Details
| Original language | English |
|---|---|
| Pages (from-to) | 80-86 |
| Number of pages | 7 |
| Journal | CIRP Journal of Manufacturing Science and Technology |
| Volume | 58 |
| Early online date | 14 Feb 2025 |
| Publication status | E-pub ahead of print - 14 Feb 2025 |
Abstract
Process planning of tool grinding operations for individual cylindrical tools requires expert knowledge as well as adjustment tests in order to enable productive manufacturing according to the quality requirements. Static deflections of the cylindrical blank lead especially in the case of drilling tools to shape errors and core diameter deviations that vary with the axial workpiece position. This paper presents an architecture to compensate for shape errors autonomously in process planning by using a technological NC-Simulation. Based on a fast prediction of the elastic workpiece deflection, the initial NC code is modified by optimizing process parameters and adapting the tool path according to the bending line. A concept for data feedback ensures self-learning effects and enables model adaption. It is shown how the prediction can be adjusted for unknown grinding wheel specifications between the grain sizes D9 and D54. In experimental investigations, the shape error could be reduced in a range of 88 % to 99 % with a productivity increase of 47 %.
Keywords
- Cutting tool, Grinding, Parameter optimization, Path adaption, Process model, Simulation
ASJC Scopus subject areas
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: CIRP Journal of Manufacturing Science and Technology, Vol. 58, 06.2025, p. 80-86.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Architecture for autonomous shape error compensation in tool grinding
AU - Denkena, Berend
AU - Wichmann, Marcel
AU - Wulf, Michael
N1 - Publisher Copyright: © 2025 The Authors
PY - 2025/2/14
Y1 - 2025/2/14
N2 - Process planning of tool grinding operations for individual cylindrical tools requires expert knowledge as well as adjustment tests in order to enable productive manufacturing according to the quality requirements. Static deflections of the cylindrical blank lead especially in the case of drilling tools to shape errors and core diameter deviations that vary with the axial workpiece position. This paper presents an architecture to compensate for shape errors autonomously in process planning by using a technological NC-Simulation. Based on a fast prediction of the elastic workpiece deflection, the initial NC code is modified by optimizing process parameters and adapting the tool path according to the bending line. A concept for data feedback ensures self-learning effects and enables model adaption. It is shown how the prediction can be adjusted for unknown grinding wheel specifications between the grain sizes D9 and D54. In experimental investigations, the shape error could be reduced in a range of 88 % to 99 % with a productivity increase of 47 %.
AB - Process planning of tool grinding operations for individual cylindrical tools requires expert knowledge as well as adjustment tests in order to enable productive manufacturing according to the quality requirements. Static deflections of the cylindrical blank lead especially in the case of drilling tools to shape errors and core diameter deviations that vary with the axial workpiece position. This paper presents an architecture to compensate for shape errors autonomously in process planning by using a technological NC-Simulation. Based on a fast prediction of the elastic workpiece deflection, the initial NC code is modified by optimizing process parameters and adapting the tool path according to the bending line. A concept for data feedback ensures self-learning effects and enables model adaption. It is shown how the prediction can be adjusted for unknown grinding wheel specifications between the grain sizes D9 and D54. In experimental investigations, the shape error could be reduced in a range of 88 % to 99 % with a productivity increase of 47 %.
KW - Cutting tool
KW - Grinding
KW - Parameter optimization
KW - Path adaption
KW - Process model
KW - Simulation
UR - http://www.scopus.com/inward/record.url?scp=85217887838&partnerID=8YFLogxK
U2 - 10.1016/j.cirpj.2025.02.001
DO - 10.1016/j.cirpj.2025.02.001
M3 - Article
AN - SCOPUS:85217887838
VL - 58
SP - 80
EP - 86
JO - CIRP Journal of Manufacturing Science and Technology
JF - CIRP Journal of Manufacturing Science and Technology
SN - 1755-5817
ER -