Volume 77, Issue 6

In this research a combination of nitriding and induction hardening is investigated, as this is expected not only to result in significant savings in process time and energy, but also to produce surface layer properties that cannot be set with one of the individual processes. The focus of the current investigations was on the dissolution of the compound layer during inductive heating and the resulting microstructure formation and the hardness profile. Furthermore, it was investigated how the absence of a compound layer affects the subsequent martensitic transformation. For this purpose, differently nitrided surface layers were martensitically hardened and the microstructure was investigated metallographically and physically. After the martensitic transformation of the nitrided layer porosity and retained austenite were observed due to the decomposition of the nitrides of the compound layer. The retained austenite could be reduced by higher temperatures during surface hardening and compound layer removal. The investigations showed, that the optimum initial condition for induction hardening is nitriding with compound layer and a mechanical removal of the latter prior to induction heat treatment.

Ensuring product and part quality impacts manufacturing productivity, efficiency and profitability. The goal of every manufacturing company is to quickly identify reduced quality in order to take appropriate measures to improve quality. The use of non-destructive testing methods such as Barkhausen noise in combination with artificial intelligence (AI), which immediately classifies the data, offers a way to implement the desired quality monitoring in a production line. In the present study, the measured data of the Barkhausen signal of surface hardened components with different degrees of tempering were analyzed. For this purpose, suitable AI models were developed and trained with the processed measurement data to generate prediction values for the surface hardness. Data preparation and further processing was carried out using the Spyder development environment with the Python programming language. The following models were applied, tested and optimized during the study: Support vector machine, random forest regression and an artificial neural network. The models were able to predict hardness levels with high accuracy after effective training. Overall, the neural network showed the best results. The applied procedures and methods are fast, non-destructive and provide results with acceptable measurement error, which allows their use in the production environment. Further improvements will be sought in the future, e. g. by applying a larger amount of training data, by changing the features used in the training and by increasing the measurement accuracy when capturing the Barkhausen signal.

Austenitic and duplex stainless steels are used in a variety of industries due to their high corrosion resistance. However, this is countered by a high coefficient of friction and thus a limited wear behaviour, which restricts the technical application possibilities. Coatings or conventional hardening processes, such as solution nitriding above 1000 °C, offer a certain reduction of these wear phenomena, but are associated with the risk of delamination and/or loss of corrosion properties. In addition, the hardening effect is limited for austenitic and duplex grades. The low temperature surface hardening process below 500 °C offers the possibility to overcome these limitations. In this article the technology of this process is explained. In addition, research results show the positive influence of this treatment on the lifetime of components made of corrosion resistant steels. As a future outlook, possible properties and applications for the prospective increasing number of hydrogen applications are explained.

Binder-based additive manufacturing processes for metallic AM components in a wide range of applications usually use organic binders and process-related additives that must be thermally removed before sintering. Debinding processes are typically parameterized empirically and thus far from the optimum. Since debinding based on thermal decomposition processes of organic components and the subsequent thermochemical reactions between process atmosphere and metal powder materials make uncomplicated parameterization difficult, in-situ instrumentation was introduced at Fraunhofer IFAM. This measurement method relies on infrared spectroscopy and mass spectrometry in various furnace concepts to understand the gas processes of decomposition of organic components and the subsequent thermochemical reactions between the carrier gas atmosphere and the metal part, as well as their kinetics. This method enables an efficient optimization of the temperature-time profiles and the required atmosphere composition to realize dense AM components with low contamination. In the paper, the optimization strategy is presented, and the achievable properties are illustrated using a fused filament fabrication (FFF) component example made of 316L stainless steel.