Microstructure and Properties of Nickel-Based Gradient Coatings Prepared Using Cold Spraying Combined with Laser Cladding Methods.

A cold spray-laser cladding composite gradient coating (CLGC) was successfully formed on a Cu substrate. In comparison with traditional laser cladding gradient coatings (LGC), cold spraying the pre-set Ni-Cu alloy's intermediate transition layer not only mitigates the negative impacts due to the high reflectivity of the copper substrate but also helps to minimize the difference in the coefficients of thermal expansion (CTE) between the substrate and coating. This reduces the overall crack sensitivity and improves the cladding quality of the coating. Besides this, the uniform distribution of hard phases in CLGC, such as Ni11Si12 and Mo5Si3, greatly increases its microhardness compared to the Cu substrate, thus resulting in the value of 478.8 HV0.5 being approximately 8 times that of the Cu substrate. The friction coefficient of CLGC is lowered compared to both the Cu substrate and LGC with respective values of 0.28, 0.54, and 0.43, and its wear rate is only one-third of the Cu substrate's. These results suggest CLGC has excellent anti-wear properties. In addition, the wear mechanism was determined from the microscopic morphology and element distribution and was found to be oxidative and abrasive. This approach combines cold spraying and laser cladding to form a nickel-based gradient coating on a Cu substrate without cracks, holes, or other faults, thus improving the wear resistance of the Cu substrate and improving its usability.

A Novel Superhard, Wear-Resistant, and Highly Conductive Cu-MoSi2 Coating Fabricated by High-Speed Laser Cladding Technique.

The pursuit of an advanced functional coating that simultaneously combines high hardness, wear resistance, and superior electrical conductivity has remained an elusive goal in the field of copper alloy surface enhancement. Traditional solid solution alloying methods often lead to a significant increase in electron scattering, resulting in a notable reduction in electrical conductivity, making it challenging to achieve a balance between high hardness, wear resistance, and high conductivity. The key lies in identifying a suitable microstructure where dislocation motion is effectively hindered while minimizing the scattering of conductive electrons. In this study, a novel Cu-MoSi2 coating was successfully fabricated on a CuCrZr alloy surface using the coaxial powder feeding high-speed laser cladding technique, with the addition of 10-30% MoSi2 particles. The coating significantly enhances the hardness and wear resistance of the copper substrate while maintaining favorable electrical conductivity. As the quantity of MoSi2 particles increases, the coating's hardness and wear resistance gradually improve, with minimal variance in conductivity. Among the coatings, the Cu-30%MoSi2 coating stands out with the highest hardness (974.5 HV0.5) and the lowest wear amount (0.062 mg/km), approximately 15 times the hardness of the copper base material (65 HV0.5) and only 0.45% of the wear amount (13.71 mg/km). Additionally, the coating exhibits a resistivity of 0.173 × 10-6 Ω·m. The extraordinary hardness and wear resistance of these coatings can be attributed to the dispersion strengthening effect of MoxSiy particles, while the high electrical conductivity is due to the low silicon content dissolved into the copper from the released MoSi2 particles, as well as the rapid cooling rates associated with the high-speed laser cladding process.

CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction.

This work proposes a new computational framework for learning a structured generative model for real-world datasets. In particular, we propose to learn a Closed-loop Transcriptionbetween a multi-class, multi-dimensional data distribution and a Linear discriminative representation (CTRL) in the feature space that consists of multiple independent multi-dimensional linear subspaces. In particular, we argue that the optimal encoding and decoding mappings sought can be formulated as a two-player minimax game between the encoder and decoderfor the learned representation. A natural utility function for this game is the so-called rate reduction, a simple information-theoretic measure for distances between mixtures of subspace-like Gaussians in the feature space. Our formulation draws inspiration from closed-loop error feedback from control systems and avoids expensive evaluating and minimizing of approximated distances between arbitrary distributions in either the data space or the feature space. To a large extent, this new formulation unifies the concepts and benefits of Auto-Encoding and GAN and naturally extends them to the settings of learning a both discriminative and generative representation for multi-class and multi-dimensional real-world data. Our extensive experiments on many benchmark imagery datasets demonstrate tremendous potential of this new closed-loop formulation: under fair comparison, visual quality of the learned decoder and classification performance of the encoder is competitive and arguably better than existing methods based on GAN, VAE, or a combination of both. Unlike existing generative models, the so-learned features of the multiple classes are structured instead of hidden: different classes are explicitly mapped onto corresponding independent principal subspaces in the feature space, and diverse visual attributes within each class are modeled by the independent principal components within each subspace.

Microstructure and Wear Resistance of Laser-Clad Ni-Cu-Mo-W-Si Coatings on a Cu-Cr-Zr Alloy.

To improve the wear resistance of high-strength and high-conductivity Cu-Cr-Zr alloys in high-speed and heavy load friction environments, coatings including Ni-Cu, Ni-Cu-10(W,Si), Ni-Cu-10(Mo,W,Si), and Ni-Cu-15(Mo,W,Si) (with an atomic ratio of Mo,W to Si of 1:2) were prepared using coaxial powder-feeding laser cladding technology. The microstructure and wear performance of coatings were chiefly investigated. The results revealed that (Mo,W)Si2 and MoNiSi phases are found in the Ni-Cu-10(Mo,W,Si) and Ni-Cu-15(Mo,W,Si) coating. WSi2 phases are found in the Ni-Cu-10(W,Si) coating. The degree of grain refinement in Ni-Cu-10(Mo,W,Si) was greater than that of the Ni-Cu-10(W,Si) coating after the effect of Mo. The excellent wear resistance and micro-hardness of the Ni-Cu-15(Mo,W,Si) coating were attributed to the increase in its dispersion phase, which were approximately 34.72 mg/km and 428 HV, 27.1% and 590% higher than the Cu-Cr-Zr substrate, respectively. The existence of silicide plays an important role in grain refinement due to the promotion of nucleation and the inhibition of grain growth. In addition, the wear mechanism transformed from adhesive wear in the Ni-Cu coating with no silicides to abrasive wear in the Ni-Cu-15(Mo,W,Si) coating with high levels of silicides.