首页|Studies from University Center Describe New Findings in Machine Learning (Predic tive modelling of residual stress in turning of hard materials using radial basi s function network enhanced with principal component analysis)
Studies from University Center Describe New Findings in Machine Learning (Predic tive modelling of residual stress in turning of hard materials using radial basi s function network enhanced with principal component analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on ar tificial intelligence. According to news reportingfrom Sao Paulo, Brazil, by Ne wsRx journalists, research stated, “This study proposes a hybrid machinelearnin g (ML) model that combines a radial basis function network (RBFN) with principal componentanalysis (PCA) to predict residual stress (RS) in the machining proce ss. Higher temperatures and plasticdeformation can generate RS conditions in th e machining of hard materials, significantly influencing thequality of machined parts, particularly their surface integrity. It is crucial to evaluate and guar anteemachining conditions that ensure reliable surface integrity that yields co mpressive RS conditions.”
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