An application of fuzzy inference for studying the dependency of roll force and roll torque on process variables in cold flat rolling

In this work, the roll force and roll torque in a cold flat rolling process are modelled using first order Takagi–Sugeno fuzzy models. The fuzzy models predict the most likely lower and upper estimates of the roll force and roll torque. Although the fuzzy models can be based on the experimental data, in the present work, the required data is generated by radial basis function neural networks. The neural networks, in turn, are trained by a finite element method-based code. It is demonstrated that the coefficients of the linear crisp function used to represent the output variables in the fuzzy inference system can be used for assessing the sensitivity of these variables with respect to the process variables. An algorithm to detect and suppress the outliers in the data is proposed. The effectiveness of the proposed algorithm is demonstrated through an example.