Prediction of Squeeze Cast Density Using Fuzzy Logic Based Approaches

Abstract

In the present work, efforts are made to develop the input-output relationships for squeeze casting process by utilizing the fuzzy logic based approaches. Casting density in Squeeze casting is expressed as function of process parameters, such as time delay before pressurizing the metal, pressure durations, squeeze pressure, pouring temperature and die temperature. It is to be noted that, Mamdani based model and Takagi and Sugeno's model have been developed to model density in squeeze casting process. Manually constructed Mamdani based fuzzy logic controller and Takagi and Sugeno's based fuzzy logic controller have been used in approach 1 and approach 2 respectively. Training of FLC is carried with the help of five hundred input-output data set generated artificially through regression equations, obtained earlier by the same authors. The performance of the developed models was tested for both the linear and non-linear membership function distributions with the help of ten test cases. Moreover, the test data was collected by conducting the experiments and not used in training of FLCs. It is interesting to note that both approaches are capable to make accurate predictions. However, the performance of approach 2 with G bell shape membership function distribution is found to outperform approach 1 and other type of membership function distributions. The findings are useful to foundry-men, since it provides information on casting density in squeeze casting process for the different combination of process parameters without conducting any experiments.

Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

“…The authors worked earlier on the fuzzy logic based approaches, namely, manually constructed FLC (MCFLC) and automatically evolved adaptive network based fuzzy interface system (ANFIS), using different membership functions. In the present work, an attempt is made by the authors to compare the performance of both BPNN and GA-NN models with that of the above-mentioned work carried out earlier by the same authors [28,29]. Table 4 shows the average absolute percent deviation in prediction of all the models for predicting the responses density and SDAS via forward mapping.…”

“…The performance of developed NN based approaches has been compared for the same test cases carried out earlier by the same authors using fuzzy logic based approaches [28,29]. The authors worked earlier on the fuzzy logic based approaches, namely, manually constructed FLC (MCFLC) and automatically evolved adaptive network based fuzzy interface system (ANFIS), using different membership functions.…”

Modelling in Squeeze Casting Process-Present State and Future Perspectives

“…It is to be note that there are generally two types of fuzzy modelling system namely linguistic type (Mamdani approach) and precise type (Takagi-Sugeno) fuzzy modelling system. The manually constructed mamdani based fuzzy logic approach and adaptive network based Takagi and Sugeno approach has been successfully implemented to predict the secondary dendrite arm spacing and density of the squeeze casting components [92,93]. The authors successfully established the input-output relationship using Mamdani based fuzzy system for various casting applications namely cement-bonded sand mould, resin-bonded sand mould [94] and green sand mould [95,96] system.…”