Optimization of Nitrided Layer Development: Modeling, Simulation & Mechanism Analysis

2. Development of the nitrided layer – mechanism of the growth, mathematical modeling and simulation. Jerzy Ratajski*, Adam Mazurkiewicz**, Dariusz Lipiński*, Jerzy Dobrodziej** * Koszalin University of Technology, Koszalin, Poland **Institute of Sustainable Technology, Radom, Poland

3. Nitriding process is very efficient: • in long-run production, • in the mass production, • and also is very often used in so called duplex process

4. 0 50 100 mm Duplex processes

5. The nitrided layer should characterizes demanded hardness and thickness, • The nitrided parts should maintain their sizes and shape stability.

7. g’ e a-Fe(N)

8. Temperature - T Temperature - T 530°C e e Concentration - CN Concentration - CN g’ g’ CN a a a a a g’ g’ g’ g’ g’ e e e a a a x Equilibrium diagram Fe-N

9. Real process Model of the process mathematical statistical Data base Process results Process parmeters Baza danych Parameters of design process Assumed resulte process Parametry procesów Rezultaty procesów Zagadnienie polioptymalizacyjne Process model ??? Choice of parameters Result Data base Parameters Results Desired result Knowledge rules Process parameters Process parametrs • Metods of artificial inteligence: • Neural network • Fuzzy logic • Evolutional algorithms

10. CN ( ) D c 2 D c ( ) e e ef = 1 k e 3 å ( ) 0 b k x ij j = 1 j Calculation by iteration methods i = 1, 2, 3 1 – e ; 2 – g’; 3 – a etc

12. e e + g’ 0 g’ 50 100 a-Fe(N) + MNx mm a-Fe(N) Structure of nitrided layer Structure of nitrided layer

13. Temperatura - T Temperature - T Concentration- CN g’ e a e Concentration - CN g’ Stężenie - CN a a

14. 0 50 100 a-Fe(N) + MNx mm Structure of nitrided layer Hardness distribution Hp g600 g500 g400

15. e1 g’ e2 e2 a g’ e1 g’ e2 a 48 50 52 54 56 Steel 4340 (AISI) KN= 3,25 , T = 5800C ,t =10 h

16. 24 e1 22 e2 e1 e2 20 24 18 22 g’ Concentration N+C [% at.] 20 0,8 0,8 18 0,4 0,4 a a 0 0 5 5 10 10 15 15 20 20 25 25 30 30 Distance [mm] t = 3 h t = 10 h Steel 4340 KN=3,25 T=5800C

17. 600 One stage process 500 HV 0,5 Two stage process 400 300 0 0,2 0,4 0,6 0,8 distance (mm) 120 mm g500 175 mm 280 mm g400 400 mm Steel 4340 KN= 10 T=5800C t = 16 h KN= 10/0.5 T=5800C t = 8/8 h

18. Conclusion A research results presented, indicate that, besides temperature and nitrogen potential, also phase composition of iron (carbo)nitrides layer on steel has considerable influence on development of hardness profiles in diffusion layer.

19. Real process Model of the process mathematical statistical Data base Process results Process parmeters Baza danych Parameters of design process Assumed resulte process Parametry procesów Rezultaty procesów Zagadnienie polioptymalizacyjne Process model ??? Choice of parameters Result Data base Parameters Results Desired result Knowledge rules Process parameters Process parametrs • Metods of artificial inteligence: • Neural network • Fuzzy logic • Evolutional algorithms

20. a) b) Neural set Distribution of microhardness in surface layer 4-K-1 T T, t, Np = const V x = var H t ć ś HV o d r Np a w t HV=f(T,t,Np,x) r x i K – nurons number in hiden layer distance x, [mm]

21. Table Characteristics of experimental data used for modeling.

22. The established neural model enables estimation of influence of the chemical composition of steel (grade of steel) and nitriding process parameters including number of stages, on hardness profile.

23. Summary • A research results presented show, that also phase composition of iron (carbo) nitrides layer on steel has considerable influence on development of hardness profile in diffusion layer. • Elaborated neural network model constitute tool to the simulation of the profiles of hardness in the nitrided layer - predicted results showed relatively low scatter with experimental results. • The model is open for constant upgrade and improvement and also can be applied in a control system and in visualization of the process course.

24. Thank you