首页|Department of Mechanical Engineering Reports Findings in Machine Learning (Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fibe r Fraction on Physical and Mechanical Features and Responses of the Tribo System and ...)
Department of Mechanical Engineering Reports Findings in Machine Learning (Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fibe r Fraction on Physical and Mechanical Features and Responses of the Tribo System and ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of Karnataka, India, by N ewsRx editors, research stated, "Recent studies focus on enhancing the mechanica l features of natural fiber composites to replace synthetic fibers that are high ly useful in the building, automotive, and packing industries. The novelty of th e work is that the woven areca sheath fiber (ASF) with different fiber fraction epoxy composites has been fabricated and tested for its tribological responses o n three-body abrasion wear testing machines along with its mechanical features." Financial support for this research came from Deanship of Scientific Research, K ing Khalid University. Our news journalists obtained a quote from the research from the Department of M echanical Engineering, "The impact of the fiber fraction on various features is examined. The study also revolves around the development and validation of a mac hine learning predictive model using the random forest (RF) algorithm, aimed at forecasting two critical performance parameters: the specific wear rate (SWR) an d the coefficient of friction (COF). The void fraction is observed to vary betwe en 0.261 and 3.8% as the fiber fraction is incremented. The hardne ss of the mat rises progressively from 40.23 to 84.26 HRB. A fair ascent in the tensile strength and its modulus is also observed. Even though a short descent i n flexural strength and its modulus is seen for 0 to 12 wt % compo site specimens, they incrementally raised to the finest values of 52.84 and 2860 MPa, respectively, pertinent to the 48 wt % fiber-loaded specimen . A progressive rise in the ILSS and impact strength is perceptible. The wear be havior of the specimens is reported. The worn surface morphology is studied to u nderstand the interface of the ASF with the epoxy matrix. The RF model exhibited outstanding predictive prowess, as evidenced by high-squared values coupled with low mean-square error and mean absolute error metrics."
KarnatakaIndiaAsiaCyborgsEmergin g TechnologiesMachine Learning
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