.... , and Kirshnamurty , S. , 2020 , “ 3D Build Melt Pool Predictive Modeling for Powder Bed Fusion Additive Manufacturing ,” International Design Engineering Technical Conferences and Computers and Information in Engineering Conference , Virtual, Online, Aug. 17–19, American Society of Mechanical Engineers , Vol...

... methodology Schematic illustration of the developed methodology As discussed in Sec. 1 , it is challenging to extract features directly from point clouds for predictive modeling due to the data complexity. One way to tackle the challenge is to represent point clouds as 2D images. Many existing...

... (FDM). The predictive model of flexural strength is trained using machine learning and validated on experimental data. The relationship between three structural design factors, including the number of fiber layers, the number of fiber rings as well as polymer infill patterns, and the flexural strength...

... larger components more efficiently, the mechanical properties of the components fabricated by cooperative 3D printing have not been systematically characterized. This paper aims to develop a data-driven predictive model that predicts the tensile strength of the components fabricated by cooperative 3D...

... regularization to also handle systems with known but uncertain initial conditions. deep neural networks regularization predictive modeling data-driven prediction design optimization metamodels computational foundations for engineering optimization machine learning for engineering applications...