Details
| Original language | English |
|---|---|
| Pages (from-to) | 133-137 |
| Number of pages | 5 |
| Journal | Procedia Manufacturing |
| Volume | 52 |
| Early online date | 24 Dec 2020 |
| Publication status | Published - 2020 |
| Event | 5th International Conference on System-Integrated Intelligence - Bremen, Germany Duration: 11 Nov 2020 → 13 Nov 2020 Conference number: 5 |
Abstract
T he process forces generated in machining are related to a deflection of the milling tool, which results in shape deviations. In addition to process parameters like feed rate, width and depth of cut or cutting speed, the wear condition of the tool has a significant influence on the shape deviation during flank milling. In process planning it is important to take the tool condition and the ideal time for tool change into account when selecting the process parameters. An assistance system is being researched at the Institute of Production Engineering and Machine T ools (IFW) in cooperation with Kennametal Shared Services GmbH to support this task. T he assistance system adjusts automatically the feed rate considering a predefined maximum shape deviation. Additionally, it identifies an optimal moment for tool change. T he advantages of the system are particularly evident in planning of individual milling processes. T he assistance system is based on a combination of a material removal simulation and empirical models of the shape error. For this purpose, spindle currents as well as measured shape errors are stored in a database. T hese data are extended by the actual local cutting conditions calculated by a process-parallel material removal simulation. Afterwards, the data is transferred into process knowledge via a Support Vector Machine (SVM). Within a technological NC simulation before the start of manufacturing, the generated knowledge is applied to predict the shape error of the workpiece and to automatically adjust the feed rate. By adapting the feed rate, it is possible to control the tool life. T he required tool change is defined by specifying a limit for the permitted width of flank wear land. T he presented assistance system enables the prediction of the shape error parallel to the manufacturing process and the automatic determination of the feed rate as well as the ideal time for tool change.
Keywords
- Machine learning, Milling, Simulation, Wear
ASJC Scopus subject areas
- Engineering(all)
- Industrial and Manufacturing Engineering
- Computer Science(all)
- Artificial Intelligence
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In: Procedia Manufacturing, Vol. 52, 2020, p. 133-137.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Simulation-based feed rate adaptation considering tool wear condition
AU - Denkena, Berend
AU - Dittrich, Marc André
AU - Mainka, Julia
N1 - Conference code: 5
PY - 2020
Y1 - 2020
N2 - T he process forces generated in machining are related to a deflection of the milling tool, which results in shape deviations. In addition to process parameters like feed rate, width and depth of cut or cutting speed, the wear condition of the tool has a significant influence on the shape deviation during flank milling. In process planning it is important to take the tool condition and the ideal time for tool change into account when selecting the process parameters. An assistance system is being researched at the Institute of Production Engineering and Machine T ools (IFW) in cooperation with Kennametal Shared Services GmbH to support this task. T he assistance system adjusts automatically the feed rate considering a predefined maximum shape deviation. Additionally, it identifies an optimal moment for tool change. T he advantages of the system are particularly evident in planning of individual milling processes. T he assistance system is based on a combination of a material removal simulation and empirical models of the shape error. For this purpose, spindle currents as well as measured shape errors are stored in a database. T hese data are extended by the actual local cutting conditions calculated by a process-parallel material removal simulation. Afterwards, the data is transferred into process knowledge via a Support Vector Machine (SVM). Within a technological NC simulation before the start of manufacturing, the generated knowledge is applied to predict the shape error of the workpiece and to automatically adjust the feed rate. By adapting the feed rate, it is possible to control the tool life. T he required tool change is defined by specifying a limit for the permitted width of flank wear land. T he presented assistance system enables the prediction of the shape error parallel to the manufacturing process and the automatic determination of the feed rate as well as the ideal time for tool change.
AB - T he process forces generated in machining are related to a deflection of the milling tool, which results in shape deviations. In addition to process parameters like feed rate, width and depth of cut or cutting speed, the wear condition of the tool has a significant influence on the shape deviation during flank milling. In process planning it is important to take the tool condition and the ideal time for tool change into account when selecting the process parameters. An assistance system is being researched at the Institute of Production Engineering and Machine T ools (IFW) in cooperation with Kennametal Shared Services GmbH to support this task. T he assistance system adjusts automatically the feed rate considering a predefined maximum shape deviation. Additionally, it identifies an optimal moment for tool change. T he advantages of the system are particularly evident in planning of individual milling processes. T he assistance system is based on a combination of a material removal simulation and empirical models of the shape error. For this purpose, spindle currents as well as measured shape errors are stored in a database. T hese data are extended by the actual local cutting conditions calculated by a process-parallel material removal simulation. Afterwards, the data is transferred into process knowledge via a Support Vector Machine (SVM). Within a technological NC simulation before the start of manufacturing, the generated knowledge is applied to predict the shape error of the workpiece and to automatically adjust the feed rate. By adapting the feed rate, it is possible to control the tool life. T he required tool change is defined by specifying a limit for the permitted width of flank wear land. T he presented assistance system enables the prediction of the shape error parallel to the manufacturing process and the automatic determination of the feed rate as well as the ideal time for tool change.
KW - Machine learning
KW - Milling
KW - Simulation
KW - Wear
UR - http://www.scopus.com/inward/record.url?scp=85100808910&partnerID=8YFLogxK
U2 - 10.1016/j.promfg.2020.11.024
DO - 10.1016/j.promfg.2020.11.024
M3 - Conference article
AN - SCOPUS:85100808910
VL - 52
SP - 133
EP - 137
JO - Procedia Manufacturing
JF - Procedia Manufacturing
SN - 2351-9789
T2 - 5th International Conference on System-Integrated Intelligence
Y2 - 11 November 2020 through 13 November 2020
ER -