Performance of Ni60 high hardness wear-resistant layer prepared by laser cladding on hydraulic support materials surface
To achieve remanufacturing and repair of damaged and failed hydraulic support columns,a Ni60 alloy layer was prepared on the surface of 27SiMn steel material for hydraulic support columns using laser cladding technology.The influence of laser process parameters on dilution rate of the cladding layer was studied through orthogonal experimental method,and the optimal process parameters were determined.Microstructure,hardness and wear resistance of the cladding layer were analyzed by means of ultra depth-of-field microscopy,microhardness tester and friction and wear testing machine.The results show that the factors that affect the dilution rate of the cladding layer,in a descending order by influence,are:laser power＞scanning speed＞powder feeding speed＞defocusing distance.The optimal process parameter combination based on dilution ratio is A1B3C3D3,which includes a laser power of 1500 W,a scanning speed of 22 mm/s,a defocusing distance of+1 mm,and a powder feeding speed of 12 g/min.The overall grain size of the cladding layer is fine and uniform.The upper part of the cladding layer is mainly composed of equiaxed crystals,the middle part is mainly composed of equiaxed crystals and dendrites,and the lower part is mainly composed of cellular and columnar crystals growing along the fusion interface.The average hardness of the cladding layer is 729.5 HV0.5,which is 2.32 times of the substrate,and the highest hardness occurs in the equiaxed grain zone at the top of the cladding layer,which is 756.9 HV0.5.The micro hardness indentation of the cladding layer has straight edges,smooth corners and no cracks,and the cladding layer has good toughness.The friction coefficient of the cladding layer is significantly lower than that of the substrate,and the stability is better.The wear mass loss of the cladding layer is only 58％of the substrate,and the wear mechanism is abrasive wear and slight adhesive wear.
NETL
NSTL
万方数据
