ArticleMaterials (Basel, Switzerland)2021
Effects of Self-Lubricant Coating and Motion on Reduction of Friction and Wear of Mild Steel and Data Analysis from Machine Learning Approach.
Article in Materials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The applications of coated mild steels are gaining significant attention in versatile industrial areas because of their better mechanical properties, anticorrosive behavior, and reproducibility. The life period of this steel reduces significantly under relative motion in the presence of friction, which is associated with the loss of billion-dollar every year in industry. Productivity is hampered, and economic growth is declined. Several pieces of research have been conducted throughout the industries to seeking the processes of frictional reduction. This study is attributed to the tribological behavior of electroplated mild steel under various operating parameters. The efficiency of commercial lubricant and self-lubrication characteristics of coated layer plays a significant role in the reduction of friction. The reciprocating and simultaneous motion in relation to pin as well as disc are considered during experimentation. The lubricating effects in conjunction with motions are responsible for compensating the friction and wear at the desired level. During frictional tests, the sliding velocity and loads are changed differently. The changes in roughness after frictional tests are observed. The coated and rubbing surfaces are characterized using SEM (Scanning Electron Microscopy) analysis. The coating characteristics are analyzed by EDS (Energy Disperse Spectroscopy), FTIR (Fourier-transform Infrared Spectroscopy), and XRD (X-ray diffraction analysis) methods. The lubrication, reciprocating motion, and low velocity result in low friction and wear. The larger the imposed loads, the smaller the frictional force, and the larger the wear rate. The machine learning (ML) concept is incorporated in this study to identify the patterns of datasets spontaneously and generate a prediction model for forecasting the data, which are out of the experimental range. It can be desired that the outcomes of this research will contribute to the improvement in versatile engineering fields, such as automotive, robotics, and complex motion-based mechanisms where multidimensional motion cannot be ignored.
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