Investigation of thermo-mechanical fatigue (TMF) phenomena usually involves complex experimentation and long testing runs, as well as difficulties in specifying and fitting appropriate models. Moreover, the experimental techniques usually consider temperature in the specimen to be constant, or slowly varying, often with a uniform distribution of temperature in the cross section of the specimen but it is not a plausible assumption. Aimed at extending the investigation to hot forging dies, where the variation of some hundreds of °C is operated in a few seconds and thermal gradient (some tens of °C per mm) is present, the experimental configuration become more complex. The present paper focuses on a recently proposed experiment for a TMF investigation of hot forging dies where Design of Experiments techniques are used to fit a model for TMF lives. This study allows to detect which factors are more important in affecting the TMF, to “describe” the relationship between TMF and these factors and to predict TMF lives. Experimental data are analyzed using Response Surface Modelling (RSM) in order to fit an empirical linear model.
Response surface modelling of thermo-mechanical fatigue in hot forging
BONNINI, Stefano;
2006
Abstract
Investigation of thermo-mechanical fatigue (TMF) phenomena usually involves complex experimentation and long testing runs, as well as difficulties in specifying and fitting appropriate models. Moreover, the experimental techniques usually consider temperature in the specimen to be constant, or slowly varying, often with a uniform distribution of temperature in the cross section of the specimen but it is not a plausible assumption. Aimed at extending the investigation to hot forging dies, where the variation of some hundreds of °C is operated in a few seconds and thermal gradient (some tens of °C per mm) is present, the experimental configuration become more complex. The present paper focuses on a recently proposed experiment for a TMF investigation of hot forging dies where Design of Experiments techniques are used to fit a model for TMF lives. This study allows to detect which factors are more important in affecting the TMF, to “describe” the relationship between TMF and these factors and to predict TMF lives. Experimental data are analyzed using Response Surface Modelling (RSM) in order to fit an empirical linear model.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
