A method, the morphology of screen printed carbon nanotube pastes is modified using a hard hairbrush, is presented. In this way, the organic matrix material is preferentially removed. Compared to those untreated films...A method, the morphology of screen printed carbon nanotube pastes is modified using a hard hairbrush, is presented. In this way, the organic matrix material is preferentially removed. Compared to those untreated films, the turn-on electric field of the treated film decreases from 2.2V/μm to 1.6V/μm, while the total emission current of the treated increases from 0.6mA/cm2 to 3mA/cm2, and uniform emission site density image has also been observed.展开更多
This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data colle...This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data collected from an experimental of EDM process, whereas several research objectives have been outlined such as experimenting machining material for selected gap current, identifying machining parameters for ANN variables and selecting appropriate size of data selection. The experimental data （input variables） of copper-electrode and steel-workpiece is based on a selected gap current where pulse on time, pulse off time and sparking frequency have been chosen at optimum value of Material Removal Rate （MRR）. In this paper, the result has significantly demonstrated that the ANN model is capable of predicting the MRR with low percentage prediction error when compared with the experimental result.展开更多
文摘A method, the morphology of screen printed carbon nanotube pastes is modified using a hard hairbrush, is presented. In this way, the organic matrix material is preferentially removed. Compared to those untreated films, the turn-on electric field of the treated film decreases from 2.2V/μm to 1.6V/μm, while the total emission current of the treated increases from 0.6mA/cm2 to 3mA/cm2, and uniform emission site density image has also been observed.
文摘This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data collected from an experimental of EDM process, whereas several research objectives have been outlined such as experimenting machining material for selected gap current, identifying machining parameters for ANN variables and selecting appropriate size of data selection. The experimental data （input variables） of copper-electrode and steel-workpiece is based on a selected gap current where pulse on time, pulse off time and sparking frequency have been chosen at optimum value of Material Removal Rate （MRR）. In this paper, the result has significantly demonstrated that the ANN model is capable of predicting the MRR with low percentage prediction error when compared with the experimental result.
