首页|New Machine Learning Research Reported from University of South China (Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Ad ditive Manufacturing Based on Machine Learning)
New Machine Learning Research Reported from University of South China (Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Ad ditive Manufacturing Based on Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting from Hengyang, People’s Repub lic of China, by NewsRx journalists, research stated, “Wire arc additive manufac turing (WAAM) has attracted increasing interest in industry and academia due to its capability to produce large and complex metallic components at a high deposi tion rate.” Financial supporters for this research include National Natural Science Foundati on of China; Natural Science Foundation of Hunan Province; Education Department of Hunan Province. The news reporters obtained a quote from the research from University of South C hina: “One of the basic tasks in WAAM is to determine appropriate process parame ters, which will directly affect the morphology and quality of the weld bead. Ho wever, the selection of process parameters relies heavily on empirical data from trial-and-error experiments, which results in significant time and cost expendi tures. This paper employed different machine learning models, including SVR, BPN N, and XGBoost, to predict process parameters for WAAM. Furthermore, the SVR mod el was optimized by the Genetic Algorithm (GA) and Particle Swarm Optimization ( PSO) algorithms. A 3D laser scanner was employed to obtain the weld bead’s point cloud, and the weld bead size was extracted using the point cloud processing al gorithm as the training data. The K-fold cross-validation strategy was applied t o train and validate machine learning models.”
University of South ChinaHengyangPeo ple’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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