PT Journal
AU Banerjee, P
   Karmakar, A
   Hui, N
TI Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Mean Thickness in Backward Metal Flow Forming Process
SO Acta Mechanica Slovaca
PY 2021
BP 46
EP 55
VL 25
IS 2
DI 10.21496/ams.2021.018
DE ANFIS; Mean thickness; backward flow forming; predictive model.
AB In the backward metal flow forming process, the mean thickness of flow formed tubes is crucial in determining the quality. Consequently, predicting the mean thickness in incremental forming and correlating these values with the forming parameters can be useful to control this vital target. Accordingly, four different techniques of adaptive neuro-fuzzy inference system (ANFIS) is used to predict the mean thickness of parts produced by the backward metal flow forming process. The objective is to determine the best membership function of the different approaches used in ANFIS. Also, the efficiency of the developed predictive models is compared statistically to determine the best technique.
ER