Details
| Original language | German |
|---|---|
| Pages (from-to) | 305-308 |
| Number of pages | 4 |
| Journal | wt Werkstattstechnik online |
| Volume | 111 |
| Issue number | 5 |
| Publication status | Published - 2021 |
Abstract
Spindle current measurement allows acquiring process information without the need for additional sensors. Digital machine controls allow accessing the data with low effort. However, precise classification of process errors is a non-trivial task, especially for complex, single item workpieces without reference data. This work presents an approach to predict the spindle current and calculate tolerance limits by using a neuronal network based on a material removal simulation.
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Automotive Engineering
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In: wt Werkstattstechnik online, Vol. 111, No. 5, 2021, p. 305-308.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Sensorlose Überwachung der Einzelteilfertigung/Spindle-current-based process monitoring using artificial intelligence
AU - Denkena, Berend
AU - Bergmann, Benjamin
AU - Becker, Jonas
AU - Blech, Heiko
PY - 2021
Y1 - 2021
N2 - Spindle current measurement allows acquiring process information without the need for additional sensors. Digital machine controls allow accessing the data with low effort. However, precise classification of process errors is a non-trivial task, especially for complex, single item workpieces without reference data. This work presents an approach to predict the spindle current and calculate tolerance limits by using a neuronal network based on a material removal simulation.
AB - Spindle current measurement allows acquiring process information without the need for additional sensors. Digital machine controls allow accessing the data with low effort. However, precise classification of process errors is a non-trivial task, especially for complex, single item workpieces without reference data. This work presents an approach to predict the spindle current and calculate tolerance limits by using a neuronal network based on a material removal simulation.
UR - http://www.scopus.com/inward/record.url?scp=85107842670&partnerID=8YFLogxK
U2 - 10.37544/1436-4980-2021-05-39
DO - 10.37544/1436-4980-2021-05-39
M3 - Artikel
AN - SCOPUS:85107842670
VL - 111
SP - 305
EP - 308
JO - wt Werkstattstechnik online
JF - wt Werkstattstechnik online
IS - 5
ER -