首页|New Machine Learning Findings from College of Engineering Trivandrum Discussed ( Potential Use of Transesterified Vegetable Oil Blends As Base Stocks for Metalwo rking Fluids and Cutting Forces Prediction Using Machine Learning Tool)
New Machine Learning Findings from College of Engineering Trivandrum Discussed ( Potential Use of Transesterified Vegetable Oil Blends As Base Stocks for Metalwo rking Fluids and Cutting Forces Prediction Using Machine Learning Tool)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news reporting originating in Kerala, India, by NewsRx journalists, research stated, "The majority of lubricants used around the world are mineral oil-based, which causes environmental and health risks. The industry is under pressure to develop eco-friendly and biodegradable lubricants due to p oor degradability and the depletion of mineral oil resources." Financial support for this research came from Kerala State Council for Science, Technology, and Environment [KSCSTE], Kera la, India. The news reporters obtained a quote from the research from the College of Engine ering Trivandrum, "Vegetable oils (VO) are being considered as an alternative so urce of lubricants due to their biodegradability, renewability, low toxicity, an d good lubricating characteristics. The VO also suffers few drawbacks such as li mited oxidation stability and poor low-temperature performance. Blending, chemic al modification, and additives can improve the oil's lubricating properties. The objective of the study is to formulate bio-lubricants from vegetable oils such as rice bran oil (RBO), jatropha oil (JO), and a blend of RBO and JO. Transester ification was performed on the vegetable oils, and all samples were assessed for tribological characteristics, oxidative stability, corrosion, and emulsion stab ility using ASTM and international standards. A lathe machine with a tool dynamo meter was used to test the performance of the formulated cutting fluid. Cutting forces were assessed and compared to those of a commercial cutting fluid. Machin e learning algorithms were also used to forecast cutting forces, which were then compared to experimental values. The 1:1 ratio of transesterified RBO and JO ha s shown a better coefficient of friction and superior oxidative stability. Also, the 40% emulsifier in the oil has shown good stability."
KeralaIndiaAsiaCyborgsEmerging T echnologiesLubricantsMachine LearningRisk and PreventionCollege of Engin eering Trivandrum
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