Details
| Original language | English |
|---|---|
| Pages (from-to) | 765-771 |
| Number of pages | 7 |
| Journal | International Journal of Advanced Manufacturing Technology |
| Volume | 99 |
| Issue number | 1-4 |
| Early online date | 10 Aug 2018 |
| Publication status | Published - Oct 2018 |
Abstract
This paper examines the influence of material transition on a process monitoring approach based on confidence limits for friction-welded EN-AW6082/20MnCr5 shafts. Process error sensitivity was investigated for different control and acceleration signals as well as for the first principal component during slot milling. To that end, three material defects were applied by a hole, stainless steel rod, and high-speed steel drill. The signal changes caused by these defects were characterized by amplitude, duration, and shape. Based on the information, errors were simulated for each signal, which were used to evaluate the confidence limits and to compare the detection time for the entire machining process. A new methodology was developed to evaluate process monitoring systems with regard to error sensitivity. It was determined that errors were not detected during the entire process due to the material transition. By combining features with the first principal component analysis, the sensitivity of process monitoring had been improved by more than 50%.
Keywords
- Hybrid parts, Milling, Monitoring, Sensor data fusion
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Software
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: International Journal of Advanced Manufacturing Technology, Vol. 99, No. 1-4, 10.2018, p. 765-771.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Impact of hybrid workpieces on statistical process monitoring of machining operations
AU - Denkena, Berend
AU - Bergmann, Benjamin
AU - Witt, Matthias
N1 - Publisher Copyright: © Springer-Verlag London Ltd., part of Springer Nature 2018. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2018/10
Y1 - 2018/10
N2 - This paper examines the influence of material transition on a process monitoring approach based on confidence limits for friction-welded EN-AW6082/20MnCr5 shafts. Process error sensitivity was investigated for different control and acceleration signals as well as for the first principal component during slot milling. To that end, three material defects were applied by a hole, stainless steel rod, and high-speed steel drill. The signal changes caused by these defects were characterized by amplitude, duration, and shape. Based on the information, errors were simulated for each signal, which were used to evaluate the confidence limits and to compare the detection time for the entire machining process. A new methodology was developed to evaluate process monitoring systems with regard to error sensitivity. It was determined that errors were not detected during the entire process due to the material transition. By combining features with the first principal component analysis, the sensitivity of process monitoring had been improved by more than 50%.
AB - This paper examines the influence of material transition on a process monitoring approach based on confidence limits for friction-welded EN-AW6082/20MnCr5 shafts. Process error sensitivity was investigated for different control and acceleration signals as well as for the first principal component during slot milling. To that end, three material defects were applied by a hole, stainless steel rod, and high-speed steel drill. The signal changes caused by these defects were characterized by amplitude, duration, and shape. Based on the information, errors were simulated for each signal, which were used to evaluate the confidence limits and to compare the detection time for the entire machining process. A new methodology was developed to evaluate process monitoring systems with regard to error sensitivity. It was determined that errors were not detected during the entire process due to the material transition. By combining features with the first principal component analysis, the sensitivity of process monitoring had been improved by more than 50%.
KW - Hybrid parts
KW - Milling
KW - Monitoring
KW - Sensor data fusion
UR - http://www.scopus.com/inward/record.url?scp=85051648962&partnerID=8YFLogxK
U2 - 10.1007/s00170-018-2534-4
DO - 10.1007/s00170-018-2534-4
M3 - Article
AN - SCOPUS:85051648962
VL - 99
SP - 765
EP - 771
JO - International Journal of Advanced Manufacturing Technology
JF - International Journal of Advanced Manufacturing Technology
SN - 0268-3768
IS - 1-4
ER -