首页|Recent Studies from Affiliated to Visvesvaraya Technological University Add New Data to Machine Learning (Machine learning and Taguchi techniques for predicting wear mechanisms of Ni-Cu alloy composites)
Recent Studies from Affiliated to Visvesvaraya Technological University Add New Data to Machine Learning (Machine learning and Taguchi techniques for predicting wear mechanisms of Ni-Cu alloy composites)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting from Karnataka, India, by New sRx journalists, research stated, "Nickel-Copper alloys hybrid composite was for med in an induction furnace set up on a sand substrate." The news reporters obtained a quote from the research from Affiliated to Visvesv araya Technological University: "With different percentages of Al2O3 (3, 6, 9 an d 12 wt%) and TiO2 (constant 9 wt%) reinforcements, th e goal is to examine the wear behavior and friction coefficient of Ni-TiO2-Al2O3 . The factors considered for the wear analysis were sliding distance (1500, 1000 , and 500 m), applied load (25,50, and 75 N), and sliding velocity (1.46, 2.93, and 4.39 m/s). The pin-on-disc equipment is utilized to perform different wear tests are carried out using in accordance with the Taguchi L27 orthogonal array. The machine learning used to correlate between actual and anticipated values fo r both metrics is strong, with a reasonable error margin. The Mean Squared Error (MSE) for the wear rate was 0.1025 (10.25%) in the Linear Regressi on model and 0.2390 (23.89%) in the Random Forest model. Regression analysis determined the impact of several parameters on wear rate, whilst machi ne learning approaches expanded the evaluation of wear rate and coefficient of f riction beyond experimental data."
Affiliated to Visvesvaraya Technological UniversityKarnatakaIndiaAsiaCyborgsEmerging TechnologiesMachine Lea rning
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