摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑-调查人员讨论机器学习的新发现。据新华社记者从湖南发回的新闻报道称，“防眩玻璃以其优异的防眩性能而闻名，因其在电子显示器上的广泛应用而引起了广泛的研究兴趣。玻璃蚀刻是防眩玻璃生产的关键步骤，但该工艺配方的优化取决于许多因素。”本研究的资金来源于湖南省科技创新计划项目。我们的新闻编辑从化学化工学院的研究中得到一句话：“在这项工作中，研究的重点是优化蚀刻溶液的组成和蚀刻时间，以达到预期的粗糙度，”本文还介绍了一种将实验刻蚀数据与机器刻蚀模型预测相结合的创新方法,以刻蚀成分和刻蚀时间为特征输入,以得到的玻璃表面粗糙度为目标输出,利用Random Forest算法,从实验数据中采集刻蚀数据集,并对实验数据进行了分析。分析和预测了这些刻蚀变量对表面粗糙度的影响，通过实验验证了该方法的准确性和可行性，可以准确预测玻璃表面粗糙度，模型的R 2达到0.9165，RSME仅为22.6690.。
Abstract
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.”
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