STEEL GRADES INCLUDED IN THE BENCHMARKING

 

214Cr-1Mo

(Energy)

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,069

0,86

0,24

1,46

0

1,29

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0,02

0

0

0

0

 

Carbide free bainitic steel

(Rails)

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,18

0,36

1,2

1,4

0

0,49

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0,027

0,011

0

0

0

 

Carbide free bainitic steel

(Rails)

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,23

1,52

1,48

1,2

0,85

0,35

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0,024

0,01

0

0

0

 

22MnB5

(Machinery)

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,25

1,5

0,4

0,3

0

0

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0,05

0,01

0,005

0,024

0,01

0

0

0

32CrB4

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,34

0,9

0,3

1,2

0

0

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0,025

0,025

0

0

0

DP 1000

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,15

1,5

0,5

0

0

0

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0

0

0

0

0,015

0,002

0

0

0

Low C high strength

C

Mn

Si

Cr

Ni

Mo

V

Co

Al

W

0,15

1,58

0,55

0,02

0,01

0

0

0

0

0

Cu

Nb

Ti

N

B

P

S

Sn

Zr

As

0

0,033

0

0

0

0,03

0,007

0

0

0

CCT SIMULATION

Machine learning based prediction of continuous cooling transformations in low-alloy steels.

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

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

Input:

  • Alloy composition

  • Austenitization temperature and time or grain size

  • Cooling rates

Output:

  • What phases are formed and their corresponding  on- and offset temperatures

  • Phase fraction at room temperature

Display2 (1).png

BENCHMARKING REPORT

The Ferritico CCT module simulation accuracy has been benchmarked through comparisons to empirical CCT measurements based on either Jominy or Dilatometer. The benchmark includes comparisons on phase formations and on- and offset temperatures but not phase fractions since the benchmarked diagrams did not include the fraction information. 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 CCT diagrams for 14 steel grades (some chemical compositions listed below) that are not included in the machine learning database and where benchmarking CCT data is available for 8-12  cooling rates.   

Ferritico %-error

Ferrite

Pearlite

Bainite

Martensite

8.1 %

18.9 %

23.4 %

22.5 %

Competitor %-error

20.7 %

27.0 %

27.9 %

27.0 %

temp2.png

Phase transformation temperatures

The aggregated average error for simulation of the temperatures on which transformation of the phases Ferrite, Pearlite, Bainite and Martensite are starting and finishing respectively. The benchmark is based on empirically measured CCT diagrams for 14 steel grades (some chemical compositions listed below) that are not included in the machine learning database and where benchmarking CCT data is available for 8-12  cooling rates. In the table, _s and _f refer to the start and finish temperature of the transformation of a phase, e.g. Fe_s being Ferrite start temperature.   

Ferritico error

Fe_s

Fe_f

P_s

P_f

B_s

B_f

M_s

34.9 °C

45.7 °C

28.4 °C

55.2 °C

53.6 °C

40.1 °C

39.6 °C

Competitor error

49.0 °C

77.5 °C

68.0 °C

117.1 °C

74.3 °C

93.4 °C

47.8 °C