Abstract
Due to the complexity of the high-pressure jet assisted turning, knowledge, and prediction of the cutting forces are essential for the planning of machining operations for maximum productivity and quality. However, it is well known that during processing using this procedure there are difficulties in collecting data. It is required to establish an adequate model that would make it possible to predict the cutting force based on the input parameters. During machining to avoid difficulties in acquisition data, two models have developed based on fuzzy logic that will allow indirect monitoring of the cutting force. This research uses the improved fuzzy logic methods for modeling, whereby it can make predictions of the main cutting force according to the different input parameters. The contribution of this work reflected through the application of two innovative methods based on reducing the number of rules, which leads to better interpretability of models. First is the Mamdani with rule reduction method, and second is the Sugeno sub-clustering method based on the identification of the model structure, it comes down to finding the required number of rules by forming specific clusters. Both approaches differ by reducing the number of rules without affecting the accuracy of the models. The ability to predict the model determined by applying different statistical parameters. It concluded that Mamdani and Sugeno models give an approximate quality of the prediction. The resulting models also have an acceptable error to predict data that did not participate in their creation. Furthermore, obtained models can be used at the generalization stage where the cutting force information is required and where direct measurement is not possible.
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Abbreviations
- D n :
-
Diameter of the nozzle
- d :
-
Distance between the impact point of the jet and the cutting edge
- P :
-
Pressure of the jet
- v c :
-
Cutting speed
- f :
-
Feed rate
- F c :
-
Main cutting force
- F x :
-
Axial cutting force
- F y :
-
Radial cutting force
- F z :
-
Tangential cutting force
- HPJAT:
-
High-pressure jet assisted turning
- MISO:
-
Multi-input–single-output
- MIMO:
-
Multi-input–multi-output
- ANOVA:
-
Analysis of variance
- MF:
-
Membership function
- x :
-
Axis x of input variable
- \( \upsigma \) :
-
Standard deviation
- c :
-
Mean value (center)
- MV :
-
Measured values
- PV :
-
Predicted values
- R 2 :
-
Coefficient of determination
- MSE :
-
Mean square error
- MAE :
-
Mean absolute error
- FL:
-
Fuzzy logic with rule reduction
- SC:
-
Sub-clustering
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Acknowledgements
This study was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project TR 35015.
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Rodić, D., Sekulić, M., Gostimirović, M. et al. Fuzzy logic and sub-clustering approaches to predict main cutting force in high-pressure jet assisted turning. J Intell Manuf 32, 21–36 (2021). https://doi.org/10.1007/s10845-020-01555-4
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DOI: https://doi.org/10.1007/s10845-020-01555-4