首页|Research from Prince Sultan University Provide New Insights into Machine Learnin g (Improving the performance of damage repair in thin-walled structures with ana lytical data and machine learning algorithms)
Research from Prince Sultan University Provide New Insights into Machine Learnin g (Improving the performance of damage repair in thin-walled structures with ana lytical data and machine learning algorithms)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on artificial intelligence have been published. According to news reporting from Riyadh, Saudi Arabia, by N ewsRx journalists, research stated, “In the last four decades, bonded composite repair has proven to be an effective method for addressing crack damage propagat ion.” Our news editors obtained a quote from the research from Prince Sultan Universit y: “On the other hand, machine learning (ML) has made it possible to employ a va riety of approaches for mechanical and aerospace problems and such significant a pproach is the repair mechanism and hence ML algorithms used to enhance in the p resent work. The current work investigates the effect of the single-sided compos ite patch bonded on a thin plate under plane stress conditions. An analytical mo del was formulated for a single-sided composite patch repair using linear elasti c fracture mechanics and Rose’s analytical modelling. From the analytical model, the stress intensity factors (SIF) were calculated by varying all possible para meters of the model. Next, ML algorithms were selected, and comparative studies were conducted for the best possible performance and to identify the parametric effects on optimum SIF. Also, the analytical model is validated with existing wo rk, and it shows good agreement with less than 10% error.”
Prince Sultan UniversityRiyadhSaudi ArabiaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine Learning
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