Non-steady state approach to the vacuum nitriding for tools

Abstract

a b s t r a c tA method to the control of vacuum nitriding process that could be applicable for a variety of tool steels is presented. The quantitative diffractometric x-ray phase analysis has been applied to identify the nitrides growth and their stoichiometry as the function of steel composition, nitriding parameters and time. It has been found that the nitrogen content in the 3 nitride depends not only on the nitriding parameters but it is also strongly influenced by alloying elements in steel. This statement has practically excluded using any steady state models to control and monitor the precise nitriding of alloy steels, especially in the case of vacuum nitriding of tool steels. The importance of the nucleation stage for repeatability of vacuum nitriding results has been shown, too. The effective method for active washing of charge is demonstrated and proposed to unify and shorten nucleation time. Finally, the neural network model is presented as a new approach to modelling and control of the non-steady state vacuum nitriding of various tool steels. This model is based on the "boost-diffusion" schedule of the process.

The Effect of the Quenching Method on the Deformations Size of Gear Wheels after Vacuum Carburizing

“…They are currently implemented in specialized furnaces or universal ones, making possible, depending on the type, the cooling of components (including quenching) in oil baths or in a stream of gas under high pressure -usually nitrogen [1][2][3][4][5][6]. Both carburizing and nitriding are currently realized in a controlled manner that allows, using computer-aided design processes, accurate control of the morphology of the surface layer [2,3,7,8]. Nitriding can be applied for various types of steel and cast irons and in case of gears of small module pitch.…”

Neural computing for a low-frictional coatings manufacturing of aircraft engines’ piston rings

“…In addition, both these groups determine the resulting (exploitation) properties of nitrided steels and cast irons. A number of scientists point out that the critical parameters of the nitriding process include temperature, process organization in the boost-diffusion segments and the times of these segments [26,28,42,43]. In addition, Filetin et al [44] pointed out that at the current state of knowledge, the choice of nitriding parameters is largely based on the experimenter's experience.…”

“…The best solution in this case, as the practice and theory indicate, is a computer-aided design based on neural networks. The assumptions to the neural network model and its applications to the low-pressure nitriding of tools have been presented in several former papers [28][29][30][31]. The knowledge analysis in the field shows that the use of neural networks to predict the resulting properties of the technological surface layer of steel and low-pressure-nitrided iron (LPN) for aerospace applications has not been considered so far.…”

Influence of the nitrided layer thickness of dies made of two types of tool steel used in hot extrusion of valve forgings made of nickel–chromium steel on the durability of these tools

“…The research of the authors [24] proves that an important role in this scope is also played by the type of alloying elements present in steel. The conducted investigations have shown that the effectiveness in improving forging tool durability largely depends on the structure and profile and to a lesser extent on the thickness of the obtained nitrided layer [25]. However, obtaining a high thickness of the nitrided layer for forging tools, reaching over 0.3 mm, is difficult with the consideration of effective hardness, as well as uneconomical, as it significantly prolongs the process.…”