The user of plasma nitriding technology is often faced with the question as to how an optimum nitriding result can be achieved and which process parameters need to be set for this purpose. In most cases, this is limited to the desired nitriding hardness depth, and standard processes are applied for the nitriding which are not optimally adapted to the material, the geometry or the end application.
Within the framework of the project, more than 500 combinations of a widely varying range of materials and process parameters were investigated. The treated samples were subsequently comprehensively characterized and evaluated. In order to validate the results, comparative tests were carried out on industrial facilities. In parallel, in collaboration with the Institute of Materials Science and Engineering at Chemnitz University of Technology, a neural network was trained using the data in order to provide a fundament for the creation of a software-based prognosis tool for the plasma nitriding processes.
The results were processed in the form of a data collection which enables the nitriding result to be estimated for e.g. a given material as well as process parameters or, alternatively, the required process parameters to be specifically selected for the desired nitriding result. As a result, the tool life of tools and/or the service life of components can be significantly increased.
The IGF project 18741 BG of the research association Deutsche Gesellschaft für Galvano- und Oberflächentechnik e.V. (DGO) is funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) via the AiF within the framework of the program for the promotion of joint industrial research (IGF) based on a resolution of the German Bundestag.