Hot Forging optimization depends on several factors, known with uncertainty: die pre-heating, geometry, tempering, workpiece temperature and shape, lubricant. There are also several objectives: quality, energy consumption and tool life. Global optimization methods require a numerous process evaluations to reach the optimum. While tests can be simulated by Finite Element Method (FEM), most of them were substituted by a Neural Network model. Neural Network training is less sensitive to problem dimension than standard Design of Experiments. The approach is assessed against the traditional Finite Element Optimization by exploiting a case study of a steel disc.

Neural Network Multiobjective Optimization of Hot Forging / D’Addona, Doriana M.; Antonelli, D.. - 67:(2018), pp. 498-503. (Intervento presentato al convegno 11th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME '17 tenutosi a Ischia, Italy nel 19-21 July 2017) [10.1016/j.procir.2017.12.251].

Neural Network Multiobjective Optimization of Hot Forging

Doriana M. D’Addona
;
2018

Abstract

Hot Forging optimization depends on several factors, known with uncertainty: die pre-heating, geometry, tempering, workpiece temperature and shape, lubricant. There are also several objectives: quality, energy consumption and tool life. Global optimization methods require a numerous process evaluations to reach the optimum. While tests can be simulated by Finite Element Method (FEM), most of them were substituted by a Neural Network model. Neural Network training is less sensitive to problem dimension than standard Design of Experiments. The approach is assessed against the traditional Finite Element Optimization by exploiting a case study of a steel disc.
2018
Neural Network Multiobjective Optimization of Hot Forging / D’Addona, Doriana M.; Antonelli, D.. - 67:(2018), pp. 498-503. (Intervento presentato al convegno 11th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME '17 tenutosi a Ischia, Italy nel 19-21 July 2017) [10.1016/j.procir.2017.12.251].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/703352
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 14
  • ???jsp.display-item.citation.isi??? 12
social impact