Enhancing the Tensile Properties and Ductile-Brittle Transition Behavior of the EN S355 Grade Rolled Steel via Cost-Saving Processing Routes.

Structural rolled steels are the primary products of modern ferrous metallurgy. Consequently, enhancing the mechanical properties of rolled steel using energy-saving processing routes without furnace heating for additional heat treatment is advisable. This study compared the effect on the mechanical properties of structural steel for different processing routes, like conventional hot rolling, normalizing rolling, thermo-mechanically controlled processing (TMCP), and TMCP with accelerating cooling (AC) to 550 °C or 460 °C. The material studied was a 20 mm-thick sheet of S355N grade (EN 10025) made of low-carbon (V+Nb+Al)-micro-alloyed steel. The research methodology included standard mechanical testing and microstructure characterization using optical microscopy, scanning and transmission electronic microscopies, energy dispersive X-ray spectrometry, and X-ray diffraction. It was found that using different processing routes could increase the mechanical properties of the steel sheets from S355N to S550QL1 grade without additional heat treatment costs. TMCP followed by AC to 550 °C ensured the best combination of strength and cold-temperature resistance due to formation of a quasi-polygonal/acicular ferrite structure with minor fractions of dispersed pearlite and martensite/austenite islands. The contribution of different structural factors to the yield tensile strength and ductile-brittle transition temperature of steel was analyzed using theoretical calculations. The calculated results complied well with the experimental data. The effectiveness of the cost-saving processing routes which may bring definite economic benefits is concluded.

Green Corrosion Inhibition on Carbon-Fibre-Reinforced Aluminium Laminate in NaCl Using Aerva Lanata Flower Extract.

Aluminium-based fibre-metal laminates are lucrative candidates for aerospace manufacturers since they are lightweight and high-strength materials. The flower extract of aerva lanata was studied in order to prevent the effect of corrosion on the aluminium-based fibre-metal laminates (FMLs) in basic media. It is considered an eco-friendly corrosion inhibitor using natural sources. Its flower species belong to the Amaranthaceae family. The results of the Fourier-transform infrared spectroscopy (FTIR) show that this flower extract includes organic compounds such as aromatic links, heteroatoms, and oxygen, which can be used as an organic corrosion inhibitor in an acidic environment. The effectiveness of the aerva-lanata flower behaviour in acting as an inhibitor of the corrosion process of FMLs was studied in 3.5% NaCl solution. The inhibition efficiency was calculated within a range of concentration of the inhibitor at room temperature, using the weight-loss method, potentiodynamic polarization measurements and electrochemical-impedance spectroscopy (EIS). The results indicate a characterization of about 87.02% in the presence of 600 ppm of inhibitor. The Tafel curve in the polarization experiments shows an inhibition efficiency of 88%. The inhibition mechanism was the absorption on the FML surface, and its absorption was observed with the aid of the Langmuir adsorption isotherm. This complex protective film occupies a larger surface area on the surface of the FML. Hence, by restricting the surface of the metallic layer from the corrosive medium, the charge and ion switch at the FML surface is reduced, thereby increasing the corrosion resistance.

Abrasive Wear of High-Carbon Low-Alloyed Austenite Steel: Microhardness, Microstructure and X-ray Characteristics of Worn Surface.

A high-carbon, high-silicon steel (1.21 wt% C, 2.56 wt% Mn, 1.59 wt% Si) was subjected to quenching from 900 and 1000 °C, resulting in microstructures containing 60 and 94% of retained austenite, respectively. Subsequent abrasive wear tests of quenched samples were performed using two-body abrasion and three-body abrasion testing machines. Investigations on worn surface and subsurface were carried out using SEM, XRD, and microhardness measurement. It was found that the highest microhardness of worn surface (about 1400 HV0.05) was achieved on samples quenched from 900 °C after three-body abrasion. Microhardness of samples after two-body abrasion was noticeably smaller. with a maximum of about 1200 HV0.05. This difference correlates with microstructure investigations along with XRD results. Three-body abrasion has produced a significantly deeper deformed layer; corresponding diffractograms show bigger values of the full width at half maximum parameter (FWHM) for both α and γ alone standing peaks. The obtained results are discussed in the light of possible differences in abrasive wear conditions and differing stability of retained austenite after quenching from different temperatures. It is shown that a structure of metastable austenite may be used as a detector for wear conditions, as the sensitivity of such austenite to phase transformation strongly depends on wear conditions, and even small changes in the latter lead to significant differences in the properties of the worn surface.

Microstructure and Properties of Heat Affected Zone in High-Carbon Steel after Welding with Fast Cooling in Water.

The purpose of the research was to obtain an arc welded joint of a preliminary quenched high-carbon wear resistant steel without losing the structure that is previously obtained by heat treatment. 120Mn3Si2 steel was chosen for experiments due to its good resistance to mechanical wear. The fast cooling of welding joints in water was carried out right after welding. The major conclusion is that the soft austenitic layer appears in the vicinity of the fusion line as a result of the fast cooling of the welding joint. The microstructure of the heat affected zone of quenched 120Mn3Si2 steel after welding with rapid cooling in water consists of several subzones. The first one is a purely austenitic subzone, followed by austenite + martensite microstructure, and finally, an almost fully martensitic subzone. The rest of the heat affected zone is tempered material that is heated during welding below A1 critical temperature. ISO 4136 tensile tests were carried out for the welded joints of 120Mn3Si2 steel and 09Mn2Si low carbon steel (ASTM A516, DIN13Mn6 equivalent) after welding with fast cooling in water. The tests showed that welded joints are stronger than the quenched 120Mn3Si2 steel itself. The results of work can be used in industries where the severe mechanical wear of machine parts is a challenge.

Machine Learning Modelling and Feature Engineering in Seismology Experiment.

This article aims to discusses machine learning modelling using a dataset provided by the LANL (Los Alamos National Laboratory) earthquake prediction competition hosted by Kaggle. The data were obtained from a laboratory stick-slip friction experiment that mimics real earthquakes. Digitized acoustic signals were recorded against time to failure of a granular layer compressed between steel plates. In this work, machine learning was employed to develop models that could predict earthquakes. The aim is to highlight the importance and potential applicability of machine learning in seismology The XGBoost algorithm was used for modelling combined with 6-fold cross-validation and the mean absolute error (MAE) metric for model quality estimation. The backward feature elimination technique was used followed by the forward feature construction approach to find the best combination of features. The advantage of this feature engineering method is that it enables the best subset to be found from a relatively large set of features in a relatively short time. It was confirmed that the proper combination of statistical characteristics describing acoustic data can be used for effective prediction of time to failure. Additionally, statistical features based on the autocorrelation of acoustic data can also be used for further improvement of model quality. A total of 48 statistical features were considered. The best subset was determined as having 10 features. Its corresponding MAE was 1.913 s, which was stable to the third decimal point. The presented results can be used to develop artificial intelligence algorithms devoted to earthquake prediction.