ANN-AGCS for the prediction of temperature distribution and required energy in hot forging process using finite element analysis. (2020)

• Record Type: Journal Article
• Authors: Dinesh Kumar, S.; Purushothaman, K.; Chandramohan, D.; Mohinish Dushyantraj, M.; Sathish, T.
• Is Part Of: Materials today. Volume 21:Part 1(2020)
• Page Start: 263
• Page End: 267
• Publication Date: 2020
• Subjects: Adaptive genetic algorithm -- Cuckoo search algorithm -- Neural network -- Finite element analysis -- Hot forging process; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2019.05.426
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: This work calculates the flow of stress of deforming metal as a function of temperature, strain and strain rate using a hybrid adaptive genetic algorithm and cuckoo search (ANN-AGCS) model. The flow behavior of material and the temperature variations in hot upsetting process are predicted by using the finite element analysis. To record the hot deformation performance through the force displacement. In this model to perform a hot non isothermal forging of a low carbon steel. A good decision is done between the predicted data and the measured results.