This paper presents a combination of numerical and analytical models for selection of optimal parameters on turning Ti6Al4V. In particular, the work demonstrates the efficiency of the combined models to properly design the process tremendously reducing the time requested to verify the final product characteristics. The applied models include the prediction of surface integrity characteristics such as grain size, hardness changes and residual stresses but also fatigue life prediction based on surface characteristics. The above models have been modified and updated according to the material characteristics, centered on physics-based equations and assumptions, process and phenomena taken into consideration for the specific setup. In particular, the models follow the overall process starting from the cutting phase up to the final fatigue operational performance. The proposed approach demonstrates that it is possible to predict, with adequate accuracy, the influence of the machining process on surface state and final performance in terms of fatigue life. Thus, it is possible to drastically reduce the time and efforts to build up knowledge based on experimental trials including the proposed models into an industrial context.
Development of customized physics-based predictive models for improved performance in turning of Ti6Al4V
Rotella, G;Del Prete, A
2022-01-01
Abstract
This paper presents a combination of numerical and analytical models for selection of optimal parameters on turning Ti6Al4V. In particular, the work demonstrates the efficiency of the combined models to properly design the process tremendously reducing the time requested to verify the final product characteristics. The applied models include the prediction of surface integrity characteristics such as grain size, hardness changes and residual stresses but also fatigue life prediction based on surface characteristics. The above models have been modified and updated according to the material characteristics, centered on physics-based equations and assumptions, process and phenomena taken into consideration for the specific setup. In particular, the models follow the overall process starting from the cutting phase up to the final fatigue operational performance. The proposed approach demonstrates that it is possible to predict, with adequate accuracy, the influence of the machining process on surface state and final performance in terms of fatigue life. Thus, it is possible to drastically reduce the time and efforts to build up knowledge based on experimental trials including the proposed models into an industrial context.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
