Assessment, analysis and optimization for high temperature sliding wear process parameter with additive built nickel base superalloy
Source abstract
Abstract
Abstract Superalloys are highly demanding alloy for high temperature application. The conventional production process and metallurgical sustainability of the superalloy against the applications is the preamble of the existing research. Based on the review it is clear to say that the development of nickel base superalloy with advanced technique without compensating the quality is open for research. The nickel base superalloy is developed through additive manufacturing process following a metal laser sintering technique. The developed alloy is used to perform high temperature sliding wear analysis with the different input process conditions. Applied load, sliding duration and the working temperature are the defined process environment for the investigation. The process conditions are designed with twenty-seven set of experimental trials for statistical analysis and process assessment. The responses on surface roughness and the material loss with respect to the input process parameters are technically assessed and justifications made with the electron microscopic images. The surface topography has influenced due to applied load and the sliding duration. Applied load has influenced the contact area prone with severe wear and the ridges are notice from the microscopic analysis. The statistical analysis has proved that the influence of temperature is less and negotiable compared to the load and time factor. From the optimization process, the optimal parameter for the experimental design is 10 N, 100 °C and 30 min is the ultimate condition to produce best results from the high temperature sliding wear analysis.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · High Entropy Alloys Studies · Additive Manufacturing and 3D Printing Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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