Modellierung des Räumprozesses mittels mehrskaliger Diskretisierung
Zusammenfassung der Projektergebnisse
Fundamental investigation has been performed in orthogonal cutting of DA718 with carbide cutting tools to determine the cutting force components, the chip form, and the cutting temperature. Those results have been used to determine a temperature-dependent tool wear model. In addition, the friction behavior for broaching DA718 with carbides has been investigated to derive a friction model for the FEM chip formation simulation. Furthermore, the experimental investigation has been used for the inverse calibration of the material and friction model for the 2D FEM chip formation simulation, which was based on the coupled Eulerian-Lagrangian formulation. The validated 2D FEM model enabled the prediction of the specific process forces 𝑘 and 𝑘 under different tool geometries, cutting parameters, and tool wear conditions. Using a Deep Neural Network trained by a total number of 268 experimental and 1977 simulated data sets, the specific process forces have been modeled as a function of process parameters. Furthermore, combining this data-driven method with an empirical tool wear model, the tool wear development in orthogonal cutting was predicted. Finally, based on the analytical discretization of the broaching tool, the local mechanical load and the tool wear development have been predicted and mapped along the cutting edge. The following conclusions can be made according to the investigations: The cutting force and the cutting normal force increased with increasing the undeformed chip thickness and decreased with increasing the cutting speed vc. Furthermore, the cutting force components increased with decreasing the rake angle γ. - The cutting temperature increased with increasing the tool wear . The comparison between the FEM-simulation and the inverse approach showed that the inversely determined maximum temperature had a better agreement. - The apparent friction coefficient decreased with the increase of the relative speed. - The FE-model with the calibrated material and friction model has successfully predicted to the cutting force and the chip segmentation ratio. - The machine-learning algorithm with ANNs achieved accuracies of 𝑅 2 > 99 % with respect to the determination of coefficient. In the case of the linear regression model, 𝑅 2 ranged only between 80 % and 82 %. - By means of a hybrid approach based on thermo-mechanical loads, the tool wear development has been successfully predicted in the orthogonal cutting under defined process parameters. - The proposed multi-scale approach achieved an acceptable agreement with the experimental results. For the prediction of the cutting force under consideration of the tool wear, the multiscale approach achieved a deviation of 8 % - 13 % in comparison to experimental results. In addition, the trend influenced by the tool wear was also well predicted by the multi-scale approach. - Regarding the computation time, the multi-scale approach was about 10 seconds, which was mainly occupied by loading ANNs, while 3D FEM chip formation simulations took about 24 hours to compute a cutting time of 𝑡 = 0.005 s. Therefore, the multiscale approach, taking into account both accuracy and computation time, has a great advantage for predicting the broaching process.
Projektbezogene Publikationen (Auswahl)
- (2018): Model-based analysis in finish broaching of inconel 718. In: The International Journal of Advanced Manufacturing Technology. Jg. 97, Nr. 9-12, S. 3751–3760
Seimann, M.; Peng, B.; Fischersworring-Bunk, A.; Rauch, S.; Klocke, F.; Döbbeler, B.
(Siehe online unter https://doi.org/10.1007/s00170-018-2221-5) - (2018): Multi Flank Chip Formation in Fir-Tree Broaching Inconel 718 with Cemented Carbide. In: Procedia Manufacturing. Jg. 26, S. 503–508
Seimann, M.; Peng, B.; Klocke, F.; Döbbeler, B.
(Siehe online unter https://doi.org/10.1016/j.promfg.2018.07.059) - (2018): Tool-based inverse determination of material model of Direct Aged Alloy 718 for FEM cutting simulation. In: Procedia CIRP. Jg. 77, S. 54–57
Klocke, F.; Döbbeler, B.; Peng, B.; Schneider, S.A.M.
(Siehe online unter https://doi.org/10.1016/j.procir.2018.08.211) - (2019): A Coupling Approach Combining Computational Fluid Dynamics and Finite Element Method to Predict Cutting Fluid Effects on the Tool Temperature in Cutting Processes. In: Journal of Manufacturing Science and Engineering. Jg. 141, Nr. 10, S. 900
Helmig, T.; Peng, B.; Ehrenpreis, C.; Augspurger, T.; Frekers, Y.; Kneer, R.; Bergs, T.
(Siehe online unter https://doi.org/10.1115/1.4044102) - (2019): A hybrid approach using machine learning to predict the cutting forces under consideration of the tool wear. In: Procedia CIRP. Jg. 82, S. 302–307
Peng, B.; Bergs, T.; Schraknepper, D.; Klocke, F.; Döbbeler, B.
(Siehe online unter https://doi.org/10.1016/j.procir.2019.04.031) - (2019): An advanced FE-modeling approach to improve the prediction in machining difficult-to-cut material. In: The International Journal of Advanced Manufacturing Technology. Jg. 103, Nr. 5-8, S. 2183–2196
Peng, B.; Bergs, T.; Klocke, F.; Döbbeler, B.
(Siehe online unter https://doi.org/10.1007/s00170-019-03456-0) - (2020): A novel approach to determine the cutting temperature under consideration of the cool wear. In: Proceedings of the ASME 2020 15th International Manufacturing Science and Engineering Conference
Bergs, T.; Peng, B.; Schraknepper, D.; Augspurger, T.
(Siehe online unter https://doi.org/10.1115/MSEC2020-8408) - (2020): Development and validation of a new friction model for cutting processes. In: The International Journal of Advanced Manufacturing Technology. Jg. 82, Nr. 4, S. 348
Peng, B.; Bergs, T.; Schraknepper, D.; Smigielski, T.; Klocke, F.
(Siehe online unter https://doi.org/10.1007/s00170-019-04709-8)
