Mechanical Properties Of Steel Research Articles

The Concept of Technological Support of Operational Properties Based on Stabilization of Mechanical Properties of Gear Materials in a Gas Turbine Engine Drive System

Modern production of gears for gas turbine engines for aviation, marine and land use is inherently related to the calculation of the stress state of the working surfaces of the mating teeth. To create conditions for the operability of the gear train and the drive system as a whole, an engineering analysis of the contact endurance of the mating surfaces is a necessary criterion. The contact strength determines the life of the gear mechanism. The value of the contact strength, taking into account its distribution in the contact zone of the working mating surfaces of the gears, is determined by the processing technology and operation, but the mechanical properties and texture of the material have the greatest influence. This determines the requirements for the stability of the mechanical properties of the materials used for the manufacture of gears, making a topical question on their study. The technology of manufacturing the gears in the gas turbine engine drives forms the layer structure of the toothed crown because of the use of chemical-thermal treatment. The surface hardened layer of the gear tooth after chemical-thermal treatment is saturated with carbon and nitrogen, and as a result of thermal action acquires mechanical properties and structure different from the material in the supply. The paper presents the results of practical research of evolution of mechanical properties of structural steels 20H3MVF-Sh, 18H2N4MA, 16H3NVFM-Sh and 12H2N4A-Sh based on various types of chemical-thermal treatment, as well as microstructure of toughened layer and core of the part. The steel mechanical properties stabilization evaluation in the process of gear technology was performed. The presented results of experimental studies of mechanical properties of the material prove stabilization of mechanical and structural indicators, which are responsible for the contact strength of the mating surfaces of toothed gears.

Analyzing the permeability distribution of multilayered specimens using pulsed eddy-current testing with multi-scale 1D-ResNet

The mechanical properties of steel are determined by its microstructure, which is closely related to its permeability profile. In thermal processing, layered structures are formed in steel and different layers have different mechanical and magnetic properties. Therefore, it is crucial to propose a practical method to monitor the change of permeability profile along the depth, which can indicate the evolution of the microstructure of steel during thermal processing, such as hot rolling. This paper presents a method for determining the layered structure and permeability profile of the steel by using pulsed eddy current testing (PECT), which offers better penetration ability. An analytical model has been deduced for calculating the time-domain pulsed eddy current (PEC) response from a Hall sensor of a triple-layer conductor system based on the inverse Laplace transform. It is found the Tau (τ) curve is closely related to the permeability profile of the conductor. For the inverse solution, the Simultaneous Iterative Reconstruction Technique (SIRT) is utilized to determine the permeability profile of the multilayered specimens from the measured response. The approximate Jacobian matrix (sensitivity matrix) is obtained by the perturbation method based on the Tau curve. However, the permeability profile suffers from smoothing effect and sharp features are lost. Deep learning (DL) algorithm based on the Multi-Scale 1D-ResNet model is therefore introduced to address this issue. Numerical simulations and experiments have been performed to evaluate the proposed method for permeability profile estimation with various materials and thicknesses. The DL method can achieve an accurate estimation of the plate permeability profile with a relative error under 5%.

THE EFFECT OF THE ELECTROLYTE COMPOSITION ON THE MICROSTRUCTURE AND MECHANICAL PROPERTIES OF STEEL 45 AFTER CATHODIC ELECTROLYTE-PLASMA NITRIDING

This research offers an in-depth exploration of the impact of cathodic nitriding on the structural and mechanical characteristics of 45 steel treated in different aqueous electrolytes. Through a combination of Xray diffraction and electron microscopy analyses, it was determined that the nitriding process facilitates the development of a multilayered surface structure, which includes oxide, nitride, and martensitic layers. The composition of the electrolyte plays a crucial role in determining the phase composition and thickness of the modified layers, directly influencing the steel's mechanical properties, as reflected by variations in hardness and wear resistance post-treatment. Notably, an electrolyte containing sodium carbonate (Na₂CO₃) and urea (CH₄N₂O) achieved a maximum microhardness of 986 HV due to the formation of a dense nitride layer. On the other hand, introducing ammonium nitrate to the electrolyte, while slightly decreasing the microhardness to 882 HV, resulted in the formation of a more intricate and stable phase structure, including additional nitrides and oxides, which contributed to enhanced corrosion resistance. These findings underscore the critical importance of optimizing electrolyte composition to improve the performance characteristics of steel, such as hardness, wear resistance, and corrosion resistance. This study underscores the effectiveness of cathodic nitriding as a method for significantly enhancing the mechanical and surface properties of 45 steel, thereby expanding its potential for use in high-load and aggressive environments.

Prediction of mechanical properties of rolled steel based on dual-attention multiscale convolutional neural network

The mechanical properties of carbon steel sheet directly depend on the chemical composition and process parameters of the steel. There is a complex nonlinear relationship between chemical composition, process parameters, and mechanical properties, and the establishment of a model with the ability to automatically distinguish the importance of different parameters, and to have good prediction accuracy and generalisation in different data sets is one of the important issues in the field of carbon steel mechanical properties prediction. In this paper, we proposed a multi-scale convolutional network model based on dual-attention mechanism (DAM-MSDSC), where the dual-attention module mines from features and channels at the same time, making the model focus on the important features. The multiscale feature fusion module effectively improves the model prediction accuracy by mining features from the output of the middle layer of the network to obtain features containing multiscale fusion features. In hot-rolled steel, for the two parameters of carbon content and temperature, it is confirmed that the change of carbon content has a greater impact on TS, while FTT and CT have a greater impact on YS. Through comparative experiments on different datasets, the proposed model had the best prediction performance, in which the correct rates of YS, TS, and EL are 98 %, 98.7 %, and 99.4 %, respectively, in cold rolled steel, and 97.8 %, 98.6 %, and 99.2 %, respectively, in hot rolled steel.

Predicting resistance of bearing steels to surface initiated fatigue: Application to hybrid contact

The constant demand for improved energy efficiency in lubricated rolling/ sliding contacts, such as in rolling bearings, is generally addressed through increased power density and the use of low-viscosity (i.e., thin-film) lubricants. In these conditions, mixed lubrication regime can dominate with intensive asperity interactions between two contacting surfaces, thus increasing the risk of surface-initiated fatigue (e.g., in the form of micropitting). In the present study, the influence of steel mechanical properties on the micropitting accumulation in a hybrid contact (steel versus Si3N4 ceramics of different roughness) has been experimentally and theoretically investigated. It was found that higher roughness of Si3N4 surface could increase the risk of micropitting on the steel component. However, despite formation of some mild micropitting, the bearing steels tested here show good performance. For better understanding of the relationship between the mechanical properties of bearing steels and micropitting resistance, a new theoretical framework is introduced. The new micropitting model, based on the finding that the nucleation of a micropit is driven by (i) local accumulation of the plastic strain in mixed rough contact, and (ii) reduction of the total energy by debonding of the plastically deformed zone is introduced. The new micropitting model was shown to match with experiments across the range of test conditions. Micropitting performance maps based on the mechanical properties of steels (yield strength, work hardening and fracture toughness) have been obtained. The new theory has implications not only for predicting micropitting but also for predicting adhesive wear resistance of different steels.

Effects of Partially Replacing Mo with Nb on the Microstructure and Properties of High-Strength Low-Alloy Steel during Reverse Austenization

This study investigated the effects of partially replacing expensive Mo with cheaper Nb on the microstructure and properties of high-strength low-alloy (HSLA) steel during reverse austenisation. The mechanical properties of the steel in the hot-rolled state were lower with a partial replacement of Mo by Nb. However, after pre-tempering and reheating and quenching, the strength increased greatly while the ductility and toughness did not decrease much. Thus, the negative effects of replacing Mo with Nb were mostly alleviated, and a good balance between strength, ductility and toughness was achieved. After heat treatment, the mass percentage of precipitates increased substantially, which helped to pin grain boundaries during austenisation. The percent of high-angle grain boundaries greatly increased while the average effective grain size decreased, which improved grain refinement. The results showed that combining a partial replacement of Mo by Nb with heat treatment allows the microstructure and mechanical properties of HSLA steel to be effectively controlled while improving the balance between cost and performance. These findings provide valuable insights into the preparation and design of steels with similar microstructures.

Prediction and analysis of mechanical properties of hot-rolled strip steel based on an interpretable machine learning

Establishing a low-cost, high-precision predictive model for mechanical properties is crucial for enhancing the properties of steel. Due to the complexity of the steel production process, traditional mechanistic models struggle to efficiently and accurately describe the relationships among alloy elements, processes, and properties. In response to this, the paper introduces a high-precision framework for predicting and optimizing mechanical properties through feature engineering and machine learning models. Firstly, an effective dimension reduction of features is achieved through a weighted fusion heterogeneous feature selection strategy, thereby identifying the optimal model input features. Subsequently, an improved seagull optimization algorithm is utilized to optimize the hyperparameters of Extreme Gradient Boosting, further enhancing the predictive accuracy of the model. Moreover, based on the Shapley Additive Explanation method, a quantitative analysis of the model's predictive outcomes is conducted, elucidating the impacts of alloy elements and processes on mechanical properties, consistent with established principles of physical metallurgy. The proposed framework not only enables precise prediction of mechanical properties in steel but also provides theoretical guidance and technical support for process optimization design and the development of new steel grades.

Inhibition of the intergranular brittleness of HR3C heat-resistant steel by strain-aging induced nano-M23C6 dispersion precipitation

M23C6 chromium-rich carbides are common grain-boundary precipitations in Cr-containing steel. The presence of grain-boundary carbides often leads to intergranular brittleness and decreases mechanical properties. This study proposes a deformation and aging technique to obtain a high-volume-fraction dispersion distribution of the hard nano-M23C6 phase by changing the nucleation sites from grain boundaries to deformation coherent twin boundaries produced during cold deformation. The M23C6 precipitation-strengthened austenitic stainless steel has a strength of up to 1.4 GPa but maintains favorable plasticity (> 11 %). This study provides a novel approach for the control of intergranular brittleness in metallic materials.

Effect of N plasma immersion ion implantation (N-PIII) on the microstructure and mechanical properties of 8Cr4Mo4V steel

To enhance the surface hardness and wear resistance of 8Cr4Mo4V steel, nitrogen plasma immersion ion implantation (N-PIII) was carried out, under various nominal implantation energies, in this study. Additionally, the microstructure and mechanical properties of the steel were analyzed. The results indicated that the depth of the nitrogen ion-implanted layer escalated with the specified implantation energy, reaching 140 nm for a 50 keV sample. Nitrogen was present within the implanted layer in the martensite lattice as a solute, and also in the form of nitrides along with alloying elements. Moreover, a nanocrystalline layer approximately 40–70 nm thick also formed at the sample surface. As the implantation energy increased, both the surface roughness and residual stress of the sample initially increased before declining, whereas the surface hardness and modulus exhibited a gradual increase. After the 50 keV N-PIII treatment, the surface hardness of the sample increased by 39 %, which caused a remarkable reduction in the friction coefficient, and the wear amount was also reduced by about 40 %. This work systematically establishes the relationship between the surface microstructures, surface morphology, and mechanical properties of implanted 8Cr steel, and significantly improves its surface hardness and wear resistance.

Influence of Tramp Elements on Surface Properties of Liquid Medium‐Carbon Steels

The transformation of the steel industry is inevitably linked to increased recycling rates of steel. However, end‐of‐life scrap is often contaminated with tramp elements like copper, molybdenum, and tin and while their influence on mechanical properties of steels is well described, their effect on nonmetallic inclusions remains largely unknown. Therefore, herein, two medium‐carbon steels are alloyed with up to 1 wt% of the listed tramp elements. The influence these elements have on the inclusion behavior in liquid steel is investigated using the sessile drop method in contact with alumina and zirconia as well as in steel/slag melting experiments to evaluate the formation and separation of new inclusions. Additionally, a semiempirical approach using CALPHAD to model the surface tension of steels with tramp elements is done. Copper and tin decrease the surface tension, while molybdenum increases the surface tension. It is demonstrated that most investigated alloys lead to a decrease of wetting angle as compared to the nonalloyed steels. The shown trends in the sessile drop experiments are lower contact angles with increased surface tension while a higher number of inclusions occurs with a decrease in surface tension. Thus, this study demonstrates that tramp elements affect the oxidic cleanness of steels.

Data enhanced lightweight network-based prediction of cold-rolled steel mechanical properties

The production of cold-rolled carbon steel is a complex nonlinear process, with chemical composition and process parameters being the key factors influencing its mechanical properties. Establishing the mapping relationship between key parameters and mechanical properties is the key to predicting the mechanical performance of rolled steel. Existing research has overlooked that chemical composition and process parameters are two different scales of data, lacking the exploration of potential features between these scales. This paper proposed a data enhancement-based expand-channel depth-separable convolutional neural network (MBE-EDSC) to predict the mechanical properties of cold-rolled steel. Multi-branch autoencoder was proposed to enhance the original data, which could be mined for potential features of the data. Meanwhile, the self-adaptive convolution channel attention mechanism was proposed to improve the sensitivity of the model to the features, and the channel extension layer to compensate for the problem of accuracy loss caused by deep convolution. Compared with the performance of the existing networks, the proposed network achieved 97.3 %, 98.5 %, and 99.2 % accuracy in predicting the yield strength, tensile strength, and elongation of cold-rolled carbon steel, respectively, and the R2 was improved by 4.9 %, 4.3 %, and 5.9 %, respectively.

Effect of Niobium on Mechanical Properties of Steels and High-Entropy Alloys

Effect of Niobium on Mechanical Properties of Steels and High-Entropy Alloys

Experimental investigation into corrosion effect on buckling and low-cycle fatigue performance of high-strength steel bars

The reinforcement in marine RC structures will inevitably be corroded under long-time erosion of chloride ions, which will weaken steel mechanical properties and then structural seismic resistance. The new metallurgical technology is adopted for manufacturing high-strength steel bars (HSSB), but that may affect the corrosion resistance of steel bars at the same time. This paper experimentally investigates the effects of corrosion on monotonic and low-cycle fatigue properties of HSSB HTRB600 and normal strength steel bars (NSB) HRB400E. The HRB400E and HTRB600 steel bars were corroded to different corrosion rates by electrochemical accelerated corrosion technique. The monotonic tensile and compressive, as well as low-cycle fatigue tests of corroded steel bars were conducted to investigate the effects of corrosion on steel mechanical properties, of which corrosion levels (0–20% target corrosion mass loss), slenderness ratios (L/D=6, 8, 10, 12) and strain amplitudes (2%, 4%) are considered. Test results show that, the corrosion morphology of steel bars under electrochemical accelerated corrosion is basically consistent with that observed under natural corrosion. Compared with uncorroded HRB400E and HTRB600 steel bars, 20% corrosion mass loss leads to 38% and 35% decrease in tensile strength, as well as 33% and 28% reduction in buckling stress, respectively. The HTRB600 steel bars present slightly superior monotonic tensile and compressive corrosion resistance than HRB400E steel bars. Corrosion obviously decreases the low-cycle fatigue (LCF) life of steel bars, resulting in reductions of 44% and 36% in LCF life at 20% corrosion mass loss for HTRB600 and HRB400E steel with a L/D of 6, respectively. Moreover, the HTRB600 steel bars present more significant corrosion degradation in total hysteretic dissipated energy, compared with HRB400E steel bars. The combined effect of corrosion and inelastic buckling further exacerbates the deterioration of LCF performance of steel bars. The corresponding deterioration models are proposed to determine the LCF life and energy dissipation capacity of corroded steel bars.

Effect of Rotary Swaging on Mechanical Behaviors of Axle Steel Rod.

The short-chain forming process using rotary swaging (RS) is an important method of achieving the manufacturing of lightweight axles. Axle steel, like 42CrMo, is widely used in many types of axles and shafts; however, there is no existing research on rotary-swaged axle steel's mechanical properties. It makes sense to carry out a comprehensive study on the effect of RS on the mechanical behaviors of axle steel rods. In this study, a 42CrMo steel rod was processed by RS through ten passes. The tensile properties, torsion properties, compression properties, and fatigue properties were tested. There was an overall improvement in the torsional and fatigue performance after RS. Combined with a finite element analysis (FEM), the uneven distribution of the dislocations and existence of the elongation material were inferred to have caused the different modes of the mechanical behaviors. Fracture surfaces were analyzed and the results showed that the fracture pattern had changed. There existed a competitive relation between the internal fatigue cracks and external cracks, which could be attributed to uneven strain hardening. This research proved the advantages of RS in the processing of axle parts, which mainly benefitted the torsional working conditions, and provided evidence for a new processing route for lightweight axles with RS.

Effect of Si and Y solution on mechanical properties of 30Cr13 martensitic stainless steel

The effect of alloying element silicon (Si) on yttrium (Y) solid solution in steel has been studied by anhydrous electrolysis experiment and first-principles calculation. The effect of Y and Si solid solution on the mechanical properties of steel were studied by tensile and hardness test. The influence mechanism was analyzed by geometry and electronic structures. The results show that Si can promote Y solid solution in steel. Since the Fe–Y bond energy was less than the Fe–Fe bond, the elastic properties of Fe were reduced by adding Y. The extremely high density of electron clouds around Si improves some of the elastic properties of Fe–Y–Si. Compared with Fe, the Young ‘s modulus of Fe–Y–Si sample was decreased by 4.3 GPa and the hardness of Fe–Y sample was increased by 4.22 HV. Y reduced the interaction of the slip surfaces and Si enhanced it, thus changing the plasticity of the steels.

Tailoring the Microstructure using Quenching and Partitioning Processing in a Commercial Mn-Si-Cr Spring Steel to Improve Tensile Properties

This study explores a novel approach to quenching and partitioning (Q&P) heat treatment applied to AISI 9260 spring steel, comprising Fe-0.65C-1.58Mn-1.05Si-0.41Cr (wt.%). Our research focuses on balancing strength and ductility through optimized Q&P pathways, leading to a diverse microstructure that includes martensite, bainite, carbide, and retained austenite. Advanced X-ray diffraction and scanning electron microscopy techniques were employed to analyze the complexities of this microstructure. A key aspect of this study is the precise control of partitioning temperature and time, crucial for modulating lattice distortion and dislocation density within martensitic and bainitic structures. Optimal partitioning temperature promotes carbon distribution into austenite, tempering lattice distortions, and dislocation densities. Concurrently, carbide precipitation and segregation contribute to the refinement of the bainite phase. The sample quenched at 125 °C and partitioned at 350 °C (Q&P-125/350) demonstrates notable mechanical properties: a yield strength of 950 ± 15 MPa, an ultimate tensile strength of 1710 ± 15 MPa, and an elongation of approximately 9.7%. These results are partly attributed to the effect of silicon in preventing cementite coarsening and the effective distribution of carbide. Our findings highlight the potential of Q&P heat treatment in developing tailored microstructures with enhanced mechanical properties in steel, without relying on costly alloying elements. This approach presents new avenues for the design and application of high-performance materials.

Influence of prior austenite grain size on martensite-austenite islands morphology and decomposition characteristics during two-step tempering in Mn-Ni-Mo steel

Martensite-austenite (M/A) islands are typical in the quenched microstructure of Mn-Ni-Mo steel, frequently utilized for fabricating reactor pressure vessels (RPV). These islands can decompose into harmful long rod-shaped Fe3C carbides during tempering above 600°C, quantifying the negative effect of M/A island decomposition on mechanical properties and indicating a potential issue in RPV fabrication. To address this issue, the present study investigates a two-step tempering process on three different prior austenite grain size (PAGS) specimens (5 µm, 47 µm, and 119 µm). The effect of PAGS on M/A island decomposition characteristics during sub-600°C tempering and their impact on precipitated carbide morphology is investigated. A preliminary pre-tempering treatment at 200°C, 350°C, and 500°C for 2 hours was followed by a 3-hour final tempering step at 650°C. The findings revealed unique temperature-dependent decomposition characteristics of M/A islands. For example, at 200°C, partial M/A island decomposition took place, resulting in long rod-shaped carbides during final tempering. Meanwhile, the 350°C pre-tempering specimen revealed aggregated carbide precipitates of varying sizes in former M/A island locations. In contrast, the 500°C pre-tempering specimen showed coarse globular carbides and the absence of carbide precipitate clustering. This result can be attributed to the decrease in surface energy associated with the carbide-matrix interface as size increases. After tempering, all specimens produced three types of precipitates: rod-shaped Fe3C carbides, rhombus-shaped Mn7C3, and globular-shaped Mn23C6 carbides. The formation of long rod-shaped carbides during M/A island decomposition reduces the critical cleavage stress for micro-crack initiation, jeopardizing steel mechanical properties. Aggregated carbides cause the simultaneous formation of numerous micro-voids in close proximity, which eventually merge to form a large crack, resulting in material failure. When compared to other pre-tempered and directly tempered specimens, pre-tempering at 500°C produced the most favorable microstructure in terms of impact properties.

Multi-task prediction of hot rolled steel mechanical properties based on CA-MMCE

The accurate prediction of mechanical properties of multi-task hot rolling steel is of great significance to the development of manufacturing, construction, and other industries, and can effectively improve the efficiency of continuous quality control of the hot rolling process. Aiming at the problem that the existing deep models lack interpretability in the field of steel rolling, the hot rolling process was deeply analyzed, and the temperature fluctuation was integrated into the input features as a process variable through feature augmentation to obtain the correlation between features. In order to solve the problem that the conventional Attention mechanism only focuses on the overall global information, a Convolutional Attention mechanism (CA) is proposed to focus on local feature information and improve the ability of the network to extract local key features. At the same time, so as to solve the negative transfer problem of the multi-task model, a Multi-gate Mixture of Convolutional Experts (MMCE) is proposed, which uses multi-gate to adjust the influence of each convolutional expert network on different tasks to capture the correlation between tasks. Thus, it can ensure that multiple tasks can be accurately predicted. Experimental results show that, compared with the existing models, the proposed model achieves excellent prediction performance in multi-task prediction and is advanced.

Assessing the feasibility of laser ablation coating removal (LACR) on legacy bridge steel: Coating removal and adhesion, and effects on mechanical properties

The application of protective organic coatings is one of the most effective and commonly used corrosion mitigation strategies. To maintain the protective nature of coatings on long-term-exposed surfaces such as steel bridge components, coatings must be periodically removed and reapplied. A relatively new method called laser ablation coating removal (LACR), which incorporates a high energy nanosecond pulsed laser beam in combination with a high efficiency filtration system, allows for safe and effective removal of coatings and contamination from metal surfaces. In this study, LACR cleaning is tested on legacy bridge steel components to investigate the effect on substrate cleanliness and steel mechanical properties. These bridge sections were never blasted and contain a 20–100 μm thick mill-scale layer (iron oxide) below several coating layers (including lead-rich coatings). The top micron of the oxide layer is melted by LACR and also thermally insulates the underlying steel and prevents any melting within the metallic substrate. The resulting surfaces are analyzed using microscopy, hardness testing, tensile, and fatigue testing, and it is shown that LACR does not cause any measurable detrimental effects to the bulk mechanical properties of the bridge steel, as well as effectively removes all paint coatings. Furthermore, adhesion testing on LACR-cleaned substrates shows excellent adhesion, qualifying above adhesion requirements for coated steel.

Mechanical properties of Q355 hot-rolled steel during the entire fire process

This study conducts steady-state tests to comprehensively examines the mechanical properties of Q355 hot-rolled steel throughout the entire fire process, including the cooling stage. Results show that the mechanical properties at the fire-cooling stage are influenced by both the peak heating temperature (Tp) and the tensile test temperature (Tt). Within a specific threshold value of Tp, the mechanical properties at the cooling stage of fire resemble those observed in the heating stage. However, when Tp surpasses this threshold value, notable disparities emerge between the mechanical properties of steel at the cooling and heating stages. The threshold temperatures for elastic modulus, ultimate strength, and ultimate strain are all 600 °C, while that for the yield strength f0.2 is 400 °C. A cooling stage influence coefficient is used to quantitatively portray the variance in mechanical properties between the cooling and heating stages of fire. Calculation formulas for the fire cooling stage influence coefficients were provided for the elastic modulus, yield strength f0.2, ultimate strength, and ultimate strain, respectively. Finally, a constitutive model for the entire fire process of Q355 steel was established.