A critical review on applications of artificial intelligence in manufacturing

O Mypati, A Mukherjee, D Mishra, SK Pal… - Artificial Intelligence …, 2023 - Springer
The fourth industrial revolution, Industry 4.0, has brought internet, artificial intelligence (AI),
and machine learning (ML) concepts into manufacturing. There is an immediate need to …

A study on the performance of various predictive models based on artificial neural network for backward metal flow forming process

P Banerjee, R Laha, MK Dikshit, NB Hui… - International Journal on …, 2024 - Springer
Backward metal flow forming is an incremental forming process that is used to manufacture
near shape products with precise dimensions. Among the most important application fields …

Experimental investigation and modeling of fiber metal laminates hydroforming process by GWO optimized neuro-fuzzy network

AH Rabiee, E Sherkatghanad… - … of Computational & …, 2023 - jcarme.sru.ac.ir
In this paper, by considering the processing parameters, including blank holder force, blank
holder gap, and cavity pressure as the most important input factors in the hydroforming …

ANFIS System for Stress Prediction of Cold Heading Fastener Body Process for a Steel Base Composite Aluminum

S Butdee, U Khanawapee - Design, Simulation, Manufacturing: The …, 2024 - Springer
The cold heading process has been widely used for metal forming with different types of
materials, particularly for producing fastener bodies, which are applied to various industrial …

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

P Banerjee, A Karmakar, N Hui - Acta Mechanica Slovaca, 2021 - researchgate.net
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 …