Robotics & Machine Learning Daily News2024,Issue(Jun.4) :59-60.

Researchers’ Work from Indian Institute for Technology Focuses on Machine Learni ng (Prediction of Surface Roughness In Hybrid Magnetorheological Finishing of Si licon Using Machine Learning)

印度理工学院的研究人员的工作重点是机器学习(使用机器学习预测Si Licon混合磁流变抛光中的表面粗糙度)

Robotics & Machine Learning Daily News2024,Issue(Jun.4) :59-60.

Researchers’ Work from Indian Institute for Technology Focuses on Machine Learni ng (Prediction of Surface Roughness In Hybrid Magnetorheological Finishing of Si licon Using Machine Learning)

印度理工学院的研究人员的工作重点是机器学习(使用机器学习预测Si Licon混合磁流变抛光中的表面粗糙度)

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摘要

机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsRx记者在印度孟买的新闻报道,研究表明:“本文提出了基于加工学习的硅双盘化学磁流变抛光工艺预测模型,采用了CatBoost回归法、XGB OOST回归法、随机森林回归法、梯度boost回归法、线性回归法、AdaBoost回归法六种不同的方法来预测表面粗糙度。”新闻记者从印度理工学院获得了这项研究的一句话,采用不同参数组合抛光硅片表面粗糙度的实验数据对模型进行训练,采用梯度boosting算法对硅片表面粗糙度的LS模式数据集进行训练,用K重交叉法验证模型的鲁棒性,并用实验结果采集的监测数据对模型进行验证。建立了双圆盘化学磁流变强化过程中超声辅助模型,CatBoost方法优于其它模型,对无超声辅助和有超声辅助的实验数据,CatBoost模型的预测精度分别为99.92%和98.35%,对无超声辅助和有超声辅助的化学磁流变强化过程,预测模型的最佳误差分别为4.21nm和3.4nm。整理工艺与实验结果吻合良好。

Abstract

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 from Mumbai, India, by NewsRx journalists, research stated, “The machining learning-based predictive model of double disc chemo-magnetorheological finishing process of silicon was proposed in the present manuscript. Six different methods such as CatBoost Regressor, XGB oost, Random Forest Regressor, Gradient Boosting Regressor, Linear Regression, a nd AdaBoost Regressor were used to predict the surface roughness.” The news correspondents obtained a quote from the research from Indian Institute for Technology, “The models were trained by the experimental data of surface ro ughness of silicon wafer polished at combination of different set of parameters. The gradient boosting algorithm was introduced to train the dataset of the mode ls for the surface roughness of the silicon wafer. The robustness of the models was verified with K-fold cross method. The models were verified with the conditi on monitoring data collected by experimental results. The models were also devel oped for ultrasonic assistance during the double disc chemo-magnetorheological f inishing process. The CatBoost approach outperformed the other models. The accur acy of the CatBoost model was 99.92% and 98.35% for the experimental data without and with ultrasonic vibration assistance. The opti mised values from the predicted model were 4.21 nm and 3.4 nm without and with t he assistance of vibration for the chemo-magnetorheological finishing process an d have good agreement with the experimental results.”

Key words

Mumbai/India/Asia/Cyborgs/Emerging T echnologies/Machine Learning/Indian Institute for Technology

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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