Acta Mechanica Slovaca 2021, 25(2):46-55 | DOI: 10.21496/ams.2021.018

Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Mean Thickness in Backward Metal Flow Forming Process

Prabas Banerjee1, *, Arnab Karmakar1, Nirmal Hui1
1 Department of Mechanical Engineering, National Institute of Technology, Durgapur-713209.

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.

Keywords: ANFIS, Mean thickness, backward flow forming, predictive model.

Received: March 14, 2021; Revised: April 10, 2021; Accepted: April 21, 2021; Published: June 25, 2021  Show citation

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Banerjee, P., Karmakar, A., & Hui, N. (2021). Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Mean Thickness in Backward Metal Flow Forming Process. Acta Mechanica Slovaca, 25(2), 46-55. doi: 10.21496/ams.2021.018
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