Monitoring of drill breakages on the spindle of a machine tool using acoustic emission

Numerous sensors are integrated into modern machine tools to detect and control the forces acting on the tool or workpiece, or to monitor mechanical components such as ball bearings. In addition to the machine itself, the tool is also a sensitive component. In extreme cases, spontaneous failure of the tool can lead to a collision with the clamped workpiece and cause irreparable damage. This results in subsequent costs due to material loss, repair, or production downtime, which can be considerable high. It is therefore important to detect possible wear or failure of tools during the production process. Tool monitoring is particularly useful for flexible production systems which, due to their dynamic application situation, do not allow an estimation of tool life and therefore no fixed tool maintenance intervals. Consequently, the integration of sensors into the machine tool is intended to detect wear at an early stage and minimize tool failure. The integration of a sensor system was carried out in collaboration with Ott Jacob Spanntechnik during a joint research project. The purely mechanical component of the rotary union of a motor spindle was expanded to include an integrated acoustic emission sensor. The focus was on the development of inexpensive sensors for integration into the machine and an evaluation procedure for detecting tool breakage. This paper presents the evaluation principles used and examines their applicability in a manufacturing process. At first, acoustic emission signals from tool breakages were generated in laboratory on a simplified cooling channel setup. The measurement data were used to detect breakage events using different acoustic emission parameters and machine learning methods. It was shown that both methods can identify drill break in the signal, regardless of the diameter. The measurements were then repeated on a machine tool under realistic operating conditions and extended to include wear detection.

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Publication Details

Journal
e-Journal of Nondestructive Testing
Published
2024-10-01
DOI
https://doi.org/10.58286/30485
Primary Topic
Advanced machining processes and optimization
Type
article
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article

Monitoring of drill breakages on the spindle of a machine tool using acoustic emission

Frank Duckhorn, Mareike Stephan, Tobias Gaul, Lars Schubert et al.
e-Journal of Nondestructive Testing
Advanced machining processes and optimization
article

Monitoring of drill breakages on the spindle of a machine tool using acoustic emission

Frank Duckhorn, Mareike Stephan, Tobias Gaul, Lars Schubert, Stefan Bonerz
article en

Abstract

Numerous sensors are integrated into modern machine tools to detect and control the forces acting on the tool or workpiece, or to monitor mechanical components such as ball bearings. In addition to the machine itself, the tool is also a sensitive component. In extreme cases, spontaneous failure of the tool can lead to a collision with the clamped workpiece and cause irreparable damage. This results in subsequent costs due to material loss, repair, or production downtime, which can be considerable high. It is therefore important to detect possible wear or failure of tools during the production process. Tool monitoring is particularly useful for flexible production systems which, due to their dynamic application situation, do not allow an estimation of tool life and therefore no fixed tool maintenance intervals. Consequently, the integration of sensors into the machine tool is intended to detect wear at an early stage and minimize tool failure. The integration of a sensor system was carried out in collaboration with Ott Jacob Spanntechnik during a joint research project. The purely mechanical component of the rotary union of a motor spindle was expanded to include an integrated acoustic emission sensor. The focus was on the development of inexpensive sensors for integration into the machine and an evaluation procedure for detecting tool breakage. This paper presents the evaluation principles used and examines their applicability in a manufacturing process. At first, acoustic emission signals from tool breakages were generated in laboratory on a simplified cooling channel setup. The measurement data were used to detect breakage events using different acoustic emission parameters and machine learning methods. It was shown that both methods can identify drill break in the signal, regardless of the diameter. The measurements were then repeated on a machine tool under realistic operating conditions and extended to include wear detection.

e-Journal of Nondestructive TestingVol. 29(10)
Openalex Percentile: Top 79%
Advanced machining processes and optimization
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Monitoring of drill breakages on the spindle of a machine tool using acoustic emission — Frank Duckhorn, Mareike Stephan, et al. · e-Journal of Nondestructive Testing (2024) | TGRS Research Map | TGRS