Sumanth Ratna Kandavalli, Chandravathi Chandrasekaran, Amuthakkannan Rajakannu, Vimalanathan Palanikumar, Srinivasan Raju, Venkatesan Srinivasan and Mayakannan Selvaraju
Abstract
Using an artificial neural network (ANN) model trained on milling factors like milling duration (T) and balls-to-powder ratio (R), the crystalline size of TiO2 (PS) nano powder was predicted. High-energy mechanical milling was used to produce this nano powder, and the results of the experiments served as the essential training data. Characterization of the produced TiO2 nanoparticles using X-ray diffraction (XRD). ANN were shown to be superior to multiple linear regressions, with ANN modelling findings agreeing with experimental results with no significant error. Ball milling is used to make TiO2 nano powder, and this experiment verifies the model used to optimize the process.
Published on: November 03, 2023
doi: 10.17756/nwj.2023-s3-163
Citation: Kandavalli SR, Chandrasekaran C, Rajakannu A, Palanikumar V, Raju S, et al. 2023. Improving TiO2 Nano Powder Synthesis Using High-energy Milling and Neural Network Optimization. NanoWorld J 9(S3): S920-S924.
doi: 10.17756/nwj.2023-s3-163
Citation: Kandavalli SR, Chandrasekaran C, Rajakannu A, Palanikumar V, Raju S, et al. 2023. Improving TiO2 Nano Powder Synthesis Using High-energy Milling and Neural Network Optimization. NanoWorld J 9(S3): S920-S924.
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