| Superalloy are widely used in large-scale industrial fields with severe service conditions due to their superior performance,but the current research on them is not perfect.In this paper,nickel-based superalloy GH3536 forgings are used as raw materials,and they are finally made into standard welding test specimens with a thickness of 1.5mm through wire-cutting,vacuum electron beam welding,polishing and other processing techniques,and a series of experimental studies are carried out.Through the processing and analysis of the test results,the following conclusions are made:(1)GH3536 alloy forgings base material grains present irregular polygonal shapes,uniformly and densely distributed,and the grain size of the weld area is larger than the base material grains,which are elongated;(2)Hardness The hardness value of the weld area is between 225-250 HV,and the base material is between 200-220HV;(3)The tensile strength of the GH3536 alloy welded structure at room temperature is 732.06 MPa,and the yield strength is 348.01 MPa,which is very different from the matrix and has equal strength;the higher the temperature,the static strength parameters of the welded structure will decrease accordingly;(4)During the static stretching process,the strain of the welded joint is mainly concentrated in the base metal area,and the maximum can reach 50%.The weld area has a small strain due to large grains and poor plasticity,which is less than 30%;(5)The hysteresis curve of the welded joint is not closed and shifts to the right;at the later stage of the fatigue-creep interactive cycle,dislocation movement is more likely to occur,and the average strain and average strain rate also increase more rapidly;(6)The average strain rate method,frequency separation method(FS)and strain energy correction method(SEFS)three life prediction models in the prediction accuracy of this test are coMPared,and it is found that the average strain of static creep and cyclic creep is considered The prediction accuracy of the rate method is significantly better than the other two prediction methods. |