首页|Research on Machine Learning Described by Researchers at National Institute of T echnology Srinagar (Prediction of corrosion rate for friction stir processed WE4 3 alloy by combining PSO-based virtual sample generation and machine learning)
Research on Machine Learning Described by Researchers at National Institute of T echnology Srinagar (Prediction of corrosion rate for friction stir processed WE4 3 alloy by combining PSO-based virtual sample generation and machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting from the National Ins titute of Technology Srinagar by NewsRx journalists, research stated, “The corro sion rate is a crucial factor that impacts the longevity of materials in differe nt applications. After undergoing friction stir processing (FSP), the refined gr ain structure leads to a notable decrease in corrosion rate.” Our news journalists obtained a quote from the research from National Institute of Technology Srinagar: “However, a better understanding of the correlation betw een the FSP process parameters and the corrosion rate is still lacking. The curr ent study used machine learning to establish the relationship between the corros ion rate and FSP process parameters (rotational speed, traverse speed, and shoul der diameter) for WE43 alloy. The Taguchi L27 design of experiments was used for the experimental analysis. In addition, synthetic data was generated using part icle swarm optimization for virtual sample generation (VSG). The application of VSG has led to an increase in the prediction accuracy of machine learning models . A sensitivity analysis was performed using Shapley Additive Explanations to de termine the key factors affecting the corrosion rate. The shoulder diameter had a significant impact in comparison to the traverse speed.”
National Institute of Technology Srinaga rCyborgsEmerging TechnologiesMachine Learning
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