Wissenschaftliche Artikel der BAM
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High-entropy alloys (HEAs) are characterized by a solid solution of minimum five and medium-entropy alloys (MEAs) of minimum three principal alloying elements in equiatomic proportions. They show exceptional application properties, such as high-strength and ductility or corrosion resistance. Future HEA/MEA-components could be exposed to hydrogen containing environments like vessels for cryogenic or high-pressure storage where the hydrogen absorption and diffusion in these materials is of interest. In our study, we investigated the HEA Co20Cr20Fe20Mn20Ni20 and the MEA Co33.3Cr33.3Ni33.3. For hydrogen ingress, cathodic charging was applied and diffusion kinetic was measured by high-resolution thermal desorption spectros-copy using different heating rates up to 0.250 K/s. Peak deconvolution resulted in high-temperature desorption peaks and hydrogen trapping above 280 °C. A total hydrogen concentration > 40 ppm was identified for the MEA and > 100 ppm for HEA. This indicates two important effects: (1) delayed hydrogen diffusion and (2) considerable amount of trapped hydrogen that must be anticipated for hydrogen assisted cracking phenomenon. Local electrochemical Volta potential maps had been measured for the hydrogen free condition by means of high-resolution Scanning Kelvin Probe Force Microscopy (SKPFM).
The corrosion resistance of aluminium surfaces is closely linked to the surfacecstate after a grinding process. For years, iron‐containing abrasive materials were suspected to lead to increased corrosion susceptibility after processing of aluminium surfaces. To prove a possible correlation between the iron content of an abrasive and the corrosion behaviour of aluminium components, scientific investigations and experimentally practical corrosion tests are necessary.
For the current investigation, specimens of a technical Al‐Si alloy from the same batch were used. The test specimens were mechanically ground with various resin‐bonded model abrasives containing different iron contents. The performed corrosion tests did not reveal a negative influence of the different iron‐containing abrasives on the corrosion behaviour of the Al–Si alloy.
However, the most sensitive measuring method (electrochemical noise)
showed differences in the surface activity depending on the type of abrasive.
Early Stage of Corrosion Formation on Pipeline Steel X70 Under Oxyfuel Atmosphere at Low Temperature
(2020)
The early stage of corrosion formation on X70 pipeline steel under oxyfuel atmosphere was investigated by applying a simulated gas mixture (CO2 containing 6700 ppmv O2, 100 ppmv NO2, 70 ppmv SO2 and 50 ppmv H2O) for 15 h at 278 K and ambient pressure. Short-term tests (6 h) revealed that the corrosion starts as local spots related to grinding marks progressing by time and moisture until a closed layer was formed. Acid droplets (pH 1.5), generated in the gas atmosphere, containing a mixture of H2SO4 and HNO3, were identified as corrosion starters. After 15 h of exposure, corrosion products were mainly X-ray amorphous and only partially crystalline. In-situ energy-dispersive X-ray diffraction (EDXRD) results showed that the crystalline fractions consist primarily of water-bearing iron sulfates. Applying Raman spectroscopy, water-bearing iron nitrates were detected as subordinated phases. Supplementary long-term tests exhibited a significant increase in the crystalline fraction and formation of additional water-bearing iron sulfates. All phases of the corrosion layer were intergrown in a nanocrystalline network. In addition, numerous globular structures have been detected above the corrosion layer, which were identified as hydrated iron sulphate and hematite. As a type of corrosion, shallow pit formation was identified, and the corrosion rate was about 0.1 mma−1. In addition to in-situ EDXRD, SEM/EDS, TEM, Raman spectroscopy and interferometry were used to chemically and microstructurally analyze the corrosion products.
Machine learning based multi-sensor fusion for the nondestructive testing of corrosion in concrete
(2020)
Half-cell potential mapping (HP) is the most popular non-destructive testing method (NDT) for locating corrosion damage in concrete. It is generally accepted that HP is susceptible to environmental factors caused by salt-related deterioration, such as different moisture and chloride gradients. Additional NDT methods are able to identify distinctive areas but are not yet used to estimate more accurate test results. We present a Supervised Machine Learning (SML) based approach to data fusion of seven different signal features to obtain higher quality information. SMLs are methods that explore (or learn) relationships between different (sensor) data from predefined data labels. To obtain a representative, labelled data set we conducted a comprehensive experiment simulating the deterioration cycle of a chloride exposed device in the laboratory. Our data set consists of 18 measurement campaigns, each containing HP, Ground Penetrating- Radar, Microwave Moisture and Wenner resistivity data. We compare the performance of different ML approaches. Many outperform the best single method, HP. We describe the intrinsic challenges posed by a data-driven approach in NDT and show how future work can help overcome them.