Mechanisms of Oxidation Degradation of Cr12 Roller Steel during Thermal Fatigue Tests

Degradation by the penetration of oxidation into the Cr12 roller steel is evaluated during thermal fatigue tests in the laboratory in the temperature range of 500–700 °C. A qualitative assessment is carried out with regard to the thermal load, the microstructure and the test temperature. The results show that the specific properties of the microstructure with respect to thermal stress and temperature have a significant influence on the oxidation behavior as well as on the crack propagation mode and crack growth. The conditions that lead to an increase in the oxidation rate and thus to premature and sudden local chipping of the roll surface layer are analyzed and explained.</jats:p

Stability of retained austenite in martensitic high carbon steels:Part II: Mechanical stability

The mechanical stability of retained austenite is explored in martensitic bearing steels under cyclic compressive stresses up to ∼106 cycles at 3GPa, combining X-ray diffraction and repetitive push testing. Finite element analysis and hardness testing were adopted to interpret the stress distribution across the specimen, and the stress-strain response was revealed. Austenite decomposition was observed for all samples regardless of the difference in their chemical composition and volume percentage. The decomposition is partial and a significant amount of austenite could be retained even after ∼106 stress cycles. A scenario revealing different stages of retained austenite behaviour under compressive stresses has been established. It is observed that retained austenite first decomposes during the first tens of cycles and at 103 cycles, whilst it remains stable at cycles ranging 102–103 and after 104. More importantly, results show the potential TRIP effect of retained austenite decomposition on dynamic hardening of bearing steels

Dynamic Fracture Criteria Evaluation of Bridge Structural Steel

J-integral is the main effective and commonly used tool for elastic-plastic cracked material resistance assessment. Considering ductile behavior of bridges steel integral approach is suitable for fracture toughness evaluation. The paper presents the method of dynamic fracture parameter J-integral evaluation in case of elastic-plastic deformation of bridge structural steel. This experimental technique is based on determination of impact fracture energies and displacements which correspond to these energies at the moment when loading rate reaches max and fracture loads. Theoretical solutions were confirmed by experimental data obtained from Three-Point Bend tests of rectangular cross section specimens with V form notch. Impact loading was generated by impact tester with drop weight. 5 series of specimens with different geometry were tested during experiment. The developed methodology enables to predict the impact fracture toughness of bridge structural elements

Dynamic J-integral evaluation of three-point-bend beams with various geometrical dimensions

J-Integral is the main effective and commonly used tool for cracked elastic-plastic material resistance assessment. Determination of fracture toughness under impact loading conditions is related with problems of crack length measurement. Nevertheless, current experimental techniques restrict the specimen’s geometry taking into account span and height ratio, which is equal to four. Evaluation of fracture toughness estimation method which requires only experimental load-line displacement curve of single specimen is research object of dynamic fracture mechanics. This article proposes an approach of impact fracture toughness determination of elastic-plastic steel from single any size specimen test. Load-line displacement data obtained from three-point-bending tests of rectangular cross section specimens with V form single edge notch was used for J-integral calculation. Five series of specimens with different geometry were manufactured from ductile steel and tested

An Industrial Approach to High Strain Rate Testing

Some industrial applications require properties of materials to be determined to evaluate components’ safety in the event of loading and impact. Understanding the behaviour of materials subjected to extreme dynamic loading will aid in enhancing their design. This work is based on developing methods to appraise high loading rate measurements. Different approaches to quantify material properties such as finite element method (FEM), instrumented Charpy testing, and impact testing using servo-hydraulic testing machines are included. Testing is performed at various loading rates, extending existing quasi-static fracture toughness determination to higher loading rates, and accounting for strain-rate dependent properties. The high loading rate servo-hydraulic test machine located at TWI, Cambridge has the capacity to test up to a displacement rate of 20 m/s. The force, displacement, and time parameters are captured by Digital Image Correlation (DIC), which improves the accuracy of the results obtained from experiments. Moreover, the underlying plasticity theory to capture the influence of the strain rate is presented, along with damage constants for FEM calculations adopting the Johnson-Cook model. In addition to the Johnson-Cook approach, analytical solutions using dislocation evolution theory were applied which features the effects of phonon drag and dynamic recovery coefficient in body-centered cubic materials of which X65 grade steel was applied. Also, a deep learning framework was built to predict the tensile curves when given specific test conditions and sample specifications. It was found that high strain rate tests lead to local change at the crack tip which increases plasticity and reduces fracture toughness with single-edged notched three-point bend specimen. The yield strength of the material increased with loading rates during tensile testing leading to a ductile to brittle transition of metals. These strategies were used to establish a revised approach for high strain rate testing and predicting stress-strain curves with a machine learning algorithm