In this article, the results of research on the metal-mineral-type abrasive wear of a wear-resistant plate made by a tubular electrode with a metallic core and an innovative chemical composition using the manual metal arc hardfacing process were presented. The properties of the new layer were compared to the results of eleven wear plates manufactured by global suppliers, including flux-cored arc welding gas-shielded (FCAW-GS, Deposition Process Reference Number: 138), flux-cored arc welding self-shielded (FCAW-SS, Deposition Process Reference Number: 114), automated hardfacing, and manual metal arc welding (MMAW, Deposition Process Reference Number: 111) hardfacing T Fe15 and T Fe16 alloys, according to EN 14700:2014. Characterization of the hardfaced layers was achieved by using hardness tests, optical microscopy, confocal microscopy, scanning electron microscopy, and EDS (Energy Dispersive Spectroscopy) and X-ray diffraction analyses. Based on wear resistance tests in laboratory conditions, in accordance with ASTM G65-00: Procedure A, and surface layer hardness tests, in accordance with PN-EN ISO 6508-1, the wear plates most suitable for use in metal-mineral conditions were chosen. The results demonstrated the high metal-mineral abrasive wear resistance of the deposit weld metal produced by the new covered tubular electrode. The tubular electrode demonstrated a high linear correlation between the surface wear resistance and the hardness of the metal matrix of the tested abrasive wear plates. In addition to hardness, size, shape, the dispersion of strengthening phases, and the base metal content, depending on hardfacing technology and technological parameters, impact wear resistance is represented by volumetric loss caused by effect-free or constrained dry abrasive medium contact. The presented results can be used in machine part material selection and wear planning for applications in inspection, conservation, and regeneration interval determination. The obtained results will be applied in a real-time wear rate prediction system based on the measurement of the working parameters.
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