The machining process is primarily used to remove material using cutting tools.Any variation in tool state affects the quality of a finished job and causes disturbances.So,a tool monitoring scheme(TMS)for categorizati...The machining process is primarily used to remove material using cutting tools.Any variation in tool state affects the quality of a finished job and causes disturbances.So,a tool monitoring scheme(TMS)for categorization and supervision of failures has become the utmost priority.To respond,traditional TMS followed by the machine learning(ML)analysis is advocated in this paper.Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation.In the current study,investigation on the single point cutting tool is carried out while turning a stainless steel(SS)workpeice on the manual lathe trainer.The vibrations developed during this activity are examined for failure-free and various failure states of a tool.The statistical modeling is then incorporated to trace vital signs from vibration signals.The multiple-binary-rule-based model for categorization is designed using the decision tree.Lastly,various tree-based algorithms are used for the categorization of tool conditions.The Random Forest offered the highest classification accuracy,i.e.,92.6%.展开更多
The red-orange emitting phosphor YBO3：Eu3＋was prepared by aldo-keto method and solid state diffusion. Aldo-keto method implied to decrease the processing time and heating temperature. The red-orange emitting phospho...The red-orange emitting phosphor YBO3：Eu3＋was prepared by aldo-keto method and solid state diffusion. Aldo-keto method implied to decrease the processing time and heating temperature. The red-orange emitting phosphor was characterized by X-ray diffraction （XRD）, scanning electron microscopy （SEM）, as well as emission and excitation photoluminescence spectra re-corded at room temperature. The result of aldo-keto method showed that the phosphor YBO3：Eu3＋could be obtained at 900 °C in less time^60%as compared to solid state diffusion （SSD）. The material showed that the strongest emission peak at 595 nm under excitation at 233 nm was only due to forced magnetic dipole 5D0→7F1 transition of Eu3＋ions. Significantly, the emission inten-sity of YBO3：Eu3＋phosphor prepared by aldo-keto method was relatively higher as compared to that obtained by the solid state diffusion.展开更多
文摘The machining process is primarily used to remove material using cutting tools.Any variation in tool state affects the quality of a finished job and causes disturbances.So,a tool monitoring scheme(TMS)for categorization and supervision of failures has become the utmost priority.To respond,traditional TMS followed by the machine learning(ML)analysis is advocated in this paper.Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation.In the current study,investigation on the single point cutting tool is carried out while turning a stainless steel(SS)workpeice on the manual lathe trainer.The vibrations developed during this activity are examined for failure-free and various failure states of a tool.The statistical modeling is then incorporated to trace vital signs from vibration signals.The multiple-binary-rule-based model for categorization is designed using the decision tree.Lastly,various tree-based algorithms are used for the categorization of tool conditions.The Random Forest offered the highest classification accuracy,i.e.,92.6%.
文摘The red-orange emitting phosphor YBO3：Eu3＋was prepared by aldo-keto method and solid state diffusion. Aldo-keto method implied to decrease the processing time and heating temperature. The red-orange emitting phosphor was characterized by X-ray diffraction （XRD）, scanning electron microscopy （SEM）, as well as emission and excitation photoluminescence spectra re-corded at room temperature. The result of aldo-keto method showed that the phosphor YBO3：Eu3＋could be obtained at 900 °C in less time^60%as compared to solid state diffusion （SSD）. The material showed that the strongest emission peak at 595 nm under excitation at 233 nm was only due to forced magnetic dipole 5D0→7F1 transition of Eu3＋ions. Significantly, the emission inten-sity of YBO3：Eu3＋phosphor prepared by aldo-keto method was relatively higher as compared to that obtained by the solid state diffusion.
