Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : fuzzy inference system

Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Surface Roughness in Incremental Sheet Metal Forming Process

Aws K. Ibrahim; Wisam K. Hamdan

Engineering and Technology Journal, 2015, Volume 33, Issue 2, Pages 380-399

In manufacturing processes, surface finish of a product is very crucial in determining the quality. Therefore, the surface quality including the surface roughness is still the most important obstacles against the incremental sheet metal forming (ISMF) process. As a consequence, the possibility to predict the surface roughness values in incremental forming and to correlate these values with the forming parameters can be useful in order to control this important target. Accordingly, an adaptive neuro-fuzzy inference system (ANFIS) is used to predict the surface roughness of parts produced by single-point incremental forming (SPIF) process. The hybrid learning algorithm is applied in ANFIS to determine the most suitable membership functions (MFs) and to simultaneously find the optimal premise and consequent parameters by directly minimizing the root mean squared error (RMSE) as a performance criterion. In order to achieve this target, five forming parameters, namely (tool diameter, incremental step size, tool shape, rotational speed and slope angle) are studied to form pyramid like shapes for the purpose of roughness measurement. Experimental results show that the difference sigmoidal MF gives the minimum RMSE. The predicted surface roughness values using ANFIS are compared with actual data. The comparison indicates that the utilization of difference sigmoidal MF in ANFIS could achieve a satisfactory prediction accuracy using both training and testing data when this MF is adopted. The training and testing prediction accuracy are 95.972% and 85.799% respectively.

A Fuzzy Interface System to Predict Ultimate Strength of Circular Concrete Filled Steel Tubular Columns

Kadhim Zuboon Nasser

Engineering and Technology Journal, 2012, Volume 30, Issue 3, Pages 364-377

In this study, a model for predicting the ultimate strength of circular concrete
filled steel tubular columns (CCFST) under axial loads has been developed using
fuzzy inference system (FIS). The available experimental results for (129) specimens
obtained from open literature were used to build the proposed model. The predicted
strengths obtained from the proposed FIS model were compared with the
experimental values and with unfactored design strengths predicted using the design
procedure specified in the AISC 2005 and Eurocode 4 for CCFST columns. Results
showed that the predicted values by the proposed FIS model were very close to the
experimental values and were more accurate than the AISC 2005 and Eurocode 4
values. As a result, FIS provided an efficient alternative method in predicting the
ultimate strength of CCFST columns.