Study of Wear of an Alloyed Layer with Chromium Carbide Particles after Plasma Melting

Abstract

Depending on operating conditions, metals and alloys are exposed to various factors: wear, friction, corrosion, and others. Plasma surface alloying of machine and tool parts is now an effective surface treatment process of commercial and strategic importance. The plasma surface alloying process involves adding the required elements (carbon, chromium, titanium, silicon, nickel, etc.) to the surface layer of the metal during the melting process. A thin layer of the compound is pre-applied to the substrate, then melted and intensively mixed under the influence of a plasma arc, and during the solidification process, a new surface layer with optimal mechanical properties is formed. Copper-based alloys—Cu-X, where X is Fe, Cr, V, Nb, Mo, Ta, and W—belong to an immiscible binary system with high mechanical strength, electrical conductivity, and magnetism (for Fe-Cu) and also high thermal characteristics. At the same time, copper-based alloys have low hardness. In this article, wear tests were carried out on coatings obtained by plasma alloying of CuSn10 and CrxCy under various friction conditions. The following were chosen as a modifying element: chromium carbide to increase hardness and iron to increase surface tension. It is noted that an increase in the chromium carbide content to 20% leads to the formation of a martensitic structure. As a result, the microhardness of the layer increased to 700 HV. The addition of CuSn10 + 20% CrxCy and an additional 5% iron to the composition of the coating improves the formation of the surface layer. Friction tests on fixed abrasive particles were carried out at various loads of 5, 10, and 50 N. According to the test results, the alloy layer of the Fe-Cr-C-Cu-Sn system has the greatest wear resistance under abrasive conditions and dry sliding friction conditions.

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

“…The number of decision trees, the maximum depth of the tree, and the learning rate are the three key hyperparameters of XGBoost. In this paper, the search range of the number of decision trees, the maximum depth of the tree, and the learning rate are [50, 100, 200, 400, 500], [3,4,5,6,7,8,9,10], and [0.01, 0.05, 0.1, 0.15, 0.2], respectively.…”

“…If the process parameters are not appropriately chosen, excessive heat input can result in deformation, high residual stresses [7], poor surface quality, and splatter phenomena [8]. Karlina et al [9] emphasized the potential for optimizing process parameters to improve material characteristics. Thus, choosing the appropriate process parameter based on the deposition trajectory yielded by the slicing process is a critical step in the WAAM process.…”

Three-Dimensional Columnar Microstructure Representation Using 2D Electron Backscatter Diffraction Data for Additive-Manufactured Haynes®282®

“…Furthermore, the study suggests averaging results from multiple cropped EBSD datasets approximating the findings from larger datasets effectively, affirming the sufficiency of this combined approach for micromechanical modeling. Additionally, the studies conducted by Balanovskiy et al [23,24] and Karlina et al [25] show the important findings related to microstructure homogeneity and mechanical properties of AM materials, highlighting the potential for optimizing AM processes to achieve the desired material characteristics.…”

Experimental Investigation on Magnetic Abrasive Finishing for Internal Surfaces of Waveguides Produced by Selective Laser Melting

“…Selective Laser Melting (SLM) is an advanced manufacturing method that utilizes a high-energy laser beam to selectively melt and solidify metal powder within specific regions, thereby achieving "near-net-shape" fabrication of three-dimensional complex structures [1][2][3]. Based on the principle of "layer-by-layer" deposition and stacking, this technology enables direct fabrication of solid objects from digital models, offering advantages such as design flexibility, short production cycles, and cost savings.…”