Many steel structures can absorb varying amounts of hydrogen during manufacturing, processing such as electroplating or acid pickling and during service life by corrosion reactions or cathodic protection. Hydrogen is known to have a devastating effect on strength and ductility of iron and steel components. Many studies have been done on the detrimental effects of hydrogen on iron and different steel structures but there is a need for comprehensive research to cover all related parameters which can control the extent of hydrogen interaction and degradation in steels. In this review, at first hydrogen uptake and different damages associated with absorbed hydrogen in steels are discussed. Then, the effective parameters in the extent of hydrogen interaction and degradation, especially hydrogen embrittlement which is more common in steel components are reviewed.

The effects of surface pretreatments on the cerium-based conversion coating applied on an AA5083 aluminum alloy were investigated using a combination of scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), polarization testing, and electrochemical impedance spectroscopy. Two steps of pretreatments containing acidic or alkaline solutions were applied to the surface to study the effects of surface pretreatments. Among the pretreated samples, the sample prepared by the pretreatment of the alkaline solution then acid washing presented higher corrosion protection (~3 orders of magnitude higher than the sample without pretreatment). This pretreatment provided a more active surface for the deposition of the cerium layer and provided a more suitable substrate for film formation, and made a more uniform film. The surface morphology of samples confirmed that the best surface coverage was presented by alkaline solution then acid washing pretreatment. The presence of cerium in the (EDS) analysis demonstrated that pretreatment with the alkaline solution then acid washing resulted in a higher deposition of the cerium layer on the aluminum surface. After selecting the best surface pretreatment, various deposition times of cerium baths were investigated. The best deposition time was achieved at 10 min, and after this critical time, a cracked film formed on the surface that could not be protective. The corrosion resistance of cerium-based conversion coatings obtained by electrochemical tests were used for training three computational techniques (artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine regression (SVMR)) based on Pretreatment-1 (acidic or alkaline cleaning: pH (1)), Pretreatment-2 (acidic or alkaline cleaning: pH (2)), and deposition time in the cerium bath as an input. Various statistical criteria showed that the ANFIS model (R² = 0.99, MSE = 48.83, and MAE = 3.49) could forecast the corrosion behavior of a cerium-based conversion coating more accurately than other models. Finally, due to the robust performance of ANFIS in modeling, the effect of each parameter was studied.

Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets.

Different amounts of nanoclay were incorporated into the acrylic resin matrix at 0, 1, 3, and 5 wt.% loadings. The coatings were applied on low carbon steel plates. Optical microscopy, sedimentation test, transmission electron microscopy, and X-ray diffraction were employed to investigate the dispersion of nanoclay in matrix. The corrosion resistance of coatings was evaluated by electrochemical impedance spectroscopy, polarization measurement, and salt spray test. In addition, pull-off and cross-cut tests were used for the assessment of coating adhesion to the substrate. The results indicated that the anti-corrosive properties of the acrylic resin were obviously increased by the addition of nanoclay. The nanocomposite coatings containing 3 wt.% clay showed the best corrosion resistance. Finally, the nanocomposites containing 1 and 3 wt.% showed the highest adhesion to the substrate.