Review on the plastic instability of medium -Mn steels for identifying the formation mechanisms of Lüders and Portevin -Le Chatelier bands

Plastic instability,including both the discontinuous yielding and stress serrations,has been frequently observed during the tensile deformation of medium-Mn steels(MMnS)and has been intensively studied in recent years.Unfortunately,research results are controversial,and no consensus has been achieved regarding the topic.Here,we first summarize all the possible factors that affect the yielding and flow stress serrations in MMnS,including the morphology and stability of austenite,the feature of the phase interface,and the deformation parameters.Then,we propose a universal mechanism to explain the conflicting experimental results.We conclude that the discontinuous yielding can be attributed to the lack of mobile dislocation before deformation and the rapid dislocation multiplication at the beginning of plastic deformation.Meanwhile,the results show that the stress serrations are formed due to the pinning and depinning between dislocations and interstitial atoms in austenite.Strain-induced martensitic transformation,influenced by the mechanical stability of austenite grain and deformation parameters,should not be the intrinsic cause of plastic instability.However,it can intensify or weaken the discontinuous yielding and the stress serrations by affecting the mobility and density of dislocations,as well as the interaction between the interstitial atoms and dislocations in austenite grains.

Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed model.Through optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction accuracy.The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost methods.The established ML models exhibited better predic-tion performance and generalization ability via property transformation descriptors.In addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion rate.The results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models.

A review on the multi-scaled structures and mechanical/thermal properties of tool steels fabricated by laser powder bed fusion additive manufacturing

The laser powder bed fusion(LPBF) process can integrally form geometrically complex and high-performance metallic parts that have attracted much interest,especially in the molds industry.The appearance of the LPBF makes it possible to design and produce complex conformal cooling channel systems in molds.Thus,LPBF-processed tool steels have attracted more and more attention.The complex thermal history in the LPBF process makes the microstructural characteristics and properties different from those of conventional manufactured tool steels.This paper provides an overview of LPBF-processed tool steels by describing the physical phenomena,the microstructural characteristics,and the mechanical/thermal properties,including tensile properties,wear resistance,and thermal properties.The microstructural characteristics are presented through a multiscale perspective,ranging from densification,meso-structure,microstructure,substructure in grains,to nanoprecipitates.Finally,a summary of tool steels and their challenges and outlooks are introduced.

A Physical Core-Loss Model for Laminated Magnetic Sheet Steels

A full-frequency instant core-loss equation built from the induction physical model of magnetic materials, where the iron loss, eddy loss, and hysteresis loss no longer have an integral term, and this new equation provides high simulation accuracy and performs dynamic core loss analysis on non-sinusoidal or pulse magnetic fields. The simulation examples use a high-grade electrical steel sheet 65CS400 by Epstein experimental data covering magnetic field 0.1 - 1.8 T and frequency 50 - 5000 Hz, and the average error of the simulated core loss is less than 4%. Since the simulation is converged by magnetic physical parameters, so the physical relevance of the similar laminated materials can be compared with the coefficient results. .

Characteristics and Weldability of Resistance Plug Welding of Dissimilar Steels

TRIP980 high-strength steel plate/SPCC low-carbon steel plate were welded by RPW. The key factors such as size and material of filler were studied, and the structure, fusion ratio and mechanical properties of the RPW joint were analyzed. The experimental results show that the calculation formulas of the length and diameter of the filler were designed reasonably. Q235 as a filler for RPW of TRIP980 high-strength steel plate/SPCC low-carbon steel plate is suitable according to schaeffler organization chart. The deposited metal of RPW joint is in the shape of “spool”,and the base metal and cap of deposited metal are alternately combined. The deposited metal has the characteristics of “locking” as rivets, which is beneficial to the improvement of mechanical properties of RPW joint. The nugget of RPW joint is uniform without deviates. TRIP980 high-strength steel plate, SPCC low-carbon steel plate, and filler were metallurgically bonded in the RPW joint.

Advances in machine learning-and artificial intelligence-assisted material design of steels

With the rapid development of artificial intelligence technology and increasing material data,machine learning-and artificial intelligence-assisted design of high-performance steel materials is becoming a mainstream paradigm in materials science.Machine learning methods,based on an interdisciplinary discipline between computer science,statistics and material science,are good at discovering correlations between numerous data points.Compared with the traditional physical modeling method in material science,the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel materials.This review starts with data preprocessing and the introduction of different machine learning models,including algorithm selection and model evaluation.Then,some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition,structure,processing,and performance.The application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also reviewed.Finally,the applicability and limitations of machine learning in the material field are summarized,and future directions and prospects are discussed.

Comparative research on the formability of several ultra-high-strength steels

Three kinds of ultra-high-strength steels are subjected to uniaxial tensile,forming limit,and hole expansion tests to characterize their material forming properties.Results show that the elongation of S1500 reaches 12.9%and is higher than that of MS1500 with the same strength grade but is lower than that of QP980.The forming limit of S1500 steel is higher than that of MS1500 but lower than that of QP980.The instantaneous n-value of the material changes with the volume fraction of retained austenite.The hole expansion ratios of S1500,MS1500,and QP980 steels are 31.3%,32.2%,and 28.3%,respectively.The hole expansion ratio of QP steel increases slightly with the increase in strength grade.This behavior is contrary to the change trend of elongation and forming limit.Among the three kinds of materials,QP980 steel has the best global formability,and S1500 steel has better global formability than martensitic steel with a similar strength grade.The local formability of the materials improves slightly with the decrease in the amount of retained austenite.MS1500 may have the best local formability in accordance with engineering practice.

Effect of Chromium-enhanced Diffusivity on Reverted Transformation in Metastable Austenitic Stainless Steels:Theoretical Calculation and Experiment

Effects of nucleation sites and diffusivity enhancement of chromium on reverted transformation of AISI 304 stainless steel during annealing process were investigated.Dynamics calculation revealed that the reverted transformation of strain-inducedα’-martensite→γaustenite could were closely associated with active nucleation sites and diffusivity enhancement of chromium in nanocrystallineα’-martensite.The experimental data and the results were in accordance with 2-grain austenite/α’-martensite junctions calculated theoretically,which could result from high chromium diffusion rate in nanocrystallineα’-martensite.In addition,low temperature is not conducive to reversed transformation,while high temperature and long annealing time will lead to inhomogeneous grain size distribution.

Literature Review of Phase Transformations and Cavitation Erosion of Duplex Stainless Steels

Phase transformation is one of the factors that would significantly influence the ability to resist cavitation erosion of stainless steels. Due to the specific properties of duplex stainless steel, the heat treatment would bring about significant phase transformations. In this paper, we have examined the previous studies on the phase transition of stainless steel, including the literature on the classification of stainless steel, spinodal decomposition, sigma phase transformation, and cavitation erosion of double stainless steel. Through these literature investigations, the destruction of cavitation erosion on duplex stainless steel can be clearly known, and the causes of failure of duplex stainless steel in seawater can be clarified, thus providing a theoretical basis for subsequent scientific research. And the review is about to help assess the possibility of using bulk heat treatment to improve the cavitation erosion (CE) behaviour of the duplex stainless steel 7MoPLUS.

Energy absorption characteristics of novel high-strength and hightoughness steels used for rock support

Nowadays,the development of novel metallic materials for rock support have attracted research interests since they can significantly improve the deformation and energy absorption capacities of rock bolts.Although previous studies proved the importance and mechanical advantages of utilizing high-strength and high-toughness(HSHT)steels in rock support,there is no systematic analysis to reveal the essential energy absorption parameter and the guidelines for further development of metallic rock support materials.This paper analyzes the energy absorption characteristics of novel HSHT steels(negative Poisson’s ratio(NPR)and twinning-induced plasticity(TWIP)steels)in comparison with conventional rock support materials.A physically based crystal plasticity(CP)model was set up and calibrated to study the effect of strain hardening rate(SHR).Meanwhile,the roles of underlying physical mechanisms,i.e.the dislocation density and twin volume fraction,were studied.The results show that the improvement of energy absorption density(EAD)is essential for further development of rock support materials,besides the increase of energy absorption rate(EAR)for previous development of conventional rock support materials.The increase of EAD requires increases of both strength and deformation capacity of materials.For HSHT steels,the decrease of SHR has a positive effect on the improvement of EAD.In addition,the increase of EAD is followed by the increase of twin volume fraction and the decrease of plastic Poisson’s ratio which can promote deformation plasticity of materials.Meanwhile,the increase of EAR is correlated with the accumulation of dislocation density,which can increase the strength of materials.This paper provides the theoretical basis and guidelines for developing rock support materials in deep underground engineering and other related fields.

钢铁海水电偶腐蚀的研究进展

对海洋工程装备中常用钢铁材料的电偶腐蚀研究进行了综述,分析了海水中钢铁电偶腐蚀的影响因素,着重探讨了海水中钢与异种金属和非金属材料连接时电偶腐蚀的研究现状,并对该领域的研究前景进行了展望。

改进YOLOv7算法的钢材表面缺陷检测研究

当前,基于深度学习的智能检测技术逐步应用于钢材表面缺陷检测领域,针对钢材表面缺陷检测精度低的问题,提出一种高精度实时的缺陷检测算法CDN-YOLOv7。加入CARAFE轻量化上采样算子来改善网络特征融合能力,融合级联注意力机制和解耦头重新设计YOLOv7检测头网络,旨在解决原始头网络特征利用效率不高的问题,使其充分利用各尺度、通道、空间的多维度信息,提升复杂场景下模型表征能力。引入归一化Wasserstein距离重新设计Focal-EIoU损失函数,提出NF-EIoU替换CIoU损失,平衡各尺度缺陷样本对Loss的贡献,降低各尺度缺陷的漏检率。实验结果表明,CDN-YOLOv7的检测精度可达80.3%,较于原YOLOv7精度提升了6.0个百分点,模型推理速度可达60.8帧/s,满足实时性需求,CDN-YOLOv7在提升各尺度缺陷检测精度的同时显著降低了缺陷的漏检率。

滚动轴承钢碳氮共渗工艺研究

以GCr15和GCr15SiMn轴承钢为研究对象,进行不同工艺的碳氮共渗热处理实验研究。利用光学显微镜观察试样热处理后的显微组织、晶粒度,利用X-射线衍射仪分析热处理后残余奥氏体的含量,利用硬度仪测量试样热处理后的硬度变化规律,研究共渗温度对渗层组织和性能的影响。研究结果表明:考虑碳氮共渗层深度、显微组织、晶粒度以及硬度综合因素,碳氮共渗温度控制在820℃,碳势控制在1.18%,进行淬火和低温回火的组织和性能较好。该工艺下增加冷处理,GCr15SiMn试样表面硬度从64.5 HRC提高至66.1 HRC,表面残余奥氏体量从22.1%下降至12.5%。冷处理有效降低了表面残余奥氏体含量。

通气孔对新型不锈钢芯管平压性能影响分析

不锈钢芯管作为不锈钢桥面芯板的重要传力构件,其平压力学性能与桥面板承载能力息息相关。因钎焊工艺要求,不锈钢芯管壁上开设有双通气圆孔。为研究双通气圆孔对不锈钢芯管平压性能的影响,开展2组不同厚度不锈钢芯管平压性能试验与有限元非线性屈曲分析。通过试验结果验证采用Riks弧长法进行不锈钢芯管非线性屈曲分析方法的正确性。并考虑圆孔直径d、偏心距e、芯管壁厚t等因素,建立不同规格不锈钢芯管模型,开展非线性屈曲分析,探究多参数协同作用下对不锈钢芯管平压性能的影响分析。研究结果表明:不锈钢芯管平压试验曲线表现出明显的非线性特征,分为弹性阶段、弹塑性阶段与塑性破坏阶段;通气孔、管端扳边处转角及管脚10 mm处易优先出现局部屈曲破坏;不锈钢芯管极限荷载Pu及其对应的压缩位移δ_(u)随孔径d、偏心距e的增加而减小,随壁厚t增加而增大;芯管极限荷载Pu受孔径d的影响大于受偏心距e的影响,增大孔径使芯管极限荷载Pu下降6.00%~15.51%,而增大偏心距使芯管极限荷载Pu下降0.64%~9.22%,芯管应规避在管身中部(e/H=0.5)开孔;当孔径比d/D≯0.04、偏心比e/H≯0.3时,通气孔对芯管极限承载力影响较小,对极限荷载下的压缩位移δ_(u)不产生影响,建议将其作为通气孔设计取值。研究中获得的临界值及变化规律可用于新型不锈钢桥面芯板结构设计与优化研究。

预制装配式型钢混凝土梁抗剪承载力的智能模型研究

通过建立计算预制装配式型钢混凝土(PSRC)梁抗剪承载力的智能模型,在一定程度上提高了计算精度与适用性。基于BP人工神经网络算法,通过对影响PSRC梁抗剪承载力的相关参数进行梳理,选取14个主要影响参数作为输入层,以试算法确定隐含层节点数为5,初步构建了3层结构人工神经网络系统;以收集的76组试验数据作为学习样本,对构建的神经网络系统进行训练,建立了对PSRC梁及SRC梁抗剪承载力计算的N14-5-1智能模型。使用智能模型对6个PSRC梁构件及6个SRC梁构件进行抗剪承载力计算,并通过与规范公式计算结果、试验结果的对比分析,证明了智能模型具有良好的计算精度及较好的泛化能力,具有一定的工程参考意义。运用Garson算法对输入参数进行敏感性分析,结果表明箍筋间距、型钢屈服强度、箍筋屈服强度、型钢腹板含钢率对抗剪承载力影响较大。随着研究试验的开展,在收集更多具有代表性的试验数据以扩充学习样本后,可对智能模型进一步优化。

钝化膜对304不锈钢裂纹尖端应力腐蚀的影响

建立了一个包含物质扩散与固体力学的二维数值模型,模型中假设氧在金属中以菲克定律扩散形成Fe_(3)O_(4)保护膜,并采用吸湿膨胀的方法计算膜致应力,该模型可以模拟裂纹尖端钝化膜的形成与膜致应力。基于该模型,在COMSOL中建立了含单边裂纹的平板试样的有限元模型,研究了在外加位移载荷的作用下,钝化对裂尖应力、等效塑性应变以及裂纹扩展的影响,并研究了钝化膜再破裂时的应力分布。结果表明,钝化膜在一定程度上减弱了外加载荷对裂纹尖端的应力腐蚀,降低了裂纹扩展的可能性。在裂纹开始扩展初期,钝化膜减弱了裂尖的应力,当裂纹扩展较长时,钝化膜对裂纹尖端基本没有影响。

南京仙新路长江大桥主桥结构设计

南京仙新路长江大桥主桥为跨径1760 m的单跨钢箱梁悬索桥,主缆垂跨比1/9,边跨跨径580 m,边中跨比0.33。该桥上、下游各设1根主缆,单根主缆由169股127∅5.4 mm镀锌铝高强钢丝索股组成,采用PPWS法施工,钢丝标准抗拉强度2100 MPa。吊索与索夹采用销接式结构,跨中设置柔性中央扣索,短吊索设置关节轴承。主索鞍采用宽鞍槽单纵肋铸焊结合构造,散索鞍采用底座式全铸结构。加劲梁采用扁平流线型封闭整体钢箱梁,总宽31.5 m,梁高4 m,顶板与U肋之间采用双面埋弧全熔透焊接。桥塔采用门形混凝土结构,总高277.3 m,其上横梁为预应力混凝土结构,外包N字造型钢结构;桥塔基础采用直径2.8 m钻孔灌注桩。南锚碇采用外径65 m圆形地下连续墙基础;北锚碇采用沉井基础,平面尺寸70 m×50 m,高50 m。对结构进行静力分析及抗风性能理论和试验研究,结果表明:结构强度、刚度均满足规范要求;在加劲梁上设置0.67 m高中央稳定板、两侧风嘴处设置1 m宽水平稳定板后,大桥的颤振、涡振等抗风性能均满足要求,且具备一定的阻尼储备。

钢铁工业含锌尘泥回转窑处置技术的应用进展

钢铁冶炼过程(炼铁、炼钢)会产生大量的含锌尘泥,该尘泥不宜直接返回炼铁工序,一般以厂内长期堆存或外委等方式进行处理。随着我国钢铁企业“固废不出厂”“循环经济”“绿色制造”等新理念的深入推进,在钢铁厂内部采用新型回转窑技术直接处置含锌尘泥成为大势所趋。本文介绍了传统回转窑处置含锌尘泥技术的工艺流程,从原料、生产过程和产品特点等方面分析其不足,提出采用原料造球、多喷孔分级进风等创新技术的新型回转窑处置技术,该技术明显提高了回转窑处置能力,缓解了回转窑结圈,已相继在永锋钢铁、八一钢铁、诚钰环保等钢铁企业和环保公司稳定运行,产生了良好的经济与社会效益。该技术在多固废协同处置、热渣干式冷却等方面具备应用前景。

不同镀层板的电阻点焊腐蚀行为

[目的]研究点焊对不同镀层板腐蚀行为的影响。[方法]对镀锌板和镀锌铝镁板进行电阻点焊,采用扫描电镜和能谱仪分析了点焊部位的微观形貌和元素分布。通过中性盐雾试验和电化学阻抗谱测试研究了两种板材点焊前后在相同环境下的腐蚀行为。[结果]点焊镀锌板的焊点边缘存在大量凸起和凹坑,而点焊镀锌铝镁板的焊点边缘无明显的凸起和凹坑。二者的焊点位置Zn元素挥发明显,均有部分基体裸露。无论点焊与否,镀锌板在中性盐雾试验中的耐腐蚀能力都不如镀锌铝镁板。[结论]电阻点焊容易导致镀层局部破损,影响镀层的耐蚀性。

基于BIM技术与模拟退火算法的村镇轻钢框架结构智能设计方法

传统村镇住宅结构设计需要进行大量的人工试算与重复建模,而受制于建设成本,村镇住宅无法像城镇住宅一样通过设计师进行专业的结构设计与验算,其安全性与经济性均难以满足要求。为此,提出一种村镇轻钢框架结构智能设计方法,包括智能建模与智能优化两个环节。基于图层自动识别算法、光学字符识别技术、自适应分块算法提出村镇轻钢框架结构BIM智能建模方法,包括图层识别、轴文本数据提取、墙体轮廓提取等,智能建模结果基本满足实际工程要求。基于提出的两阶段模拟退火算法给出村镇轻钢框架结构的智能优化方法,优化速度较快,优化效果良好。通过实际工程案例对提出的智能设计方法进行验证,结果表明,提出的村镇轻钢框架结构智能设计方法具有可行性,与传统的人工设计方法相比,设计周期可缩短70%以上,材料用量、结构设计指标接近人工设计结果。