temp2.png

Phase transformation time

The aggregated average error for simulation of the time on which transformation of the phases Ferrite, Pearlite, Bainite and Martensite are starting and finishing respectively. The benchmark is based on empirically measured TTT diagrams for 14 steel grades (chemical compositions listed below) that are not included in the machine learning database and where benchmarking TTT data has been extracted for randomly selected temperatures. In the table, _s and _f refer to the start and finish time of the transformation of a phase, e.g. Fe_s being Ferrite start time. TTT M_s model is temporary and will be improved in V 2.0 when consuming the main ferritico Ms model.  

Ferritico error

Fe_s

Fe_f

P_s

P_f

B_s

B_f

M_s

3.57 °C

3.06 °C

3.06 °C

4.69 °C

3.55 °C

4.10 °C

17.37 °C

Competitor error

9.14 °C

3.26 °C

3.26 °C

8.22 °C

2.26 °C

3.90 °C

13.52 °C

STEEL GRADES INCLUDED IN THE BENCHMARKING

912,7 °C

Auestenitization temp

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,06

0,43

0

0

0

0

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0

0

0

0

0


910,0 °C
Auestenitization temp

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,54

0,46

0

0

0

0

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0

0

0

0

0


910,0 °C
Auestenitization temp

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,64

1,13

0

0

0

0

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0

0

0

0

0


871,0 °C
Auestenitization temp

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,40

0,57

0

0

3,49

0,01

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0

0

0

0

0


927,0 °C
Auestenitization temp

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,60

0,52

0

0

5,00

0

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0

0

0

0

0


927,0 °C
Auestenitization temp

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,21

0,78

0

0,99

1,09

0

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0

0

0

0

0


927,0 °C
Auestenitization temp

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,22

0,77

0

1,91

1,08

0

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0

0

0

0

0


843,0 °C
Auestenitization temp

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,33

0,53

0

0,90

0

0,18

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0

0

0

0

0


899,0 °C
Auestenitization temp

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,11

0,45

0

1,52

3,22

0

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0

0

0

0

0


1.038,0 °C
Auestenitization temp

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,33

0,41

0

0

0

1,96

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0

0

0

0

0


857,0 °C
Auestenitization temp

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,35

0,80

0

0

0

0,25

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0

0

0

0

0


871,0 °C
Auestenitization temp

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,40

0,42

0

0

0

0,53

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0

0

0

0

0


815,0 °C
Auestenitization temp

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,48

0,94

0

0

0

0,25

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0

0

0

0

0


899,0 °C
Auestenitization temp

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,68

0,87

0

0

0

0,24

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0

0

0

0

0


899,0 °C
Auestenitization temp

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,11

0,38

0,44

5,46

0

0,42

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0

0

0

0

0

TTT SIMULATION

Machine learning based prediction of isothermal transformation in low-alloy steels.

We are happy to provide the market with improved TTT simulation capabilities and to support additional TTT data use cases as the simulation accuracy has significantly improved compared to conventional tools. 

The Ferritico TTT module is consumed as a SaaS where the TTT data is presented in a TTT diagram  or in a aggregated data file.

Input:

  • Alloy composition

  • Austenitization temperature

Output:

  • What phases are formed for each temperature and their corresponding  on- and offset time

  • Time for phase fractions (10%,  50%, 90%, 100%)

  • Hardness (HV) for each transformation temperature

  • AC1 and AC3

ttt_new2.png

BENCHMARKING REPORT

The Ferritico TTT module simulation accuracy has been benchmarked through comparisons to empirical TTT measurements based on either Jominy or Dilatometer. The benchmark includes comparisons on phase formations and on- and offset time . The benchmarking report below also compares the Ferritico simulation accuracy with market leading simulation software built on physical models.    

transformation5.png

Phase formation

The aggregated average percentage error for simulation of whether the phases Ferrite, Pearlite, Bainite and Martensite are formed. The benchmark is based on empirically measured TTT diagrams for 14 steel grades (chemical compositions listed below) that are not included in the machine learning database and where benchmarking TTT data is extracted for randomly selected temperatures .   

Ferritico %-error

Ferrite start

Pearlite start

Bainite start

Martensite start

5.07 %

4.91 %

7.60 %

28.85 %

Competitor %-error

23.04 %

31.13 %

4.17 %

0.00 %

Ferrite end

Pearlite end

Bainite end

Martensite end

4.91 %

3.37 %

12.75 %

NA

31.13 %

27.73 %

12.43 %

NA