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