首页|Study Findings from Norwegian University of Science and Technology (NTNU) Advance Knowledge in Machine Learning (Transferability of Temperature Evolution of Dissimilar Wire-Arc Additively Manufactured Components by Machine Learning)
Study Findings from Norwegian University of Science and Technology (NTNU) Advance Knowledge in Machine Learning (Transferability of Temperature Evolution of Dissimilar Wire-Arc Additively Manufactured Components by Machine Learning)
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Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Trondheim, Norway, by NewsRx correspondents, research stated, “Wire-arc additive manufacturing (WAAM) is a promising industrial production technique.” Our news journalists obtained a quote from the research from Norwegian University of Science and Technology (NTNU): “Without optimization, inherent temperature gradients can cause powerful residual stresses and microstructural defects. There is therefore a need for data-driven methods allowing realtime process optimization for WAAM. This study focuses on machine learning (ML)-based prediction of temperature history for WAAM-produced aluminum bars with different geometries and process parameters, including bar length, number of deposition layers, and heat source movement speed. Finite element (FE) simulations are used to provide training and prediction data. The ML models are based on a simple multilayer perceptron (MLP) and performed well during baseline training and testing, giving a testing mean absolute percentage error (MAPE) of less than 0.7% with an 80/20 train-test split, with low variation in model performance. When using the trained models to predict results from FE simulations with greater length or number of layers, the MAPE increased to an average of 3.22% or less, with greater variability.”
Norwegian University of Science and Technology (NTNU)TrondheimNorwayEuropeCyborgsEmerging TechnologiesMachine Learning
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