Search

To ensure the safety and dependability of rubber components, deflection analysis and prediction play a crucial role in process of design. Material property testing and finite element analysis (FEA) are combined to forecast the maximum deflection of a railway elastomeric pad. IRMRA (Indian Rubber Manufacturer's Research Association) developed the chloroprene rubber. Using the FEA method, maximum deflections of an anti-vibration mount under several compressive loads are calculated. Mooney-Rivlin nonlinear hyperelastic three parameter model with element type Plane 182 is used for and FEA. Curve fitting of the uniaxial tensile test results is used to extract three parameter Mooney-Rivlin model constants by using FEA. Then, these Mooney-Rivlin model constants are used to analyze anti-vibration mount and predict the deflections at different compressive loads. The outcomes are contrasted with the technical specifications provided by the Research Designs and Standards Organization of Indian Railway and predicted deflections are within the limits of maximum values allowed. The results are also contrasted with data from literature, and 10% variation is observed between results obtained and literature results. This methodology can be used to predict deflections of any newly developed rubber at initial stage of design. [ABSTRACT FROM AUTHOR]

This research provides a web application for stock prediction that analyze financial news sentiment and predicts market performance using machine learning techniques. Machine learning techniques used are vector based, lexicon based analysis and LSTM. For sentiment analysis and stock prediction. With 86% accuracy, the sentiment analysis algorithm categorizes news headlines as positive or negative. The stock prediction model estimates stock performance with a mean absolute inaccuracy of 3.4 percent. When both models are combined, they forecast stock performance with an accuracy of 83%. The system's potential for usage in the stock market is demonstrated by the results, which provide vital insights into machine learning algorithms for stock data prediction along with sentiment analysis utilizing headlines from financial news. [ABSTRACT FROM AUTHOR]