Surface sliding revealed by operando monitoring of high-pressure torsion by acoustic emission

Y. Beygelzimer; D. Orlov; B. Baretzky; Y. Estrin; A. Vinogradov; R. Kulagin

doi:10.1016/j.matlet.2024.136303

Surface sliding revealed by operando monitoring of high-pressure torsion by acoustic emission

Y. Beygelzimer, D. Orlov, B. Baretzky, Y. Estrin, A. Vinogradov, R. Kulagin

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

High-pressure torsion (HPT) is widely used as a key method for microstructure control through deformation processing across a broad range of materials. However, certain gaps in process control impact its efficacy. In this study, we investigate the acoustic emission (AE) signals generated during HPT by considering commercially pure molybdenum as an example. By employing the adaptive sequential k-means algorithm, we analyse the AE stream to categorise and identify its sources. By comparing the kinetics of AE signal evolution during HPT processing at pressures of 2 GPa and 5 GPa, two distinct signal types are identified: one linked to plastic deformation and the other to workpiece slippage over HPT anvil surfaces. This research demonstrates the potential of AE tools for operando monitoring of HPT stability and detection of workpiece slippage, thereby enhancing the processing efficiency.

Original language	English
Article number	136303
Journal	Materials Letters
Volume	363
DOIs	https://doi.org/10.1016/j.matlet.2024.136303
Publication status	Published - 15 May 2024

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10.1016/j.matlet.2024.136303Licence: CC BY

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title = "Surface sliding revealed by operando monitoring of high-pressure torsion by acoustic emission",

abstract = "High-pressure torsion (HPT) is widely used as a key method for microstructure control through deformation processing across a broad range of materials. However, certain gaps in process control impact its efficacy. In this study, we investigate the acoustic emission (AE) signals generated during HPT by considering commercially pure molybdenum as an example. By employing the adaptive sequential k-means algorithm, we analyse the AE stream to categorise and identify its sources. By comparing the kinetics of AE signal evolution during HPT processing at pressures of 2 GPa and 5 GPa, two distinct signal types are identified: one linked to plastic deformation and the other to workpiece slippage over HPT anvil surfaces. This research demonstrates the potential of AE tools for operando monitoring of HPT stability and detection of workpiece slippage, thereby enhancing the processing efficiency.",

keywords = "Acoustic emission, Cluster analysis, High-pressure torsion, Severe plastic deformation, Slippage",

author = "Y. Beygelzimer and D. Orlov and B. Baretzky and Y. Estrin and A. Vinogradov and R. Kulagin",

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AU - Beygelzimer, Y.

AU - Orlov, D.

AU - Baretzky, B.

AU - Estrin, Y.

AU - Vinogradov, A.

AU - Kulagin, R.

PY - 2024/5/15

Y1 - 2024/5/15

N2 - High-pressure torsion (HPT) is widely used as a key method for microstructure control through deformation processing across a broad range of materials. However, certain gaps in process control impact its efficacy. In this study, we investigate the acoustic emission (AE) signals generated during HPT by considering commercially pure molybdenum as an example. By employing the adaptive sequential k-means algorithm, we analyse the AE stream to categorise and identify its sources. By comparing the kinetics of AE signal evolution during HPT processing at pressures of 2 GPa and 5 GPa, two distinct signal types are identified: one linked to plastic deformation and the other to workpiece slippage over HPT anvil surfaces. This research demonstrates the potential of AE tools for operando monitoring of HPT stability and detection of workpiece slippage, thereby enhancing the processing efficiency.

AB - High-pressure torsion (HPT) is widely used as a key method for microstructure control through deformation processing across a broad range of materials. However, certain gaps in process control impact its efficacy. In this study, we investigate the acoustic emission (AE) signals generated during HPT by considering commercially pure molybdenum as an example. By employing the adaptive sequential k-means algorithm, we analyse the AE stream to categorise and identify its sources. By comparing the kinetics of AE signal evolution during HPT processing at pressures of 2 GPa and 5 GPa, two distinct signal types are identified: one linked to plastic deformation and the other to workpiece slippage over HPT anvil surfaces. This research demonstrates the potential of AE tools for operando monitoring of HPT stability and detection of workpiece slippage, thereby enhancing the processing efficiency.

KW - Acoustic emission

KW - Cluster analysis

KW - High-pressure torsion

KW - Severe plastic deformation

KW - Slippage

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JO - Materials Letters

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Surface sliding revealed by operando monitoring of high-pressure torsion by acoustic emission

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