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Towards predictive identification of plastic deformation of WC–Co tools in high-speed machining, part 1 : multi-sensor approach for onset detection

Sridhar, Gautam LU ; Stergiopoulou, Xenia LU ; Bushlya, Volodymyr LU ; Hrechuk, Andrii LU orcid ; M’Saoubi, Rachid LU and Gutnichenko, Oleksandr LU (2026) 10th Conference on High Performance Cutting, CIRP-HPC 2026 In Procedia CIRP 141. p.358-363
Abstract

Plastic deformation remains a critical tool deterioration mechanism that limits the performance of cemented carbide cutting tools in high-speed machining. Reliable detection and prediction of its onset are essential for optimizing tool performance and machining process stability. This paper, the first in a two-part study, presents a multi-sensor framework for detecting and predicting the onset of plastic deformation. Controlled machining experiments (detailed in Part 2) were conducted to induce plastic deformation in CVD-coated WC-Co inserts. An experimental framework was developed that integrates multiple sensors, including force measurement, acoustic emission, accelerometer and audio analysis, with the aim of identifying the early... (More)

Plastic deformation remains a critical tool deterioration mechanism that limits the performance of cemented carbide cutting tools in high-speed machining. Reliable detection and prediction of its onset are essential for optimizing tool performance and machining process stability. This paper, the first in a two-part study, presents a multi-sensor framework for detecting and predicting the onset of plastic deformation. Controlled machining experiments (detailed in Part 2) were conducted to induce plastic deformation in CVD-coated WC-Co inserts. An experimental framework was developed that integrates multiple sensors, including force measurement, acoustic emission, accelerometer and audio analysis, with the aim of identifying the early stages of deformation during cutting. Analysis of the sensor data validated the efficacy of the approach, enabling the realtime detection of the onset and further monitoring of the progression of plastic deformation with high accuracy and robustness. This demonstrates that sensor-based detection can capture the transition into plastic deformation with high temporal resolution, providing a method for real-time monitoring of plastic deformation of the tool using edge computing devices.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Acoustic emission, Cemented carbide cutting tools, High speed machining, Plastic deformation, Signal processing algorithms
in
Procedia CIRP
volume
141
pages
358 - 363
publisher
Elsevier
conference name
10th Conference on High Performance Cutting, CIRP-HPC 2026
conference location
Cluny, France
conference dates
2026-06-17 - 2026-06-19
external identifiers
  • scopus:105042582833
ISSN
2212-8271
DOI
10.1016/j.procir.2026.03.041
language
English
LU publication?
yes
id
0c104da1-8cf8-47ca-bfcb-2f10dadaef72
date added to LUP
2026-06-29 11:23:30
date last changed
2026-08-31 08:27:39
@article{0c104da1-8cf8-47ca-bfcb-2f10dadaef72,
  abstract     = {{<p>Plastic deformation remains a critical tool deterioration mechanism that limits the performance of cemented carbide cutting tools in high-speed machining. Reliable detection and prediction of its onset are essential for optimizing tool performance and machining process stability. This paper, the first in a two-part study, presents a multi-sensor framework for detecting and predicting the onset of plastic deformation. Controlled machining experiments (detailed in Part 2) were conducted to induce plastic deformation in CVD-coated WC-Co inserts. An experimental framework was developed that integrates multiple sensors, including force measurement, acoustic emission, accelerometer and audio analysis, with the aim of identifying the early stages of deformation during cutting. Analysis of the sensor data validated the efficacy of the approach, enabling the realtime detection of the onset and further monitoring of the progression of plastic deformation with high accuracy and robustness. This demonstrates that sensor-based detection can capture the transition into plastic deformation with high temporal resolution, providing a method for real-time monitoring of plastic deformation of the tool using edge computing devices.</p>}},
  author       = {{Sridhar, Gautam and Stergiopoulou, Xenia and Bushlya, Volodymyr and Hrechuk, Andrii and M’Saoubi, Rachid and Gutnichenko, Oleksandr}},
  issn         = {{2212-8271}},
  keywords     = {{Acoustic emission; Cemented carbide cutting tools; High speed machining; Plastic deformation; Signal processing algorithms}},
  language     = {{eng}},
  pages        = {{358--363}},
  publisher    = {{Elsevier}},
  series       = {{Procedia CIRP}},
  title        = {{Towards predictive identification of plastic deformation of WC–Co tools in high-speed machining, part 1 : multi-sensor approach for onset detection}},
  url          = {{http://dx.doi.org/10.1016/j.procir.2026.03.041}},
  doi          = {{10.1016/j.procir.2026.03.041}},
  volume       = {{141}},
  year         = {{2026}},
}