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Description DLNN unsupervised greedy layer‐wise pretraining approach.
Article Title: Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
Publication Title: Advanced Engineering Materials -
Description DNN supervised training.
Article Title: Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
Publication Title: Advanced Engineering Materials -
Description a) Compression testing setup, b) one of the compression stress–strain diagram for the Ti64 cellular structures, and c) fractures sample.
Article Title: Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
Publication Title: Advanced Engineering Materials -
Description The matrix of the investigated parameters and measured porosity and hardness
Article Title: Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
Publication Title: Advanced Engineering Materials -
Description SNN supervised training.
Article Title: Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
Publication Title: Advanced Engineering Materials -
Description a) Nodally connected diamond structure used in this study and b) their design parameters.
Article Title: Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
Publication Title: Advanced Engineering Materials -
Description Design parameters and their corresponding levels
Article Title: Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
Publication Title: Advanced Engineering Materials -
Description a,b) Predicted contours distribution of the ultimate compression strength and elastic modulus with respect to the lattice struts diameter and length; c,d) Stress–strain diagrams of samples X1–X4.
Article Title: Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
Publication Title: Advanced Engineering Materials -
Description Young's modulus compared with the output of the DLNN/DNN/DoE/SNN models.
Article Title: Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
Publication Title: Advanced Engineering Materials -
Description Predicted contours distribution of the specific strength with respect to the lattice struts diameter, a) length, and b) angle.
Article Title: Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
Publication Title: Advanced Engineering Materials -
Description a,b) Predicted contours distribution of the ultimate compression strength and elastic modulus with respect to the strut angle; c) Stress–strain diagrams of samples X5–X6.
Article Title: Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
Publication Title: Advanced Engineering Materials -
Description Maximum strength compared with the output of the DoE/DLNN/DNN/SNN models.
Article Title: Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
Publication Title: Advanced Engineering Materials -
Description MPE comparison for tested approaches.
Article Title: Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
Publication Title: Advanced Engineering Materials -
Description Specific strength compared with the output of the DLNN/DNN/DoE/SNN models.
Article Title: Controlling the Properties of Additively Manufactured Cellular Structures Using Machine Learning Approaches
Publication Title: Advanced Engineering Materials