PT  - JOURNAL ARTICLE
AU  - Banerjee, Prabas
AU  - Karmakar, Arnab
AU  - Hui, Nirmal
TI  - Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Mean Thickness in Backward Metal Flow Forming Process
DP  - 2021 Jun 25
TA  - Acta Mechanica Slovaca
PG  - 46--55
VI  - 25
IP  - 2
AID - 10.21496/ams.2021.018
IS  - 13352393
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.