Design, thermomechanical processing and induction hardening of a new medium-carbon steel microalloyed with niobium
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
Linnanmaa, sali L10
Topic of the dissertation
Design, thermomechanical processing and induction hardening of a new medium-carbon steel microalloyed with niobium
Doctoral candidate
Master of Science Vahid Javaheri
Faculty and unit
University of Oulu Graduate School, Faculty of Technology, Materials and Mechanical Engineering Research Unit
Subject of study
Materials Engineering
Opponent
Professor Hans-Olof Andren, Chalmers University of Technology
Second opponent
Professor Pasi Peura, University of Tampere
Custos
Professor David Porter, University of Oulu
Development of a new slurry pipeline steel with induction hardening
The main aim of this thesis was to develop a steel composition and processing route suitable for making a slurry transportation pipeline with the aid of induction hardening, and to characterize the phase transformations and microstructures involved in the various stages of the processing route.
A novel steel chemistry was designed based on metallurgical principles assisted by computational thermodynamics and kinetics. The designed composition is a medium-carbon, low-alloy steel microalloyed with niobium, in wt.% 0.40 C, 0.20 Si, 0.25 Mn, 0.50 Mo, 0.90 Cr, and 0.012 Nb.
This was subsequently cast, thermomechanically rolled on a laboratory rolling mill to two bainitic microstructures, and finally subjected to the thermal cycles predicted to be encountered with the internal induction hardening of a typical pipe geometry.
The phase transformations and microstructures found at various stages of the simulated production process have been characterized and algorithms developed to enable the optimization of microstructure and hardness through the pipe wall thickness.
The thesis has been made within the European Industrial Doctorate (EID) project called Mathematics and Materials Science for Steel Production and Manufacturing, abbreviated as MIMESIS, which has five partners: EFD Induction in Norway; SSAB, Outokumpu, and the University of Oulu in Finland; and Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Germany.
This thesis has been made within the European Industrial Doctorate (EID) project called Mathematics and Materials Science for Steel Production and Manufacturing, abbreviated as MIMESIS, which has five partners: EFD Induction in Norway; SSAB, Outokumpu, and the University of Oulu in Finland; and Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Germany.
A novel steel chemistry was designed based on metallurgical principles assisted by computational thermodynamics and kinetics. The designed composition is a medium-carbon, low-alloy steel microalloyed with niobium, in wt.% 0.40 C, 0.20 Si, 0.25 Mn, 0.50 Mo, 0.90 Cr, and 0.012 Nb.
This was subsequently cast, thermomechanically rolled on a laboratory rolling mill to two bainitic microstructures, and finally subjected to the thermal cycles predicted to be encountered with the internal induction hardening of a typical pipe geometry.
The phase transformations and microstructures found at various stages of the simulated production process have been characterized and algorithms developed to enable the optimization of microstructure and hardness through the pipe wall thickness.
The thesis has been made within the European Industrial Doctorate (EID) project called Mathematics and Materials Science for Steel Production and Manufacturing, abbreviated as MIMESIS, which has five partners: EFD Induction in Norway; SSAB, Outokumpu, and the University of Oulu in Finland; and Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Germany.
This thesis has been made within the European Industrial Doctorate (EID) project called Mathematics and Materials Science for Steel Production and Manufacturing, abbreviated as MIMESIS, which has five partners: EFD Induction in Norway; SSAB, Outokumpu, and the University of Oulu in Finland; and Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Germany.
Last updated: 1.3.2023