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

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’.

Authors

Institutions

Publication Details

Journal
International Journal of Extreme Manufacturing
Published
2025-11-26
DOI
https://doi.org/10.1088/2631-7990/ae24c2
Citations
1
Primary Topic
Advanced machining processes and optimization
Type
article
Field-Weighted Citation Impact
0.71
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Biao Zhao, Wenfeng Ding, Nian Wan, Zhihao Wang et al.
1 citations
International Journal of Extreme Manufacturing
Advanced machining processes and optimization
0.71
article

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

Biao Zhao, Wenfeng Ding, Nian Wan, Zhihao Wang, Qi Liu
article en
1 citations

Abstract

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’.

International Journal of Extreme ManufacturingVol. 8(2)
University of Bath (GB), Nanjing University of Aeronautics and Astronautics (CN)
Openalex Percentile: Top 25%
Advanced machining processes and optimization
0.71
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.