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Improving the wear resistance and corrosion resistance of 60Si2Mn steel is an important issue in agricultural machinery. A Rockit 606 coating layer may exhibit excellent performance in wear resistance and corrosion resistance. However, there are very a few public reports and articles involving the topic of a Rockit 606 laser cladding layer on a steel 60Si2Mn surface. It is of great importance to research Rockit 606 laser cladding layers. This work focuses on studying the microstructure and properties of Rockit 606 coating layers with different scanning speeds by disk laser cladding. Firstly, the laser cladding platform was designed and set up. Secondly, the laser cladding parameters were designed, and then the laser cladding experiment was conducted, and the Rockit 606 coating layers were obtained. And finally, the microstructure, phase distribution, corrosion resistance, surface hardness, and wear resistance of the coating layers were measured and analyzed. A scanning electron microscope (SEM), X-ray diffractometer (XRD), electrochemical workstation, and microhardness tester were used in this work. It was found that the microstructure Rockit 606 alloy coating consists of γ-Fe, V8C7, and Cr7C3. The microhardness of the Rockit 606 alloy coatings decreased with an increase in the scanning speed. When the scanning speed was 4 mm/s, the highest microhardness value reached 867.2 HV, which is about three times of that of the substrate. The average coefficients of friction (COFs) of the coatings decreased with an increase in the scanning speed, which led to the corresponding decrease of the wear rate. When the scanning speed was 4 mm/s, the wear behavior of the coating was mainly oxidative wear and a small amount of adhesive wear. The self-corrosion current density of the coatings prepared by laser cladding in a 3.5 wt.% NaCl solution is one order of magnitude lower than that of the substrate, indicating that the coatings have better corrosion resistance properties. [ABSTRACT FROM AUTHOR]

Large residual stress induced by significant difference in the coefficient of thermal expansion compromises the mechanical performance of a brazed joint between the WC–Co cemented carbide (YG20) and 3Cr13 stainless steel in a cutting tool of shield machine. In this study, a melamine formaldehyde foam precursor was utilized to produce a 3D hollow carbon foam (HCF) used as interlayer through a high‐temperature carbonization process to aid in the brazing process of YG20 and 3Cr13. First, the morphology and microstructure of the HCF interlayer were revealed as a defect‐rich carbon shell with a hollow structure. Afterward, the effects of introducing the as‐prepared HCF interlayer on the joint microstructure and shear performance were investigated. Results indicate that the HCF interlayer contributed to phase homogenization and, moreover, the residual stress relieving through in situ forming a core (metallic matrix)–sheath (ceramic shell) structure. This method outperformed the direct brazing approach without using an interlayer, with a 41% increase in the joint shear strength due to the synergistic strengthening mechanism of the HCF through residual stress reliving and joint seam toughening. Based on the results, in spite of the residual stress, the fast load transfer and the compensation from the matrix core to the ceramic sheath are responsible for the improved mechanical performance of the joint. [ABSTRACT FROM AUTHOR]

Refractory high-entropy alloys (RHEAs) typically exhibit excellent high-temperature strength but limited ductility. In this study, a comprehensive machine learning strategy with integrated material knowledge is proposed to predict the elongation of TiZrNbVAl RHEAs. By referring to the ductility theories, a set of cost-effective material features is developed with various mathematical forms of thermodynamic parameters. These features are proven to effectively incorporate material knowledge into ML modeling. They also offer potential alternatives to those obtained from costly first-principles calculations. Based on Pearson correlation coefficients, the linear relationships between pairwise features were compared, and the seven key features with the greatest impact on the model were selected for ML modeling. Regression tasks were performed to predict the ductility of TiZrNbVAl, and the CatBoost gradient boosting algorithm exhibiting the best performance was eventually selected. The established optimized model achieves high predictive accuracies exceeding 0.8. These key features were further analyzed using interpretable ML methods to elucidate their influences on various ductility mechanisms. According to the ML results, different compositions of TiZrNbVAl with excellent tensile properties were prepared. The experimental results indicate that Ti44Zr24Nb17V5Al10 and Ti44Zr26Nb8V13Al9 both exhibited ultimate tensile strengths of approximately 1180 MPa and elongations higher than 21%. They verified that the ML strategy proposed in this study is an effective approach for predicting the properties of RHEAs. It is a potential method that can replace costly first-principles calculations. Thermodynamic parameters have been shown to effectively predict alloy ductility to a certain extent. [ABSTRACT FROM AUTHOR]