VIII Conferência Brasileira sobre Temas de Tratamento Térmico
21 a 24 de maio de 2017 – Hotel Quatro Estações – Indaiatuba – SP

Optimization of hot work tool steel heat treatment using computational methods

Bojan Podgornik

Institute of Metals and Technology

Abstract
Tool material properties including hardness and fracture toughness depend on the microstructure, mainly defined by heat treatment conditions. Traditionally trade-off between high fracture toughness and sufficient hardness and wear resistance is required. However, in the case of vacuum heat treatment proper combination of austenitizing and tempering time and temperature allows optimization of microstructure, resulting in improved fracture toughness while maintaining high hardness. Response of tool steel in terms of fracture toughness and hardness on vacuum heat treatment conditions depends on tool steel type and processing route, but mainly on the steel composition and alloying elements involved in the precipitation of secondary carbides. However, with the modification of the composition and alloying elements content effect of heat treatment conditions on the tool steel properties will change, requiring tremendous experimental work and design of tempering diagrams. In the lecture potential and possibilities of computational, especially neural network based modelling, regarding selection and optimization of vacuum heat treatment conditions depending on the hot work tool steel composition and required properties will be presented. Process of building and training of the model is described on the basis of experimentally obtained tempering diagrams for several different hot work tool steel compositions. Due to the radial symmetry and possibility to simultaneously measure different properties (fracture toughness, hardness, compression and bending strength, etc.) circumferentially notched and fatigue pre-cracked tensile bar specimens (CNPTB) is the most suitable to prepare tempering diagrams. Finally, by comparing the measured values to the model prediction and adding new experimental data, validity of the models can be evaluated and model corrected and improved.


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