Fuzzy logic and sub-clustering approaches to predict main cutting force in high-pressure jet assisted turning

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.