DLPP DANIELI LONG PRODUCTS PROPERTIES PREDICTOR. ITA Ltd. Ostrava, Czech republic. Danieli Morgardshammar Buttrio, Italy

Transcription

1 DLPP DANIELI LONG PRODUCTS PROPERTIES PREDICTOR Danieli Morgardshammar Buttrio, Italy ITA Ltd. Ostrava, Czech republic

2 DLPP - Danieli Long Products Properties Predictor Introduction Commissioning of new steel grades and new technologies using only process and laboratory testing is not only time-consuming but also expensive! A very useful tool in optimizing this kind of processes is computer simulation! D L P P Off-line tool for prediction of microstructure and final mechanical properties of hot rolled bars and wire rods.

3 DLPP software Chart of main physically based executive modules Input Data Technology&Process Steel Properties METARoll Module grain evolution recrystallization residual strain precipitation Temperature Module THERM 1D FEM Time Temperature curve for a single bar Temperature Module COIL 2D FEM Time Temperature curve for a bar in coil The last Pass Data residual strain of austenite austenite grain size METACool Module CCT Diagram austenite decomposition mechanical properties Output Data microstructure in every Pass structure shares mechanical properties

4 DLPP software Steel groups and limits of chemical composition C Mn Si Cr Ni Mo V W Ti Nb B Al N GROUP min max min max min max min max min max min max max max max max max max max C < C < C < C < Mn Cr C < Cr C > Mo Cr-Mo Cr-Si Ni-Cr-Mo

5 DLPP software Theoretical Background Finite Element Temperature Models Therm 1D -> nonstationary heat transfer FE analysis for 1D axisymmetric bodies (linear 2-node ring) Coil 2D -> nonstationary heat transfer FE analysis for 2D plane axisymmetric bodies (linear 4-node quadrangle) Model of thermal properties -> thermal conductivity, density and specific heat depend on the coil temperature and its tightening Heat transfer -> time and temperature dependent heat transfer coefficient and ambient temperature

6 DLPP software Theoretical Background Finite Element Temperature Calculations Temperature [ C] Induction reheating Time - Temperature curves - Head 39mm, 3.00 m/s, 160x160 mm / 12 m slow cooling on cooling bed or in coil surface temperature core temperature 5.00 mm below surface STAND#1 ENTRY C STAND#4 EXIT C STAND#5 ENTRY C STAND#13 ENTRY C STAND#18 EXIT C SIZING ENTRY C SIZING EXIT C CB/GARRETT ENTRY C heat transfer into rolls accelerated water cooling heat generation due plastic deformation Time [s]

7 DLPP software Theoretical Background MetaROLL Module Metallurgy during rolling Chemical composition Activation energy of recrystalization Zenner-Hollomon parameter Process parameters in every pass: temperatures strains strain rates! pure physical approach! Grain size [um] Austenitic grain after every pass - Head dasd, m/s, 150x125 mm / 14 m average grain size grain after growth grain after recrystallization Temperature [ C] average rolling temperature average interstand temperature Increasing of the activation energy in case of precipitation Recrystallized part Xrex for given temperature, effective strain and time available between neighbouring passes Residual strain FIrest = FIef *(1 - Xrex) Size of recrystallized grain of austenite /1 1/2 1/3 1/4 1/5 2/1 2/2 2/3 2/4 2/5 2/6 3/1 3/2 4/1 4/2 4/3 4/4 Mill / Pass Growth of recrystallized grain between particular passes

8 from literarture [s] DLPP software MetaCOOL Module Metallurgy during cooling - Step 1 CCT Diagram Prediction Temperature [ C] 1000,0 900,0 800,0 700,0 Temperature [ C] 1000,0 900,0 GRAIN, FIREST 600,0 800,0 Structure Shares Calculation Mechanical Properties Calculation Sx Sox A S exp((-b S C S 500,0 400,0 300,0 200,0 700,0 600,0 500,0 400,0 100, ,010 0,10 1,00 10,00 100,0 1000, ,0 200,0 100, ,001 1,00 10,00 100,0 1000, Linear multiparametric regression S(i) = exp (Bo + (B(i). C(i)) T(i) = Ao DS + (A(i). C(i)) CEQ) FIR) DI exp(e MIKRO) FIR ,1 0,01 S Time [s] Carbon&alloyed Steels Time Coordinates of Noses 0,001 0,01 0, computed [s] 1/3 )) exp( ferrite strat pearlite start perlite finish bainite start bainite finish Time [s] F S B)

9 DLPP software Theoretical Background MetaCOOL Module Metallurgy during cooling - Step 2 CCT Diagram Prediction Structure Shares Calculation X (t, i Avrami equation for pearlite and bainite transformation T) (1 exp( k n t )) X Mechanical Properties Calculation Koistinen-Marburger equation for martensite transformation Xm(T) (1 exp( b (T Ms T) n )) X

10 HV hardnessfrom experiments experimental values of Vicker's hardness from anisothermal TTT diagrams DLPP software Theoretical Background MetaCOOL Module Metallurgy during cooling - Step 3 CCT Diagram Prediction Linear regression HV = C O + %Fe * (C1 i * c i ) + %Pe * (C2 i * c i ) + %Ba * C3 i * c i ) + %Ma * (C4 i * c i ) Vicker's hardness of tool steels for complete interval of chemical compositions and for different cooling rates Structure Shares Calculation Mechanical Properties Calculation Vicker's hardness calculated by TTSteel tempering 100 C tempering 200 C tempering 300 C tempering 400 C tempering 500 C HV hardness from regression analysis

11 DLPP Technique of process simulation New Rolling process -> Import of Layout

12 DLPP Technique of process simulation Slab specification

13 DLPP Technique of process simulation Steel specification

14 DLPP Technique of process simulation Furnace conditions

15 DLPP Technique of process simulation Rolling Technology

16 DLPP Technique of process simulation Water Box Cooling Technology

17 DLPP Technique of process simulation Garret Cooling Technology

18 DLPP Technique of process simulation Temperature calculations

19 DLPP Technique of process simulation Metallurgical calculations

20 Výpočet [MPa] Výpočet [MPa] DLPP - Examples Verification for WIRE ROD rolling ( 64 pcs) various diameters C Mn Si Cr Ni Ti B Měřeni [MPa] Yield Stress Měřeni [MPa] Ultimate Tensile Strength

21 DLPP - Examples Verification for BAR rolling C Mn Si Ti Teplota [ C] Finální mechanické vlastnosti - střední Ferit Perlit Bainit Martenzit průměrná teplota povrchová teplota Finální 300.0vlastnosti Teploty [ C] Ac3 = 890 Ac1 = Ar3 = 782 Ar1 = 647 Strukturní podíly [%] Ferit = Perlit = 14.3 Bainit = 0.0 Martenzit = 0.0 Austenit = 0.0 Mechanické v lastnosti 0.00 HV = 166 HB = Re Rm = 310 MPa = 494 MPa Diameter 40 mm Re (MPa) Rm (MPa) % of Ferrite Čas [s] % of Pearlite measured calculated

22 DLPP - Examples QTB Technology - verification Bar for reinforcement Diameter: 32 mm Exit rolling speed: 7.2 m/s Exit temperature: 995 C Cooling equipment: Water tubes + Cooling bed Chemical composition: C 0.2, Mn 0.9, Si 0.4, Ti 0.03

23 DLPP - Examples QTB Technology austenite decomposition Temperature [ C] 1100,0 1000,0 900,0 Ferrite Pearlite Bainite Martensite 800,0 700,0 600,0 500,0 400,0 300,0 200,0 100,0 0,00 0,10 1,00 10,00 100,0 1000,0 Time [s]

24 DLPP - Examples QTB Technology HV Hardness HV calculated HV measured Tensile strength (measured / calculated): 628 / 626 MPa Yield stress (measured / calculated): 529 / 484 MPa Depth below surface [mm]

25 DLPP - Danieli Long Products Properties Predictor Summary the DLPP has been developed for off-line computer simulation of metallurgical processes in hot rolled bars or wire rods during rolling and after subsequent cooling! based on specified steel chemistry and rolling technology the DLPP predicts microstructure parameters of deformed austenite after rolling, especially grain size, recrystallized fraction and retained strain! based on specified steel chemistry, microstructure of deformed austenite and cooling strategy the DLPP predicts secondary structure shares and corresponding mechanical properties of final product! the DLPP can be delivered including training and tuning for specific conditions of your plant!

26 thank for your attention!