Ning-Yu Kao
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View article: Convolutional neural networks for melt depth prediction and visualization in laser powder bed fusion
Convolutional neural networks for melt depth prediction and visualization in laser powder bed fusion Open
Powder bed fusion is a method of additive manufacturing (AM) where parts are constructed by iteratively melting metal cross-sections to build complex 3D structures. Defects often form during the printing process, where the dynamics of the …
View article: MechProNet: Machine Learning Prediction of Mechanical Properties in Metal Additive Manufacturing
MechProNet: Machine Learning Prediction of Mechanical Properties in Metal Additive Manufacturing Open
Predicting mechanical properties in metal additive manufacturing (MAM) is essential for ensuring the performance and reliability of printed parts, as well as their suitability for specific applications. However, conducting experiments to e…
View article: MeltpoolNet: Melt pool characteristic prediction in Metal Additive Manufacturing using machine learning
MeltpoolNet: Melt pool characteristic prediction in Metal Additive Manufacturing using machine learning Open
Characterizing melt pool shape and geometry is essential in Metal Additive Manufacturing (MAM) to control the printing process, and avoid defects. Predicting melt pool flaws based on process parameters and powder material is difficult due …
View article: MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning
MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning Open
Characterizing meltpool shape and geometry is essential in metal Additive Manufacturing (MAM) to control the printing process and avoid defects. Predicting meltpool flaws based on process parameters and powder material is difficult due to …