A.; de Buruaga, M.S.; Badiola, X.; Vicente, J. A novel machine learning-based methodology for tool wear prediction using acoustic emission signals. Sensors 2021, 21, 5984. [Google Scholar] [CrossRef] [PubMed]
- Fish, R.K.; Ostendorf, M.; Bernard, G.D.; Castanon, D.; Shivakumar, H. Feature Selection for Tool Wear Diagnosis Using Soft Computing Techniques. In Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE), Virtual conference, Virtual, 1–5 November 2021; Volume 2000–X, pp. 157–163. [Google Scholar]
- Ying, X. An Overview of Overfitting and its Solutions. J. Phys. Conf. Ser. 2019, 1168, 022022. [Google Scholar] [CrossRef]
- Abellan-Nebot, J.V.; Subirón, F.R. A review of machining monitoring systems based on artificial intelligence process models. Int. J. Adv. Manuf. Technol. 2010, 47, 237–257. [Google Scholar] [CrossRef]
- Yuan, J.; Liu, L.; Yang, Z.; Zhang, Y. Tool wear condition monitoring by combining variational mode decomposition and ensemble learning. Sensors 2020, 20, 6113. [Google Scholar] [CrossRef]
- Lauro, C.; Brandão, L.; Baldo, D.; Reis, R.; Davim, J. Monitoring and processing signal applied in machining processes—A review. Measurement 2014, 58, 73–86. [Google Scholar] [CrossRef]
- Yang, Y.; Hao, B.; Hao, X.; Li, L.; Chen, N.; Xu, T.; Aqib, K.M.; He, N. A Novel Tool (Single-Flute) Condition Monitoring Method for End Milling Process Based on Intelligent Processing of Milling Force Data by Machine Learning Algorithms. Int. J. Precis. Eng. Manuf. 2020, 21, 2159–2171. [Google Scholar] [CrossRef]
- Xie, Z.; Li, J.; Lu, Y. Feature selection and a method to improve the performance of tool condition monitoring. Int. J. Adv. Manuf. Technol. 2019, 100, 3197–3206. [Google Scholar] [CrossRef]
- Jia, W.; Sun, M.; Lian, J.; Hou, S. Feature dimensionality reduction: A review. Complex Intell. Syst. 2022, 8, 2663–2693. [Google Scholar] [CrossRef]
- Hu, M.; Ming, W.; An, Q.; Chen, M. Tool wear monitoring in milling of titanium alloy Ti-6Al-4 V under MQL conditions based on a new tool wear categorization method. Int. J. Adv. Manuf. Technol. 2019, 104, 4117–4128. [Google Scholar] [CrossRef]
- Wiciak-Pikula, M.; Felusiak, A.; Twardowski, P. Artificial Neural Network models for tool wear prediction during Aluminium Matrix Composite milling. In Proceedings of the 2020 IEEE 7th International Workshop on Metrology for AeroSpace (MetroAeroSpace), Pisa, Italy, 22–24 June 2020; pp. 255–259. [Google Scholar]
- Wang, G.; Yang, Y.; Zhang, Y.; Xie, Q. Vibration sensor based tool condition monitoring using ν support vector machine and locality preserving projection. Sens. Actuators A Phys. 2014, 209, 24–32. [Google Scholar] [CrossRef]
- Dong, J.; Subrahmanyam, K.V.R.; Wong, Y.S.; Hong, G.S.; Mohanty, A.R. Bayesian-inference-based neural networks for tool wear estimation. Int. J. Adv. Manuf. Technol. 2006, 30, 797–807. [Google Scholar] [CrossRef]
- Nießner, S.; Liewald, M.; Weyrich, M. A methodology to quantify tool wear effects in a shear cutting process based on an automatic feature extraction. IFAC-PapersOnLine 2022, 55, 540–547. [Google Scholar] [CrossRef]
- Li, G.; Wang, Y.; He, J.; Hao, Q.; Yang, H.; Wei, J. Tool wear state recognition based on gradient boosting decision tree and hybrid classification RBM. Int. J. Adv. Manuf. Technol. 2020, 110, 511–522. [Google Scholar] [CrossRef]
- Chen, J.; Susanto, V. Fuzzy Logic Based In-Process Tool-Wear Monitoring System in Face Milling Operations. Int. J. Adv. Manuf. Technol. 2003, 21, 186–192. [Google Scholar] [CrossRef]
- Khairnar, A.; Abhishek, P.; Pardeshi, S.; Jegadeeshwaran, R. Supervision of Carbide Tool Condition by Training of Vibration-based Statistical Model using Boosted Trees Ensemble. Int. J. Perform. Eng. 2021, 17, 229. [Google Scholar] [CrossRef]
- Patange, A.D.; Jegadeeshwaran, R.; Bajaj, N.S.; Khairnar, A.N.; Gavade, N.A. Application of Machine Learning for Tool Condition Monitoring in Turning. Sound Vib. 2022, 56, 127–145. [Google Scholar] [CrossRef]
- Peikari, M.; Salama, S.; Nofech-Mozes, S.; Martel, A.L. A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification. Sci. Rep. 2018, 8, 7193. [Google Scholar] [CrossRef]
- Patange, A.D.; Jegadeeshwaran, R. A machine learning approach for vibration-based multipoint tool insert health prediction on vertical machining centre (VMC). Measurement 2021, 173, 108649. [Google Scholar] [CrossRef]
- Zhou, Y.; Xue, W. Review of tool condition monitoring methods in milling processes. Int. J. Adv. Manuf. Technol. 2018, 96, 2509–2523. [Google Scholar] [CrossRef]
- Venkatesh, K.; Zhou, M.; Caudill, R.J. Design of artificial neural networks for tool wear monitoring. J. Intell. Manuf. 1997, 8, 215–226. [Google Scholar] [CrossRef]
- Lee, J.; Choi, H.J.; Nam, J.; Jo, S.B.; Kim, M.; Lee, S.W. Development and analysis of an online tool condition monitoring and diagnosis system for a milling process and its real-time implementation. J. Mech. Sci. Technol. 2017, 31, 5695–5703. [Google Scholar] [CrossRef]
- Salgado, D.; Alonso, F. Tool wear detection in turning operations using singular spectrum analysis. J. Mech. Work. Technol. 2006, 171, 451–458. [Google Scholar] [CrossRef]
- Kilundu, B.; Dehombreux, P.; Chiementin, X. Tool wear monitoring by machine learning techniques and singular spectrum analysis. Mech. Syst. Signal Process. 2011, 25, 400–415. [Google Scholar] [CrossRef]
- He, M.; He, D. A new hybrid deep signal processing approach for bearing fault diagnosis using vibration signals. Neurocomputing 2020, 396, 542–555. [Google Scholar] [CrossRef]
- Segreto, T.; Simeone, A.; Teti, R. Multiple Sensor Monitoring in Nickel Alloy Turning for Tool Wear Assessment via Sensor Fusion. Procedia CIRP 2013, 12, 85–90. [Google Scholar] [CrossRef]
- Seemuang, N.; McLeay, T.; Slatter, T. Using spindle noise to monitor tool wear in a turning process. Int. J. Adv. Manuf. Technol. 2016, 86, 2781–2790. [Google Scholar] [CrossRef]
- Lu, M.-C.; Wan, B.-S. Study of high-frequency sound signals for tool wear monitoring in micromilling. Int. J. Adv. Manuf. Technol. 2013, 66, 1785–1792. [Google Scholar] [CrossRef]
- Zhang, X.; Han, C.; Luo, M.; Zhang, D. Tool Wear Monitoring for Complex Part Milling Based on Deep Learning. Appl. Sci. 2020, 10, 6916. [Google Scholar] [CrossRef]
- Safonova, A.; Ghazaryan, G.; Stiller, S.; Main-Knorn, M.; Nendel, C.; Ryo, M. Ten deep learning techniques to address small data problems with remote sensing. Int. J. Appl. Earth Obs. Geoinf. 2023, 125, 103569. [Google Scholar] [CrossRef]
- Chang, Z.; Jia, K.; Han, T.; Wei, Y.-M. Towards more reliable photovoltaic energy conversion systems: A weakly-supervised learning perspective on anomaly detection. Energy Convers. Manag. 2024, 316, 118845. [Google Scholar] [CrossRef]
- Chen, Y.; Yan, Z.; Zhu, Y.; Ren, Z.; Shen, J.; Huang, Y. Data Augmentation for Environmental Sound Classification Using Diffusion Probabilistic Model with Top-K Selection Discriminator. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS 2023, 14087, 283–295. [Google Scholar] [CrossRef]
- Peng, Y.; Wang, Y.; Liu, Y.; Gao, K.; Yin, T.; Yu, H. Few-shot learning based multi-weather-condition impedance identification for MPPT-controlled PV converters. IET Renew. Power Gener. 2022, 16, 1345–1353. [Google Scholar] [CrossRef]
- Dou, J.; Jiao, S.; Xu, C.; Luo, F.; Tang, L.; Xu, X. Unsupervised online prediction of tool wear values using force model coefficients in milling. Int. J. Adv. Manuf. Technol. 2020, 109, 1153–1166. [Google Scholar] [CrossRef]
- Li, W.; Liang, Y. Deep transfer learning based diagnosis for machining process lifecycle. Procedia CIRP 2020, 90, 642–647. [Google Scholar] [CrossRef]
- Yu, H.; Yang, R.; Liu, H.; Du, W.; Zhang, J.; Han, Z. Tool wear state prediction based on GAF-MTF-AlexNet Tool wear state predicftion based on GAF-MTF-AlexNet. Preprints 2024. [Google Scholar] [CrossRef]
- Ross, N.S.; Sheeba, P.T.; Shibi, C.S.; Gupta, M.K.; Korkmaz, M.E.; Sharma, V.S. A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models. J. Intell. Manuf. 2024, 35, 757–775. [Google Scholar] [CrossRef]
- Transfer Learning: Definition, Tutorial & Applications|Encord. Available online: https://encord.com/blog/transfer-learning/ (accessed on 20 August 2024).
- Turšič, N.; Klančnik, S. Tool Condition Monitoring Using Machine Tool Spindle Current and Long Short-Term Memory Neural Network Model Analysis. Sensors 2024, 24, 2490. [Google Scholar] [CrossRef]
- Liu, M.; Xie, H.; Pan, W.; Ding, S.; Li, G. Prediction of cutting force via machine learning: State of the art, challenges and potentials. J. Intell. Manuf. 2023, 34, 1–62. [Google Scholar] [CrossRef]
- Scalability in Machine Learning Systems: Challenges, Strategies, and Best Practices|by amirsina torfi|Medium. Available online: https://medium.com/@amirsina.torfi/scalability-in-machine-learning-systems-challenges-strategies-and-best-practices-231cc2fb2889 (accessed on 10 August 2024).
- Machine Learning: How to Build Scalable Machine Learning Models. Available online: https://www.codementor.io/blog/scalable-ml-models-6rvtbf8dsd (accessed on 10 August 2024).
- Performance Metrics in Machine Learning Javatpoint. Available online: https://www.javatpoint.com/performance-metrics-in-machine-learning (accessed on 10 August 2024).
- Sheth, V.; Tripathi, U.; Sharma, A. A Comparative Analysis of Machine Learning Algorithms for Classification Purpose. Procedia Comput. Sci. 2022, 215, 422–431. [Google Scholar] [CrossRef]
- Cao, Q.; Giustozzi, F.; Zanni-Merk, C.; Beuvron, F.d.B.d.; Reich, C. Smart Condition Monitoring for Industry 4.0 Manufacturing Processes: An Ontology-Based Approach. Cybern. Syst. 2019, 50, 82–96. [Google Scholar] [CrossRef]
- Introduction to Cyber-Physical System GeeksforGeeks. Available online: https://www.geeksforgeeks.org/introduction-to-cyber-physical-system/ (accessed on 10 August 2024).
- Tran, M.-Q.; Elsisi, M.; Liu, M.-K.; Vu, V.Q.; Mahmoud, K.; Darwish, M.M.F.; Abdelaziz, A.Y.; Lehtonen, M. Reliable Deep Learning and IoT-Based Monitoring System for Secure Computer Numerical Control Machines Against Cyber-Attacks With Experimental Verification. IEEE Access 2022, 10, 23186–23197. [Google Scholar] [CrossRef]
- Teti, R.; Jemielniak, K.; O’donnell, G.; Dornfeld, D. Advanced monitoring of machining operations. CIRP Ann. 2010, 59, 717–739. [Google Scholar] [CrossRef]
- Tapoglou, N.; Mehnen, J.; Vlachou, A.; Doukas, M.; Milas, N.; Mourtzis, D. Cloud-Based Platform for Optimal Machining Parameter Selection Based on Function Blocks and Real-Time Monitoring. J. Manuf. Sci. Eng. Trans. ASME 2015, 137, 040909. [Google Scholar] [CrossRef]
- Chen, X.; Li, C.; Tang, Y.; Xiao, Q. An Internet of Things based energy efficiency monitoring and management system for machining workshop. J. Clean. Prod. 2018, 199, 957–968. [Google Scholar] [CrossRef]
- Saif, Y.; Yusof, Y.; Latif, K.; Kadir, A.Z.A.; Ahmad, M.B.I.; Adam, A.; Hatem, N. Development of a smart system based on STEP-NC for machine vision inspection with IoT environmental. Int. J. Adv. Manuf. Technol. 2022, 118, 4055–4072. [Google Scholar] [CrossRef]
- Li, H.; Zhang, X.; Tao, S. A Cloud Computing-Based Approach for Efficient Processing of Massive Machine Tool Diagnosis Data. J. Circuits Syst. Comput. 2021, 30, 2150297. [Google Scholar] [CrossRef]
- Wu, D.; Liu, S.; Zhang, L.; Terpenny, J.; Gao, R.X.; Kurfess, T.; Guzzo, J.A. A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing. J. Manuf. Syst. 2017, 43, 25–34. [Google Scholar] [CrossRef]
- Peng, C.; Du, H.; Liao, T.W. A research on the cutting database system based on machining features and TOPSIS. Robot. Comput. Manuf. 2017, 43, 96–104. [Google Scholar] [CrossRef]
- Interoperability in IoT: Speaking the Same Language Libelium. Available online: https://www.libelium.com/libeliumworld/interoperability-in-iot-speaking-the-same-language/ (accessed on 10 August 2024).
- Xiao, H.; Hu, W.; Liu, G.; Zhou, H. Edge computing-based unified condition monitoring system for process manufacturing. Available online: https://ssrn.com/abstract=4179189 (accessed on 3 September 2024).
- Rahman, G.; Wen, C.C. Fog Computing, Applications, Security and Challenges, Review. Int. J. Eng. Technol. 2018, 7, 1615. [Google Scholar] [CrossRef]
- Liao, R.; He, Y.; Feng, T.; Yang, X.; Dai, W.; Zhang, W. Mission reliability-driven risk-based predictive maintenance approach of multistate manufacturing system. Reliab. Eng. Syst. Saf. 2023, 236, 109273. [Google Scholar] [CrossRef]
- Laghmouchi, H.; Laghmouchi, A.; Geisert, C.; Hohwieler, E. Smart Wireless Sensor Network and Configuration of Algorithms for Condition Monitoring Applications Centre of Excellence in Production Informatics and Control (EPIC CoE) View Project Holistic Optimization of Sculptured Surface Manufacturing View Project Smart Wireless Sensor Network and Configuration of Algorithms for Condition Monitoring Applications. 2017. Available online: https://www.researchgate.net/publication/317835436 (accessed on 3 September 2024).
- Setiawan, A.; Angela, D.; Irawan, B. Modelling of Cutting Tool Condition Monitoring System (CTCMS) to support industry 4.0. MATEC Web Conf. 2018, 204, 02012. [Google Scholar] [CrossRef]
- Liu, Z.; Lang, Z.-Q.; Gui, Y.; Zhu, Y.-P.; Laalej, H. Digital twin-based anomaly detection for real-time tool condition monitoring in machining. J. Manuf. Syst. 2024, 75, 163–173. [Google Scholar] [CrossRef]
- Natarajan, S.; Thangamuthu, M.; Gnanasekaran, S.; Rakkiyannan, J. Digital Twin-Driven Tool Condition Monitoring for the Milling Process. Sensors 2023, 23, 5431. [Google Scholar] [CrossRef]
- What Is a Virtual Factory|NVIDIA Blog. Available online: https://blogs.nvidia.com/blog/virtual-factories-industrial-digitalization/ (accessed on 20 August 2024).
- Cutting Tool Assembly Creation and Management Software. Available online: https://www.sandvik.coromant.com/en-gb/tools/digital-machining/coroplus-tool-library (accessed on 29 August 2024).
- CoroPlus®. Available online: https://www.sandvik.coromant.com/en-us/campaigns/coroplus (accessed on 29 August 2024).
- NOVO Web Browser App Kennametal. Available online: https://www.kennametal.com/in/en/resources/novo.html?srsltid=AfmBOopCQpz8py-2A50_pKgj_S9IMnYJBL_2uJaltwCo1M4CaSlLSWlH (accessed on 29 August 2024).
- NOVO Advise Kennametal. Available online: https://www.kennametal.com/in/en/resources/novo/advise.html (accessed on 29 August 2024).
- ToolBOSS Tool Management Solutions Kennametal. Available online: https://www.kennametal.com/in/en/services/toolboss-tool-vending-machines.html?srsltid=AfmBOoqJ4U_QKCnuMfS8TDWPTgu3L3A4W1zvENWSP4ifUWuTE4-oRl_r (accessed on 29 August 2024).
- Iscar Tool Advisor New. Available online: https://www.iscar.com/ITA/home/machines (accessed on 29 August 2024).
- Latest Articles. Available online: https://www.iscar.com/newarticles.aspx/lang/en/newarticleid/3195 (accessed on 29 August 2024).
- BIG KAISER EWA Fine Boring Tool BIG KAISER Präzisionswerkzeuge|BIG KAISER|BIG KAISER. Available online: https://www.bigkaiser.eu/en/news/detail/?news_id=13342 (accessed on 29 August 2024).
- Everything You Need to Know about Digital Boring Heads. Available online: https://www.bigdaishowa.com/en/blog/everything-you-need-know-about-digital-boring-heads (accessed on 29 August 2024).
- CELOS X Machine & Manufacturing DMG MORI India. Available online: https://in.dmgmori.com/products/digitization/celos-x?utm_source=google&utm_medium=paid_cpc&utm_content=search&utm_campaign=celos_in&gad_source=1 (accessed on 29 August 2024).
- Easy Tool Monitor 2.0 DMG MORI India. Available online: https://in.dmgmori.com/products/digitization/technology-cycles/turning-milling/monitoring/easy-tool-monitoring?utm_source=google&utm_medium=paid_cpc&utm_content=search&utm_campaign=celos_in&gad_source=1 (accessed on 29 August 2024).
- Insights Hub|Siemens Software. Available online: https://plm.sw.siemens.com/en-US/insights-hub/ (accessed on 29 August 2024).
- Edge Computing with SINUMERIK Siemens Xcelerator Global. Available online: https://xcelerator.siemens.com/global/en/industries/machinebuilding/machine-tools/cnc4you/manufacturing-automation-and-networking/sinumerik-edge.html (accessed on 29 August 2024).
- SFx Smart Factory Getting Started. Available online: https://support.hexagonmi.com/s/article/SFx-Smart-Factory-Getting-Started (accessed on 29 August 2024).
- PC-DMIS|Hexagon. Available online: https://hexagon.com/products/product-groups/measurement-inspection-software/metrology-software/pc-dmis (accessed on 29 August 2024).
- Modular Machine Control System|Blù Marposs. Available online: https://www.blu.marposs.com/en/ (accessed on 29 August 2024).
- smarTcheck ZOLLER Tool Inspection and Measuring Technology. Available online: www.absicht.ag (accessed on 29 August 2024).
- Digital Makino|Makino. Available online: https://www.makino.eu/en-us/digital-makino (accessed on 29 August 2024).
- Machine Condition Monitoring Market Size, Share, Industry Report, Revenue Trends and Growth Drivers. Available online: https://www.marketsandmarkets.com/Market-Reports/machine-health-monitoring-market-29627363.html (accessed on 30 August 2024).
Process Technology | Method | Description | Development |
---|---|---|---|
Visual Inspection | Direct | Use of cameras and imaging systems to directly observe and measure tool wear | Early methods relied on manual inspection. Recent advancements include automated optical systems using AI for analysis. |
Thermal imaging | Direct | Infrared cameras measure the temperature distribution on the tool surface to assess wear | Initially used in aerospace industries, now integrated with real-time monitoring systems in various machining processes. |
Optical microscopy | Direct | Direct examination of tool surface under magnification to measure wear | Traditionally used in labs, with modern applications including automated image analysis using machine learning. |
Laser displacement sensor | Direct | Measure tool surface wear by detecting small changes in position using laser technology | Widely used in micro-machining industries, with integration into IoT systems for real-time monitoring. |
Spectroscopic analysis | Direct | Analyzes material composition and change in the tool using spectroscopic techniques | Advanced methods used in specific industries like aerospace and automotive, now enhanced with AI for detailed analysis. |
Capacitive and inductive sensor | Indirect | Detects proximity changes caused by tool wear or deformation | Commonly used in automated systems, advancements include integration with machine-learning algorithms for analysis. |
Ultrasonic sensor | Indirect | Uses ultrasonic waves to detect changes in material properties due to wear or cracks | Applied in high-precision machining, with AI-based analysis introduced in recent years for better accuracy. |
Electric current monitoring | Indirect | Monitors the electric current consumed by the machine, with variations indicating tool wear | Gained popularity in the 1980s, now often combined with AI for predictive maintenance in smart manufacturing setups. |
Force monitoring | Indirect | Measures cutting forces during machining, and the variations indicate tool wear or failure | Common since the 1970s with continuous enhancements in multi-axis dynamometers and real-time data processing. |
Vibration analysis | Indirect | Uses accelerometers to measure vibration which is correlated with the tool’s condition | Developed significantly in the 1990s with improvements in sensor accuracy and signal-processing techniques. |
Acoustic emission | Indirect | Detects high-frequency acoustic waves generated by tool wear and other machining processes | This method has been widely adopted since the 1980s with advancements in sensor technology and data analysis using AI. |
Features | Pros | Cons |
---|---|---|
Time-domain feature | Display different signals immediately and takes less time to process | Excessive noise |
Frequency-domain feature | Suitable for steady-state systems | Not easy to identify the relevant frequency band |
Time–frequency-domain feature | Suitable for non-steady-state systems | It does not have a standard procedure to select importantly features |
Authors | Signal | Feature Domain | Data-Processing Methods | Data Prediction Model | Prediction Accuracy |
---|---|---|---|---|---|
Salgado et al. [84] | Vibration | Frequency | Singular spectrum analysis (SSA) | ANN | RMSE: 15.11 |
Kilundu et al. [85] | Vibration | Frequency | SSA | ANN | 67.4% accuracy |
Miao et al. [86] | Vibration | Frequency | CNN | CNN | 99.92% accuracy |
Segreto et al. [87] | Force, AE, vibration | Frequency | Linear predictive analysis | ANN | 98.9% accuracy |
Seemuang et al. [88] | Sound | Time–frequency | STFT | Tested spindle noise at various feeds | _ |
Liu et al. [36] | Sound | Time–frequency | WPD | ANN | 8.59% error |
Salgado et al. [84] | Motor current, Sound FR | Time–frequency | SSA | LS-SVM | 4.94–8.72% error |
Tran et al. [53] | Cutting force | Time–frequency | Continuous WT | CNN | 99.67% accuracy |
Kothuru et al. [35] | Sound | Frequency | FFT | SVM | 95.92% accuracy |
Yao et al. [61] | Vibrations | Time, frequency, time–frequency | FFT | ANN based on FL | 0.0003% MSE |
Lu et al. [89] | Sound | Frequency | FFT | Hidden Markov model | 91.8% accuracy |
Performance Metrics | |
---|---|
Accuracy | The proportion of correctly predicted tool conditions to the total predictions |
Computational efficiency | The time and resources required for training and inference |
Robustness to noise | The algorithm’s ability to handle noisy or incomplete data |
Real-time suitability | The feasibility of deploying the algorithm in real-time monitoring systems |
Algorithm | Accuracy | Computational Efficiency | Robustness to Noise | Real-Time Suitability |
---|---|---|---|---|
SVM | High (98%) | Moderate | High | Moderate |
DT | Moderate (78%) | High | Moderate | High |
RF | High (97%) | Moderate | High | Moderate |
KNN | Moderate (73%) | Low | Low | Low |
CNN | High (98%) | Low | High | Low |
LSTM | High (99%) | Low | High | Low |
Ensemble Learning | High (93%) | Moderate | High | Moderate |
TL | High (94%) | High | Moderate | Moderate |
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Kasiviswanathan, S.; Gnanasekaran, S.; Thangamuthu, M.; Rakkiyannan, J. Machine-Learning- and Internet-of-Things-Driven Techniques for Monitoring Tool Wear in Machining Process: A Comprehensive Review. J. Sens. Actuator Netw. 2024, 13, 53. https://doi.org/10.3390/jsan13050053
Kasiviswanathan S, Gnanasekaran S, Thangamuthu M, Rakkiyannan J. Machine-Learning- and Internet-of-Things-Driven Techniques for Monitoring Tool Wear in Machining Process: A Comprehensive Review. Journal of Sensor and Actuator Networks. 2024; 13(5):53. https://doi.org/10.3390/jsan13050053
Chicago/Turabian StyleKasiviswanathan, Sudhan, Sakthivel Gnanasekaran, Mohanraj Thangamuthu, and Jegadeeshwaran Rakkiyannan. 2024. "Machine-Learning- and Internet-of-Things-Driven Techniques for Monitoring Tool Wear in Machining Process: A Comprehensive Review" Journal of Sensor and Actuator Networks 13, no. 5: 53. https://doi.org/10.3390/jsan13050053