WC-reinforced Ni60 composite coatings with different types of WC particles were prepared on 304 stainless steel surface by laser cladding. The influences of spherical WC, shaped WC, and flocculent WC on the microstructures and properties of composite coatings were investigated. The results showed that three types of WC particles distribute differently in the cladding coatings, with spherical WC particles stacking at the bottom, shaped WC aggregating at middle and lower parts, with flocculent WC particles dispersing homogeneously. The hardnesses, wear resistances, corrosion resistances, and thermal shock resistances of the coatings are significantly improved compared with the stainless steel substrate, regardless of the type of WC that is added, and especially with regard to the microhardness of the cladding coating; the addition of spherical or shaped WC particles can be up to 2000 HV0.05 in some areas. Flocculent WC, shaped WC, and spherical WC demonstrate large to small improvements in that order. From the results mentioned above, the addition of flocculent WC can produce a cladding coating with a uniform distribution of WC that is of higher quality compared with those from spherical WC and shaped WC.

A new method is developed to monitor joint quality based on the information collection and process in spot welding. First, twelve parameters related to weld quality are mined from electrode displacement signal on the basis of different phases of nugget formation marked by simultaneous dynamic resistance signal. Second, through correlation analysis of the parameters and taking tensile-shear strength of the spot-welded joint as evaluation target, different characteristic parameters are reasonably selected. At the same time, linear regression, nonlinear regression and radial basis function (RBF) neural network models are set up to evaluate weld quality between the selected parameters and tensileshear strength. Finally, the validity of the proposed models is certified. Results show that all of the models can be used to monitor joint quality. For the RBF neural network model, which is more effective for monitoring weld quality than the others, the average error validated is 2.88% and the maximal error validated is under 10%.

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In the brazing joint between titanium alloy and stainless steel, a lot of Fe-Ti intermetallic compounds (IMCs) can be easily formed to make joints crack. A lap resistance brazing process with metal powder layers on both sides of the filler metal was used to solve this problem. The microstructure and metallurgical behavior of joints was studied through comparative experiments. The result showed that Nb, V and Cr powders and the solder reacted with the base material to form a new phase, which replaced the Ti-Fe brittle phase in the joint. At the same time, metal powder clusters hindered the diffusion of Ti and Fe elements and improved the distribution of new phases. The established atomic reaction model revealed the metallurgical behavior and formation mechanism of the joints. Therefore, the intervening position of the metal powder layer and the multi-reaction zone structure are the main reasons the shear strength of joints is improved.

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