首页|New Findings from University of Ghent Update Understanding of Nanocrystals (Prediction of Residual Stress Distribution Induced By Ultrasonic Nanocrystalline Surface Modification Using Machine Learning)
New Findings from University of Ghent Update Understanding of Nanocrystals (Prediction of Residual Stress Distribution Induced By Ultrasonic Nanocrystalline Surface Modification Using Machine Learning)
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NETL
NSTL
Elsevier
Current study results on Nanotechnology Nanocrystals have been published. According to news originating from Ghent, Belgium, by NewsRx correspondents, research stated, “Ultrasonic Nanocrystalline Surface Modification (UNSM) offers an efficient and cost-effective approach for enhancing material mechanical properties by inducing Severe Plastic Deformation (SPD). It leads to grain refinement and substantial residual stress generation beneath the workpiece surface.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), China Scholarship Council, Ministry of Trade, Industry & Energy (MOTIE), Republic of Korea, Korea Institute for Advancement of Technology (KIAT) through the International Cooperative RD Program. Our news journalists obtained a quote from the research from the University of Ghent, “This study investigates the influence of key modification parameters, specifically static load, vibration amplitude, and strike tip size on compressive residual stress (CRS) distribution. A Finite Element Method (FEM)-based model for the UNSM process is developed, and validated against experimental outcomes, yielding a dataset of 45 unique cases across various modification scenarios. The Balancing Composite Motion Optimization (BCMO), as a meta-heuristic algorithm is used to optimize the hyperparameters of the Support Vector Regression (SVR) model. Additionally, the performance of Artificial Neural Network (ANN), Polynomial Chaotic Extension (PCE), and Kriging algorithms is evaluated in parallel. Among these Machine Learning (ML) models, the SVR-BCMO emerges as a pioneer for its accuracy in estimating residual stress. A sensitivity analysis employing Sobol’ indices further clarifies the distinct impact of each input parameter on residual stress distribution resulting from UNSM. In essence, this research offers a tool for rapidly estimating residual stress, even in cases of limited datasets. Furthermore, the findings help in making prompt decisions regarding of UNSM conditions.”
GhentBelgiumEuropeCyborgsEmerging TechnologiesMachine LearningNanocrystalsNanotechnologyUniversity of Ghent
NETL
NSTL
Elsevier
