A Systematic Review on Smart and Predictive Maintenance in Tool Condition Monitoring
The main goal in the field of reliability and maintenance is ensuring and enhancing the availability of assets. A decrease in the production capability of machines can be the outcome of untimely and inefficient maintenance planning. Unexpected and unscheduled machinery shutdown due to required maint...
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2025-01-01
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| author | Dhanalekshmi Prasad Yedurkar Thomas Schlech Markus G. R. Sause |
| author_facet | Dhanalekshmi Prasad Yedurkar Thomas Schlech Markus G. R. Sause |
| author_sort | Dhanalekshmi Prasad Yedurkar |
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| description | The main goal in the field of reliability and maintenance is ensuring and enhancing the availability of assets. A decrease in the production capability of machines can be the outcome of untimely and inefficient maintenance planning. Unexpected and unscheduled machinery shutdown due to required maintenance reflects poorly on a business, resulting in damaged credibility and financial losses. This puts organizations in a position to decide between undertaking preventive replacement of parts that could have been used for some more time or running the machine till it dies (run to failure). On the other hand, organizations can improve their uptime by promptly replacing potentially good parts that could have been used for some more cycles. In addition to assisting enterprises in minimizing or preventing unplanned downtime, smart and predictive maintenance (SPM) extends the machinery’s remaining useful life (RUL). A crucial instance is the cutting tool in machinery used for milling, drilling, or turning. It is an ideal asset to apply tool condition monitoring (TCM) since a breakdown of this part will result in unexpected downtime, resulting in a downturn in productivity. In a situation like this, a well-planned SPM strategy involving monitoring real-time health of tools used for cutting is beneficial. In the industrial predictive maintenance domain of Industry 5.0, accurate prediction of RUL of machinery is highly desired. Much research has been done on this topic, but none of it has covered all the techniques that have been used or have the potential to be used in the future. This study aims to support a comprehensive and methodical review of studies on the data-driven approach for estimating the RUL of cutting tools used in various computer numerical control (CNC) machining processes, including drilling, milling, and turning operations. This paper is a summary of various methods for monitoring, feature extraction techniques, decision-making models, and sensors currently available in this domain. A comparison of the accuracy of different prediction models used for estimating tool wear in TCM is also presented in this paper. The study concludes with a discussion of recent advances, challenges, and limitations in RUL prognostic methods that use artificial intelligence (AI), as well as the potential for further research in this domain. |
| format | Article |
| id | doaj-art-85da8bb8ecd44d60ab4c70d5a4d3b0d6 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-85da8bb8ecd44d60ab4c70d5a4d3b0d62025-08-20T03:23:57ZengIEEEIEEE Access2169-35362025-01-011310624610628610.1109/ACCESS.2025.357920411031427A Systematic Review on Smart and Predictive Maintenance in Tool Condition MonitoringDhanalekshmi Prasad Yedurkar0https://orcid.org/0000-0002-2235-1536Thomas Schlech1https://orcid.org/0000-0001-7670-2429Markus G. R. Sause2https://orcid.org/0000-0002-6477-0691Department of Mechanical Engineering, Institut für Materials Resource Management, Universität Augsburg, Augsburg, GermanyDepartment of Mechanical Engineering, Institut für Materials Resource Management, Universität Augsburg, Augsburg, GermanyDepartment of Mechanical Engineering, Institut für Materials Resource Management, Universität Augsburg, Augsburg, GermanyThe main goal in the field of reliability and maintenance is ensuring and enhancing the availability of assets. A decrease in the production capability of machines can be the outcome of untimely and inefficient maintenance planning. Unexpected and unscheduled machinery shutdown due to required maintenance reflects poorly on a business, resulting in damaged credibility and financial losses. This puts organizations in a position to decide between undertaking preventive replacement of parts that could have been used for some more time or running the machine till it dies (run to failure). On the other hand, organizations can improve their uptime by promptly replacing potentially good parts that could have been used for some more cycles. In addition to assisting enterprises in minimizing or preventing unplanned downtime, smart and predictive maintenance (SPM) extends the machinery’s remaining useful life (RUL). A crucial instance is the cutting tool in machinery used for milling, drilling, or turning. It is an ideal asset to apply tool condition monitoring (TCM) since a breakdown of this part will result in unexpected downtime, resulting in a downturn in productivity. In a situation like this, a well-planned SPM strategy involving monitoring real-time health of tools used for cutting is beneficial. In the industrial predictive maintenance domain of Industry 5.0, accurate prediction of RUL of machinery is highly desired. Much research has been done on this topic, but none of it has covered all the techniques that have been used or have the potential to be used in the future. This study aims to support a comprehensive and methodical review of studies on the data-driven approach for estimating the RUL of cutting tools used in various computer numerical control (CNC) machining processes, including drilling, milling, and turning operations. This paper is a summary of various methods for monitoring, feature extraction techniques, decision-making models, and sensors currently available in this domain. A comparison of the accuracy of different prediction models used for estimating tool wear in TCM is also presented in this paper. The study concludes with a discussion of recent advances, challenges, and limitations in RUL prognostic methods that use artificial intelligence (AI), as well as the potential for further research in this domain.https://ieeexplore.ieee.org/document/11031427/Artificial intelligencesensorssmart and predictive maintenancetool condition monitoringtool wear |
| spellingShingle | Dhanalekshmi Prasad Yedurkar Thomas Schlech Markus G. R. Sause A Systematic Review on Smart and Predictive Maintenance in Tool Condition Monitoring IEEE Access Artificial intelligence sensors smart and predictive maintenance tool condition monitoring tool wear |
| title | A Systematic Review on Smart and Predictive Maintenance in Tool Condition Monitoring |
| title_full | A Systematic Review on Smart and Predictive Maintenance in Tool Condition Monitoring |
| title_fullStr | A Systematic Review on Smart and Predictive Maintenance in Tool Condition Monitoring |
| title_full_unstemmed | A Systematic Review on Smart and Predictive Maintenance in Tool Condition Monitoring |
| title_short | A Systematic Review on Smart and Predictive Maintenance in Tool Condition Monitoring |
| title_sort | systematic review on smart and predictive maintenance in tool condition monitoring |
| topic | Artificial intelligence sensors smart and predictive maintenance tool condition monitoring tool wear |
| url | https://ieeexplore.ieee.org/document/11031427/ |
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