Institute of Mechatronics, Nanotechnology and Vacuum Technique Koszalin University of Technology

Institute of Mechatronics, Nanotechnology and Vacuum Technique 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

Institute of Mechatronics, Nanotechnology and Vacuum Technique 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

Institute of Mechatronics, Nanotechnology and Vacuum Technique mm Duplex processes

Institute of Mechatronics, Nanotechnology and Vacuum Technique 5 The nitrided layer should characterizes demanded hardness and thickness, The nitrided parts should maintain their sizes and shape stability.

Institute of Mechatronics, Nanotechnology and Vacuum Technique 6

7  -Fe(N)  ’’

Institute of Mechatronics, Nanotechnology and Vacuum Technique 8 CNCN x Equilibrium diagram Fe-N Concentration - C N  ’’  Temperature - T Concentration - C N  ’’  Temperature - T 530°C   ’’  ’’  ’’   ’’   ’’ 

Institute of Mechatronics, Nanotechnology and Vacuum Technique 9 Baza danych Parametry procesówRezultaty procesów Data base Process parmeters Process results Model of the processReal processmathematical statistical Zagadnienie polioptymalizacyjne ??? Parameters of design process Assumed resulte process Result Process model Choice of parameters Desired result Process parameters Results Knowledge rules Parameters Process parametrs Data base Metods of artificial inteligence: Neural network Fuzzy logic Evolutional algorithms

Institute of Mechatronics, Nanotechnology and Vacuum Technique 10 CNCN x i = 1, 2, 3 1 –  ; 2 –  ’ ; 3 –  etc        j jij ef c k cD k    Calculation by iteration methods

Institute of Mechatronics, Nanotechnology and Vacuum Technique 11

Institute of Mechatronics, Nanotechnology and Vacuum Technique mm  +  ’  -Fe(N) + MN x  ’’  -Fe(N) Structure of nitrided layer

Institute of Mechatronics, Nanotechnology and Vacuum Technique 13  ’’  Concentration- C N Stężenie - C N  ’’  Temperatura - T  Concentration - C N Temperature - T

Institute of Mechatronics, Nanotechnology and Vacuum Technique mm HpHp  -Fe(N) + MN x Structure of nitrided layer Hardness distribution g 400 g 500 g 600

Institute of Mechatronics, Nanotechnology and Vacuum Technique 15  ’’  ’’      ’  Steel 4340 (AISI) K N = 3,25, T = C, t =10 h

Institute of Mechatronics, Nanotechnology and Vacuum Technique 16 Steel 4340 K N = 3,25 T = C t = 3 h t = 10 h Concentration N+C [% at.] Distance [  m] ,4 0, ,4 0,8  11 22  11 22 ’’

Institute of Mechatronics, Nanotechnology and Vacuum Technique ,20,40,60,8 HV 0,5 distance (mm) g 50 0 g 40 0 One stage process 280  m 120  m K N = 10/0.5 T = C t = 8/8 h K N = 10 T = C t = 16 h Steel  m 400  m Two stage process

Institute of Mechatronics, Nanotechnology and Vacuum Technique 18 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. Conclusion

Institute of Mechatronics, Nanotechnology and Vacuum Technique 19 Baza danych Parametry procesówRezultaty procesów Data base Process parmeters Process results Model of the processReal processmathematical statistical Zagadnienie polioptymalizacyjne ??? Parameters of design process Assumed resulte process Result Process model Choice of parameters Desired result Process parameters Results Knowledge rules Parameters Process parametrs Data base Metods of artificial inteligence: Neural network Fuzzy logic Evolutional algorithms

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

Institute of Mechatronics, Nanotechnology and Vacuum Technique 21 Table Characteristics of experimental data used for modeling.

Institute of Mechatronics, Nanotechnology and Vacuum Technique 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.

Institute of Mechatronics, Nanotechnology and Vacuum Technique 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.

Institute of Mechatronics, Nanotechnology and Vacuum Technique 24 Thank you