20.500.12556/RUL-158311 Predicting the deep drawing process of TRIP steel grades using multilayer perceptron artificial neural networks TRIP (Transformation Induced Plasticity) steels belong to the group of advanced high-strength steels. Their main advantage is their excellent strength combined with high ductility, which makes them ideal for deep drawing processes. The forming of TRIP steels in the deep drawing process enables the production of a thin-walled final product with superior mechanical properties. For this reason, this study presents comprehensive research into the deep drawing of cylindrical cups made from TRIP steel. The research focuses on three main aspects of the deep drawing process, namely the sheet metal thinning, the maximum force value and the ear height as a result of the anisotropic material behaviour. Artificial neural networks (ANNs) were built to predict all the mentioned output parameters of the part or the process itself. The ANNs were trained using data obtained from a sufficient number of simulations based on the finite element method (FEM). The ANN models were developed based on variable material properties, including anisotropic parameters, blank holding force, blank diameter, and friction coefficient. A good agreement between simulation, ANN and experimental results is evident. forming deep drawing TRIP steel artificial neural network finite element methods modelling simulation ANN FEM preoblikovanje globoko vlečenje TRIP jeklo umetna nevronska mreža metoda končnih elementov modeliranje simulacije true false true Angleški jezik Slovenski jezik Članek v reviji 2024-06-04 13:06:10 2024-06-04 13:06:14 2025-01-29 10:53:59 0000-00-00 00:00:00 2024 0 0 Str. 46–64 no. 1 Vol. 19 Mar. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 621.7:669 1854-6250 10.14743/apem2024.1.492 197726467 Sevsek...-Predicting_the_deep_drawing_process_of_TRIP_steel_grades_using_multilayer_perceptron_artificial_neural_networks.pdf Sevsek...-Predicting_the_deep_drawing_process_of_TRIP_steel_grades_using_multilayer_perceptron_artificial_neural_networks.pdf 1 0BC36AC1C4614B1EDB2DB9DE27DE1B0C 04b2ec3d305bb37950cbff09c66cd95b54edddf3f262ac6d053ab6b94a89777d d616aa32-2262-11ef-90a3-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=185867 https://apem-journal.org/Archives/2024/Abstract-APEM19-1_046-064.html 1 76b97117-2262-11ef-90a3-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=185866 Fakulteta za strojništvo 0 0 0