AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (7.1 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Topical Review | Open Access

Intelligent evolution strategies for high-performance cutting tools: status, challenges, and trends

Nian Wan1Zhihao Wang1Biao Zhao1 ( )Wenfeng Ding1 Qi Liu2
National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People’s Republic of China
Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, United Kingdom
Show Author Information

Abstract

Traditional tools have limited adaptability in complex machining environments due to their lack of working-condition perception and autonomous regulation. With advances in sensors, materials, and data-processing technologies, tool design is shifting from a single-function ‘mechanical arm’ for cutting towards integrated intelligent terminals. This paper systematically reviews progress in intelligent tool technology from two perspectives: design and regulation. For intelligent design, the fundamental principles of condition-perception tools equipped with built-in multi-type sensors are discussed, enabling in situ, real-time monitoring of multidimensional parameters such as cutting force, temperature, and vibration. Force monitoring is achieved through elastic deformation or dynamic charge response, temperature monitoring through the thermoelectric effect, and vibration monitoring through micro-displacement and intensity detection. The design focus emphasises sensor miniaturisation and integration, balancing measurement accuracy with tool stiffness while minimising machining interference. In regulation, key technologies for constructing closed-loop control systems (CLCS) are summarised, which dynamically adjust cutting speed, feed rate, and other parameters based on sensed data, achieving precise control of force, temperature, and vibration via feedback mechanisms and driving units. Breakthroughs in tool wear compensation (TWC) mechanisms are introduced. Multi-source signal fusion combined with deep learning algorithms is further examined for improving monitoring accuracy and remaining useful life (RUL) prediction. Through model predictive control, intelligent regulation of cutting parameters within process flows is realised. Finally, challenges such as sensor reliability, multi-source coupling, and balancing cost with industrial applicability are analysed. Future directions highlight novel structural designs, high-performance material development, and multi-technology integration, aiming to establish a fully intelligent machining system through the integrated design of ‘perception-decision-execution’.

References

【1】
【1】
 
 
International Journal of Extreme Manufacturing

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
Wan N, Wang Z, Zhao B, et al. Intelligent evolution strategies for high-performance cutting tools: status, challenges, and trends. International Journal of Extreme Manufacturing, 2026, 8(2). https://doi.org/10.1088/2631-7990/ae24c2

295

Views

2

Downloads

6

Crossref

6

Web of Science

7

Scopus

0

CSCD

Received: 03 June 2025
Revised: 19 September 2025
Accepted: 26 November 2025
Published: 16 December 2025
© 2025 The Author(s).

Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.