Machining of complex parts for aerospace applications is still problematic. Multiple effects have an influence on the quality of the manufactured parts: Vibrations, tool wear and material properties such as the hardness of (e.g.) new high-temperature materials to name but a few. Especially the geometrical and material related properties of aerospace parts such as turbine blades suffer under these effects, which can result in out of tolerance geometrical dimensions and surface roughness that degrade the performance. Researchers in manufacturing make use of sensors with high sampling frequency to investigate and react to these influences. In contrast, the digitalization of machine control devices and their internal servo control cycles has the potential to substitute these sensors while still being financially more viable options. Additionally, they enable users to integrate the device vertically and horizontally into the factory environment, which enables in-line quality tracking and anomaly surveillance. However, lower sampling frequencies and distance from the process area are disadvantages of this approach. This paper investigates the substitution potential of internal control data for the machining process. The investigation focuses on drilling processes. Process-relevant sensor data such as forces, accelerations and acoustic emissions are measured in addition to NC servo control data. Through correlating internal and external data as well as surface and geometrical properties, the paper will give recommendations as to whether certain sensors can be successfully substituted by internal control data.
%0 Journal Article
%1 ramme2023process
%A Ramme, Johannes
%A Reeber, Tim
%A Rapp, Maximilian
%A Möhring, Hans-Christian
%D 2023
%I SSRN
%J MIC Procedia (2023)
%K control-data correlation data drilling monitoring myown process sensor-data
%P 57-64
%R 10.2139/ssrn.4657800
%T Process Stability Monitoring - Potential of Internal Control Data for Drilling Processes in the Aerospace Industry
%X Machining of complex parts for aerospace applications is still problematic. Multiple effects have an influence on the quality of the manufactured parts: Vibrations, tool wear and material properties such as the hardness of (e.g.) new high-temperature materials to name but a few. Especially the geometrical and material related properties of aerospace parts such as turbine blades suffer under these effects, which can result in out of tolerance geometrical dimensions and surface roughness that degrade the performance. Researchers in manufacturing make use of sensors with high sampling frequency to investigate and react to these influences. In contrast, the digitalization of machine control devices and their internal servo control cycles has the potential to substitute these sensors while still being financially more viable options. Additionally, they enable users to integrate the device vertically and horizontally into the factory environment, which enables in-line quality tracking and anomaly surveillance. However, lower sampling frequencies and distance from the process area are disadvantages of this approach. This paper investigates the substitution potential of internal control data for the machining process. The investigation focuses on drilling processes. Process-relevant sensor data such as forces, accelerations and acoustic emissions are measured in addition to NC servo control data. Through correlating internal and external data as well as surface and geometrical properties, the paper will give recommendations as to whether certain sensors can be successfully substituted by internal control data.
@article{ramme2023process,
abstract = {Machining of complex parts for aerospace applications is still problematic. Multiple effects have an influence on the quality of the manufactured parts: Vibrations, tool wear and material properties such as the hardness of (e.g.) new high-temperature materials to name but a few. Especially the geometrical and material related properties of aerospace parts such as turbine blades suffer under these effects, which can result in out of tolerance geometrical dimensions and surface roughness that degrade the performance. Researchers in manufacturing make use of sensors with high sampling frequency to investigate and react to these influences. In contrast, the digitalization of machine control devices and their internal servo control cycles has the potential to substitute these sensors while still being financially more viable options. Additionally, they enable users to integrate the device vertically and horizontally into the factory environment, which enables in-line quality tracking and anomaly surveillance. However, lower sampling frequencies and distance from the process area are disadvantages of this approach. This paper investigates the substitution potential of internal control data for the machining process. The investigation focuses on drilling processes. Process-relevant sensor data such as forces, accelerations and acoustic emissions are measured in addition to NC servo control data. Through correlating internal and external data as well as surface and geometrical properties, the paper will give recommendations as to whether certain sensors can be successfully substituted by internal control data.},
added-at = {2023-12-19T09:29:56.000+0100},
author = {Ramme, Johannes and Reeber, Tim and Rapp, Maximilian and Möhring, Hans-Christian},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c922aafec1efc9045dd9afec152c8399/jramme},
doi = {10.2139/ssrn.4657800},
interhash = {4842b6dc49051a4e88334b81e298b9ed},
intrahash = {c922aafec1efc9045dd9afec152c8399},
journal = {MIC Procedia (2023)},
keywords = {control-data correlation data drilling monitoring myown process sensor-data},
language = {English},
location = {https://ssrn.com/paper=4657800},
month = {november},
pages = {57-64},
publisher = {SSRN},
timestamp = {2023-12-20T16:37:57.000+0100},
title = {Process Stability Monitoring - Potential of Internal Control Data for Drilling Processes in the Aerospace Industry},
type = {Conference Paper},
year = 2023
}
