首页|New Machine Learning Study Findings Have Been Reported from School of Chemistry and Chemical Engineering (Prediction and Analysis Etching Model of Anti-glare Gl ass Roughness Based On Machine Learning Method)
New Machine Learning Study Findings Have Been Reported from School of Chemistry and Chemical Engineering (Prediction and Analysis Etching Model of Anti-glare Gl ass Roughness Based On Machine Learning Method)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news reporting originating from Hunan, People’s R epublic of China, by NewsRx correspondents, research stated, “Antiglare glass, renowned for its exceptional anti-glare properties, has attracted substantial re search interest for its wide application in the electronic displays. Glass etchi ng is the key step of the anti-glare glass production, but the formula optimizat ion of this process depends on a numerous factors.” Financial support for this research came from Hunan Provincial Science and Techn ology Innovation Plan Project. Our news editors obtained a quote from the research from the School of Chemistry and Chemical Engineering, “In this work, the research focus is on optimizing th e composition of etching solution and duration of etching to achieve a desired r oughness, recorded at 137.80 nm in our experiment. This study also introduces an innovative approach that integrates experimental etching data with machine lear ning model predictions. The etch dataset was collected from the experimental etc hing data, using the etch component and etching duration as the featured inputs, with the resultant glass surface roughness as the target output. Aided by the R andom Forest algorithm, how these etching variables influence surface roughness were analyzed and predicted. The accuracy and feasibility of this method are ver ified by experimental validation, allowing accurate predictions of glass surface roughness. The R 2 of the model reaches 0.9165, and RSME is only 22.6690.”
HunanPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningSchool of Chemistry and Chemi cal Engineering
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