Nr 2/2014 ___________________ IN Ż YNIERIA MATERIA Ł O W A ________________________ 147 BARTŁOMIEJ JANUSZEWICZ, PIOTR KULA, EMILIA WOŁOWIEC, ROBERT PIETRASIK, ADAM RZEPKOWSKI Boost-diffusion vacuum nitriding of X37CrMoV51 steel INTRODUCTION Tools for machining and dies for forging, forming and die casting require several subsequent operations of advanced vacuum heat treatment and surface engineering, namely, quenching, tempering, nitriding and PVD coating. The ability to carry out some of these operations in the same device would make the manufacturing much easier and cheaper. There have been several reports about attempts at coupling the ion nitriding and PVD plating in the same vacuum chamber [1÷4]. Modern multipurpose vacuum furnaces are able to integrate austenizing, high pressure gas quenching, single or multiple tempering and also low pressure nitriding [5÷8]. However, a reliable low pressure nitriding requires the development of models and technology for a variety of alloying tool steels due to high microstructure demands from nitrided cases on tools. The “boost-diffusion” process has been proposed to control and monitor low pressure nitriding of tools in multipurpose vacuum furnaces. The basic assumptions for this model are: 1. all “boost” stages are carried out at the constant total pressure of 26 hPa, at the ammonia supplying flow that is proportional to the total area of nitrided charge. The level of pressure is in conformity with the industrial safety requirement. This assumption should guarantee the constant and repeatable nitrogen content in optional ε phase on treated steel grades during the “boost” stages rich in nitrogen; 2. all “diffusion” stages are carried out in vacuum to separate the nitrogen reserve in nitrides from any external interactions. It enables the reliable modelling of nitrogen diffusive transfer based only on disproportionation and decomposition of nitrides’ zone. An exemplary schedule of “boost-diffusion” low pressure nitriding is shown in the Figure 1. The model of low-pressure nitriding was based totally on the neural network method; moreover:  evaluation of the steel types and the specific nature of the nitriding process was made by applying neural networks to model the classification problems,  diffusion kinetics in multiphase systems and the relationships of phase formation: diffusion, γ' and ε phases, were reproduced with neural network for modelling regression problems. To optimize applied procedures and to find out dependences between process parameters and created layers properties it was necessary to carry out some experiments, and obtained data were utilized to teach neural networks and introduce corrections. EXPERIMENTAL STUDY To investigate the parameters of boost-diffusion process on the properties of nitrided samples, a series of experiments was done. The samples were prepared in form of discs of 25 mm in diameter and 6 mm in thickness. Samples underwent the process of vacuum nitriding in the multipurpose furnace accordingly to parameters presented in Table 1. Processes were divided into stages of nitriding (N) and diffusion (D). Two types of process division were chosen on the basis of the previous investigations. The digit in the process stage represents the time in hours. After processes the cross sections of samples were prepared, microhardness profile was assessed as well as microstructure observation. the residual stresses distribution was measured by the X-ray diffraction method. RESULTS Microhardness was assessed by the KB tester on each sample in separate rows from the surface to the depth of 150 micrometers with load of 0.1 N, results are the average from three rows measurements. The total depth case was determined at criterion core hardness + 50HV. Result of microhardness measurements is presented in Figure 2. Microstructure of samples was observed on the Nikon Eclipse light microscope. Samples underwent the typical preparation process, namely grinding, polishing and etching in 4%HNO 3 solution in alcohol. Pictures of the observed samples are presented in Figure 3. Fig. 1. The low-pressure nitriding process exemplary schedule Rys. 1. Schematyczny przebieg procesu azotowania próżniowego Table 1. Nitriding process parameters Tabela 1. Parametry procesów azotowania Sample No. Temperature Process stages Nitrogen flow l/h 1 520 2N2D 7.5 2 560 2N2D 7.5 3 520 2A1D2A1D 7.5 4 560 2A1D2A1D 7.5 Dr inż. Bartłomiej Januszewicz (bartlomiej.januszewicz@p.lodz.pl), prof. dr hab. inż. Piotr Kula, dr inż. Emilia Wołowiec, dr inż. Adam Rzepkowski – Instytut Inżynierii Materiałowej, Wydział Mechaniczny, Politechnika Łódzka