In recent years, interest has been increasing in application of Nickel alloys in the oil industry. For subsea engineering, the possibility to weld high-strength materials in an effective manner is essential. Friction Stir Welding (FSW) is alternative to join several materials retaining their properties or even improving them. This fact is relevant for Corrosion-Resistant Alloys (CRA) used in deep-water exploitation of hydrocarbons. Publications up to now have focused on FSW of Inconel® series as alloy 600, 625, and 718. To provide a solid basis for development, this review discusses the crucial points for FSW. The tool materials are described, as well as the joint microstructure and properties achieved. Furthermore, the basics of the corrosion resistance and the early corrosion studies of FSW joints are presented. It is concluded that FSW is a promising process for Ni alloys, but depends on upcoming research regarding tool technology and corrosion investigations.

Understanding the acting wear mechanisms in many cases is key to predicting lifetime, developing models describing component behavior, or for the improvement of the performance of components under tribological loading. Conventionally scanning electron microscopy (SEM) and sometimes additional analytical techniques are performed in order to analyze wear appearances, i.e., grooves, pittings, surface films, and others. In addition, experience is required in order to draw the correct and relevant conclusions on the acting damage and wear mechanisms from the obtained analytical data. Until now, different types of wear mechanisms are classified by experts examining the damage patterns manually. In addition to this approach based on expert knowledge, the use of artificial intelligence (AI) represents a promising alternative. Here, no expert knowledge is required, instead, the classification is done by a purely data-driven model. In this contribution, artificial neural networks are used to classify the wear mechanisms based on SEM images. In order to obtain optimal performance of the artificial neural network, a hyperparameter optimization is performed in addition. The content of this contribution is the investigation of the feasibility of an AI-based model for the automated classification of wear mechanisms.

In this work, Al alloys with 6.6%, 10.4%, and 14.6% Si were deposited as thick coatings by Friction Surfacing (FS), resulting in grain refinement and spheroidization of needle-shaped eutectic Si phase. Lubricated sliding wear tests were performed on a pin-on-disc tribometer using Al-Si alloys in as-cast and FS processed states as pins and 42CrMo4 steel discs. The chemical composition of the worn surfaces was analyzed by X-ray photoelectron spectroscopy (XPS). The wear mechanisms were studied by scanning electron microscopy (SEM) and focused ion beam (FIB), and the wear was evaluated by measuring the weight loss of the samples. For the hypoeutectic alloys, spheroidization of the Si phase particles in particular leads to a significant improvement in wear resistance. The needle-shaped Si phase in as-cast state fractures during the wear test and small fragments easily detach from the surface. The spherical Si phase particles in the FS state also break away from the surface, but to a smaller extent. No reduction in wear due to FS was observed for the hypereutectic alloy. Here, large bulky primary Si phase particles are already present in the as-cast state and do not change significantly during FS, providing high wear resistance in both material states. This study highlights the mechanisms and limitations of improved wear resistance of Si-rich Al alloys deposited as thick coatings by Friction Surfacing.

In friction surfacing (FS), material is deposited onto a substrate in the plasticized state, using frictional heat and shear stresses. The coating material remains in the solid state and undergoes severe plastic deformation (SPD) at high process temperatures (≈0.8 T_melt), followed by high cooling rates in the range of 30 K/s. Dynamic recrystallization and the thermal cycle determine the resulting microstructure. In this study, Ni-based alloy 625 was deposited onto 42CrMo4 substrate, suitable, for instance, for repair welding of corrosion protection layers. Alloy 625 is known to undergo discontinuous dynamic recrystallization under SPD, and the resulting grain size depends on the strain rate. The coating microstructure was studied by microscopy and electron backscatter diffraction (EBSD). The coatings exhibit a fully recrystallized microstructure with equiaxed grains (0.5–12 µm) and a low degree of grain average misorientation. Flow lines caused by a localized decrease in grain size and linear alignment of grain boundaries are visible. Grain nucleation and growth were found to be strongly affected by localized shear and nonuniform material flow, resulting in varying amounts of residual strain, twins and low-angle grain boundaries in different regions within a single coating layer’s cross section. FS can be used to study dynamic recrystallization at high temperatures, strains and strain rates, while at the same time materials with a recrystallization grain size sensitive to the strain rate can be used to study the material flow during the process.

For a better understanding and modeling of cavitation, investigations of isolated single-bubble events are useful. They allow for describing and quantifying the physics of the bubble collapse and its interaction with surrounding material. The generation of such single bubbles in a liquid using an electric spark or focused laser beam is well described in the literature, and sophisticated analyses of bubble dynamics near surfaces exist. However, only a few studies addressed the material damage induced by single-bubble collapse. This article presents experiments in water, generating single bubbles with 3-mm diameters at various defined distances to the polished surface of a commercially pure aluminum specimen. The collapse of each laser-induced bubble was captured by high-speed imaging. A detailed quantitative analysis of the surface damage was performed using 3-D profilometry. A single bubble created a shallow pit with a typical depth of 1 to 2 μm. The overall statistics of the damage parameters, such as pit depth and volume, are consistent with previous investigations. Outliers with unusually small or multiple pits can be explained by the high-speed images of the corresponding bubble collapse. The image sequences also help identify effects of the edge of the specimen surface. In addition, a complementary numerical investigation of single bubbles based on the Navier-Stokes equations was used to obtain flow characteristics near the surface, such as microjet impact and pressure. For the commercially pure aluminum used in this study, simulation and measured surface damage correlate well. The methods developed here provide a basis for studies on more complex engineering materials.