Grain size and temperature evolutions during linear friction welding of Ni-base superalloy Waspaloy: Simulations and experimental validations

Abstract

This research study was aimed at investigating the influence of linear friction welding parameters on grain size alteration and temperature distribution of Ni-base superalloy Waspaloy. A 3D finite element model was developed to predict average grain size and peak temperature as responses. The linear friction welding parameters consisted of oscillation amplitude, oscillation frequency, and applied pressure. Initially, the evolution of the average grain size as a function of the most influential process parameters was subsequently modeled based on the Johnson-Mehl-Avrami-Kolmogorov recrystallization model and were then validated with experimental results. Then, D-optimal design of experiments and analysis of variance were conducted to determine the most influential process parameters that affect the average grain size and peak temperature of the welded joint. Thereafter, response surface method was employed to obtain the regression models of the responses. The analysis of variance demonstrated that the P-value of the regression models was smaller than 5% and R2, Radj2, and RPred2 were between 87% and 97%, which showed that the predictive regression models of PT and AGS can be used with a high level of confidence. The regression models were then validated by selecting two extra LFW tests in the space of the DoE. The optimum values of the welding parameters were determined to minimize the responses. The multi-criteria optimization analysis showed that both average grain size and peak temperature were more dependent on pressure than oscillation amplitude and frequency. The developed finite element and regression models can be utilized as a predictive tool for the design of joining industrial components, which minimize expensive and time-consuming experimental tests and measurements.