1. Modelling Approaches for Optimization of Heavy Plate Steels for Pressure Vessel Application
, Prof. Dr.-Ing. Andreas Kern; Dipl.-Kffr. Esther Pfeiffer ; Dipl.-Ing. Klaus Janhofer; Peter Walter
thyssenkrupp Steel Europe; Heavy Plate Unit

2. Production and application of heavy plate
3rd Steel Plate Conference-Düsseldorf

3. Production of high-strength steels for pressure vessels
Process
normalizing,
normalizing rolling N
temperature
time A c 3
normalizing
thermomechanical
rolling TM
accelerated
cooling AC A r 3
temperature
time
(direct) quenching
and tempering
(D)QT A c 3
temperature
time
hardening 1
tempering
Treatment
Microstructure
Yield strength,
MPa
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4. Benefits and application of modeling
Fields of application
Pre-calculation of mechanical properties
Identification of significant influencing variables
Fast optimization of chemical composition and production conditions
Shorter test-phases for new steels
Assessment of performance and risk
Case studies
Technical processing of customer inquiries
Approval of heats
Planning of plant trials
Homogenization of analysis concepts
Product optimization
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5. Concept of pre-calculation model
Simulation
Knowledge
Quantitative description of metallurgical processes
via physically based equations etc.
Metall. mechanisms
i.e. Grain coarsening
Recrystallization
Transformation
Mathematical model
Process step
Heating
Rolling
Cooling
Four-high mill
Chemical composition,
production conditions
Properties
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6. Different usage of a simulation model – Top-Down or Bottom-Up
Start
Result
Chemical composition
Production conditions
Top-Down principle
Bottom-Up
priciple
Simulation
(Mathematical model)
Mechanical properties
Result
Start
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7. Verification of modeling – Comparison of measured and calculated values
95% Confidence interval
Tensile strength measured, MPa
Yield strength measured, MPa
Yield strength calculated, MPa
Tensile strength calculated, MPa
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8. Application of modeling: Optimization of alloying concepts for normalized pressure vessel steels
Target:
Reduction of cost-intensive alloying components Cu and Ni
Keeping mechanical properties and carbon equivalent constant
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9. Bottom-up-application using Monte-Carlo-algorithm
Random selection of the chemical composition and the production conditions
Solution area schematic
Chemical analysis
Objective criteria
Production conditions
Calculating of the objective criteria (Re, Rm …)
n = n +1
Restrictions are met? no
yes
Save the result (solution)
Statistical analysis of the solutions (n=N)
n≤N
N: Number of attempts
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10. Bottom-up-application: Results of an optimization calculation after statistical analysis (schematic)
min .
max
Carbon, wt %
Manganese, wt %
Niobium, wt %
Chromium, wt % x̃
Cumulation rate
x̃ : characteristic results of the bottom-up-application
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11. Results of modeling: New concepts for pressure vessel steels (exemplarily)
Modeling of material properties
P265 – P355/ASTM A 516 Gr
Chemical composition, max [%]
old
new *
Carbon [%]
Silicon [%]
Manganese [%]
Chromium [%]
Copper [%]
Nickel [%]
0,20
0,30
1,20
0,05
0,25
0,16
0,30
1,10
0,20
Targetted properties
Properties
Production parameters
Chemical composition
* Occasionally microalloying with Nb
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12. Estimation of property profile of newly developed pressure vessel steels
Grade A 516 Gr Attempts: 5000
Re min
Rm min
Cumulation rate
Cumulation rate
Cumulation rate
Cumulation rate
Cumulation rate
Cumulation rate
Cumulation rate
Cumulation rate
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13. Comparison of mechanical properties of different alloying concepts (steel grade: A516 Gr.60)
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14. Thank you for your attention!