Review on the design of high-strength and hydrogen-embrittlement-resistant steels

Given the carbon peak and carbon neutrality era,there is an urgent need to develop high-strength steel with remarkable hydrogen embrittlement resistance.This is crucial in enhancing toughness and ensuring the utilization of hydrogen in emerging iron and steel materials.Simultaneously,the pursuit of enhanced metallic materials presents a cross-disciplinary scientific and engineering challenge.Developing high-strength,toughened steel with both enhanced strength and hydrogen embrittlement(HE)resistance holds significant theoretical and practical implications.This ensures secure hydrogen utilization and further carbon neutrality objectives within the iron and steel sector.Based on the design principles of high-strength steel HE resistance,this review provides a comprehensive overview of research on designing surface HE resistance and employing nanosized precipitates as intragranular hydrogen traps.It also proposes feasible recommendations and prospects for designing high-strength steel with enhanced HE resistance.

水田动力底盘逆向建模与质心验证——基于Geomagic Design X

针对目前农业机械仿真和优化模型精度不高的问题,提出了一种构建精确模型的逆向建模方法。应用光学扫描仪采集点云数据,基于Geomagic Design X处理点云数据并重构零部件模型,在SolidWorks中完成水田动力底盘的装配。以VP6D-CQ型水田动力底盘为例,将测得的实际质心位置与所建的模型质心位置进行比较,并对提出的逆向建模方法进行了试验验证以及误差分析。结果表明:总质量、x坐标、y坐标、z坐标的误差值分别为4.55%、3.62%、2.23%、4.81%,误差值均在5%内。此方法可构建准确的三维模型,为后续仿真优化数据精确性奠定了基础,与传统的研发相比较,可缩短农业机械研发周期、降低设计成本。

Designing new low alloyed Mg-RE alloys with high strength and ductility via high-speed extrusion

Two new low-alloyed Mg-2RE-0.8Mn-0.6Ca-0.5Zn(wt%,RE=Sm or Y)alloys are developed,which can be produced on an in-dustrial scale via relatively high-speed extrusion.These two alloys are not only comparable to commercial AZ31 alloy in extrudability,but also have superior mechanical properties,especially in terms of yield strength(YS).The excellent extrudability is related to less coarse second-phase particles and high initial melting point of the two as-cast alloys.The high strength-ductility mainly comes from the formation of fine grains,nano-spaced submicron/nano precipitates,and weak texture.Moreover,it is worth noting that the YS of the two alloys can maintain above 160 MPa at elevated temperature of 250°C,significantly higher than that of AZ31 alloy(YS:45 MPa).The Zn/Ca solute segregation at grain boundaries,the improved heat resistance of matrix due to addition of RE,and the high melting points of strengthening particles(Mn,MgZn_(2),and Mg-Zn-RE/Mg-Zn-RE-Ca)are mainly responsible for the excellent high-temperature strength.

Machine Learning Design of Aluminum-Lithium Alloys with High Strength

Due to the large unexplored compositional space,long development cycle,and high cost of traditional trial-anderror experiments,designing high strength aluminum-lithium alloys is a great challenge.This work establishes a performance-oriented machine learning design strategy for aluminum-lithium alloys to simplify and shorten the development cycle.The calculation results indicate that radial basis function(RBF)neural networks exhibit better predictive ability than back propagation(BP)neural networks.The RBF neural network predicted tensile and yield strengths with determination coefficients of 0.90 and 0.96,root mean square errors of 30.68 and 25.30,and mean absolute errors of 28.15 and 19.08,respectively.In the validation experiment,the comparison between experimental data and predicted data demonstrated the robustness of the two neural network models.The tensile and yield strengths of Al-2Li-1Cu-3Mg-0.2Zr(wt.%)alloy are 17.8 and 3.5 MPa higher than those of the Al-1Li4.5Cu-0.2Zr(wt.%)alloy,which has the best overall performance,respectively.It demonstrates the reliability of the neural network model in designing high strength aluminum-lithium alloys,which provides a way to improve research and development efficiency.

Assessment of compressive strength of jet grouting by machine learning

Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.

High-strength Ti-Al-V-Zr cast alloys designed using α and β cluster formulas

Ti-Al-V-Zr quaternary titanium alloys were designed followingα-{[Al-Ti12](AlTi2)}17-n+β-{[Al-Ti12Zr2](V3)}n,where n=1-7(the number ofβunits),on the basis of the dual-cluster formula of popular Ti-6Al-4V alloy.Such an alloying strategy aims at strengthening the alloy via Zr and V co-alloying in theβ-Ti unit,based on the originalβformula[Al-Ti14](V2Ti)of Ti-6Al-4V alloy.The microstructures of the as-cast alloys by copper-mold suction-casting change from pureα(n=1)toα+α’martensite(n=7).When n is 6,Ti-5.6Al-6.8V-8.1Zr alloy reaches the highest ultimate tensile strength of 1,293 MPa and yield strength of 1,097 MPa,at the expense of a low elongation of 2%,mainly due to the presence of a large amount of acicularα’martensite.Its specific strength far exceeds that of Ti-6Al-4V alloy by 35%.

An innovative joint interface design for reducing intermetallic compounds and improving joint strength of thick plate friction stir welded Al/Mg joints

Friction stir welding of dissimilar Al/Mg thick plates still faces severe challenges, such as poor formability, formation of thick intermetallic compounds, and low joint strength. In this work, two joint configurations, namely inclined butt(conventional butt) and serrated interlocking(innovative butt), are proposed for improving weld formation and joint quality. The results show that a continuous and straight intermetallic compound layer appears at the Mg side interface in conventional butt joint, and the maximum average thickness reaches about 60.1 μm.Additionally, the Mg side interface also partially melts, forming a eutectic structure composed of Mg solid solution and Al_(12)Mg_(17) phase.For the innovative butt joint, the Mg side interface presents the curved interlocking feature, and intermetallic compounds can be reduced to less than 10 μm. The joint strength of innovative butt joint is more than three times that of conventional butt joint. This is due to the interlocking effect and thin intermetallic compounds in the innovative joint.

Investigation of anisotropic strength criteria for layered rock mass

Layered rock mass is a type of engineering rock mass with sound mechanical anisotropy,which is generally unfavorable to the stability of underground works.To investigate the strength anisotropy of layered rock,the Mohr-Coulomb and Hoek-Brown criteria are introduced to establish the two transverse isotropic strength criteria based on Jaeger's single weak plane theory and maximum axial strain theory,and parameter determination methods.Furthermore,the sensitivity of strength parameters(K 1,K 2,and K 3)that are used to characterize the anisotropy strength of non-sliding failure involved in the strength criteria and confining pressure are investigated.The results demonstrate that strength parameters K 1 and K 2 affect the strength of layered rock samples at all bedding angles except for the bedding angle of 90°and the angle range that can cause the shear sliding failure along the bedding plane.The strength of samples at any bedding angle decreases with increasing K 1,whereas the opposite is for K 2.Except for bedding angles of 0°and 90°and the bedding angle range that can cause the shear sliding along the bedding plane,K 3 has an impact on the strength of rock samples with other bedding angles that the specimens'strength increases with increase of K 3.In addition,the strength of the rock sample increases as confining pressure rises.Furthermore,the uniaxial and triaxial tests of chlorite schist samples were carried out to verify and evaluate the strength criteria proposed in the paper.It shows that the predicted strength is in good agreement with the experimental results.To test the applicability of the strength criterion,the strength data of several types of rock in the literature are compared.Finally,a comparison is made between the fitting effects of the two strength criteria and other available criteria for layered rocks.

基于“Design Thinking”理念的职业院校创新创业人才培养模式

随着我国经济不断发展和社会进步,创新创业已经成为推动经济增长的关键驱动力,在2015年全国人民代表大会上,李克强总理将“大众创业、万众创新”明确列为我国经济增长的“双引擎”之一,彰显了创新创业对于推动社会发展的重要地位。为响应国家政策,各高校积极开展创新创业教育,将其纳入高等教育综合改革的重要议程,成为培养未来人才的重要途径。本文分析了“Design Thinking”理念下职业院校创新创业人才培养的必要性,围绕新时期专业院校创新创业人才培养的要求,提出了基于“Design Thinking”理念的职业院校创新创业人才培养模式的有效策略,为职业院校人才培养效率的强化提供了参考性意见。

Uniaxial Compressive Strength Prediction for Rock Material in Deep Mine Using Boosting-Based Machine Learning Methods and Optimization Algorithms

Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ stress.Thus,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization algorithms.For this purpose,the Lead-Zinc mine in Southwest China is considered as the case study.Rock density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the output.Subsequently,twelve hybrid predictive models are obtained.Root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation metrics.Experimental results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing dataset.The values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,respectively.The highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing dataset.The proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines.

Estimating shear strength of high-level pillars supported with cemented backfilling using the HoekeBrown strength criterion

Deep metal mines are often mined using the high-level pillars with subsequent cementation backfilling(HLSCB)mining method.At the design stage,it is therefore important to have a reasonable method for determining the shear strength of the high-level pillars(i.e.cohesion and internal friction angle)when they are supported by cemented backfilling.In this study,a formula was derived for the upper limit of the confining pressure σ3max on a high-level pillar supported by cemented backfilling in a deep metal mine.A new method of estimating the shear strength of such pillars was then proposed based on the Hoek eBrown failure criterion.Our analysis indicates that the horizontal stress σhh acting on the cemented backfill pillar can be simplified by expressing it as a constant value.A reasonable and effective value for σ3max can then be determined.The value of s3max predicted using the proposed method is generally less than 3 MPa.Within this range,the shear strength of the high-level pillar is accurately calculated using the equivalent MohreCoulomb theory.The proposed method can effectively avoid the calculation of inaccurate shear strength values for the high-level pillars when the original HoekeBrown criterion is used in the presence of large confining pressures,i.e.the situation in which the cohesion value is too large and the friction angle is too small can effectively be avoided.The proposed method is applied to a deep metal mine in China that is being excavated using the HLSCB method.The shear strength parameters of the high-level pillars obtained using the proposed method were input in the numerical simulations.The numerical results show that the recommended level heights and sizes of the high-level pillars and rooms in the mine are rational.

A simple atomization approach enables monolayer dispersion of nano graphenes in cementitious composites with excellent strength gains

Carbon nano additives(CNAs)are critical to achieving the unique properties of functionalized composites,however,controlling the dispersion of CNAs in material matrix is always a challenging task.In this study,a simple atomization approach was successfully developed to promote the dispersion efficiency of graphene nanoplatelets(GNPs)in cement composites.This atomization approach can be integrated with the direct,indirect and combined ultrasonic stirrings in a homemade automatic stirring-atomization device.Mechanical and microstructure tests were performed on hardened cement pastes blended with GNPs in different stirring and mixing approaches.Results show that the direct ultrasonic stirrings enabled more homogeneous dispersions of GNP particles with a smaller size for a longer duration.The atomized droplets with the mean size of~100μm largely mitigated GNPs’agglomerations.Monolayer GNPs were observed in the cement matrix with the strength gain by up to 54%,and the total porosity decrease by 21%in 0.3 wt%GNPs dosage.The greatly enhanced dispersion efficiency of GNPs in cement also raised the cement hydration.This work provides an effective and manpower saving technique toward dispersing CNAs in engineering materials with great industrialization prospects.

基于Cast Designer的壳体铸件铸造工艺设计及智能优化

使用Cast Designer铸造仿真分析软件,通过可铸性评估系统DFM,完成浇冒系统的辅助设计,然后使用DOE技术和基于遗传算法GA的智能优化技术,评估不同工艺设计方案与铸造过程参数对壳体铸件质量的影响,分析在不同的冒口、冷铁和浇注温度条件下得料率和缩孔量的关系。根据对DOE模拟结果的分析,发现顶冒口和侧冒口尺寸是影响得料率和缩孔量的关键因数,再利用遗传算法GA选择最佳设计方案和参数。通过帕累托曲线得到了生产壳体铸件的最优工艺方案。

Web Layout Design of Large Cavity Structures Based on Topology Optimization

Large cavity structures are widely employed in aerospace engineering, such as thin-walled cylinders, blades andwings. Enhancing performance of aerial vehicles while reducing manufacturing costs and fuel consumptionhas become a focal point for contemporary researchers. Therefore, this paper aims to investigate the topologyoptimization of large cavity structures as a means to enhance their performance, safety, and efficiency. By usingthe variable density method, lightweight design is achieved without compromising structural strength. Theoptimization model considers both concentrated and distributed loads, and utilizes techniques like sensitivityfiltering and projection to obtain a robust optimized configuration. The mechanical properties are checked bycomparing the stress distribution and displacement of the unoptimized and optimized structures under the sameload. The results confirm that the optimized structures exhibit improved mechanical properties, thus offering keyinsights for engineering lightweight, high-strength large cavity structures.

Adhesion strength of tetrahydrofuran hydrates is dictated by substrate stiffness

Understanding the hydrate adhesion is important to tackling hydrate accretion in petro-pipelines.Herein,the relationship between the Tetrahydrofuran(THF)hydrate adhesion strength(AS)and surface stiffness on elastic coatings is systemically examined by experimental shear force measurements and theoretical methods.The mechanical factor-elastic modulus of the coatings greatly dictates the hydrate AS,which is explained by the adhesion mechanics theory,beyond the usual factors such as wettability and structural roughness.Moreover,the hydrate AS increases with reducing the thickness of the elastic coatings,resulted from the decrease of the apparent surface elastic modulus.The effect of critical thickness for the elastic materials with variable elastic modulus on the hydrate AS is also revealed.This study provides deep perspectives on the regulation of the hydrate AS by the elastic modulus of elastic materials,which is of significance to design anti-hydrate surfaces for mitigation of hydrate accretion in petro-pipelines.

A Comparative Study of Metaheuristic Optimization Algorithms for Solving Real-World Engineering Design Problems

Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.

Combining Deep Learning with Knowledge Graph for Design Knowledge Acquisition in Conceptual Product Design

The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product design.This study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge graph.Specifically,the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data,and further constructs the knowledge graph to support design knowledge acquisition for conceptual product design.Moreover,the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module,and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity in the relation extraction module.Experimental comparison verified the effectiveness and accuracy of the proposed knowledge extraction model.The case study demonstrated the feasibility of the knowledge graph construction with real fragmentary porcelain data and showed the capability to provide designers with interconnected and visualised design knowledge.

Estradiol increases cortical and trabecular bone accrual and bone strength in an adolescent male-to-female mouse model of gender-affirming hormone therapy

The effects of gender-affirming hormone therapy on the skeletal integrity and fracture risk in transitioning adolescent trans girls are unknown.To address this knowledge gap,we developed a mouse model to simulate male-to-female transition in human adolescents in whom puberty is first arrested by using gonadotrophin-releasing hormone analogs with subsequent estradiol treatment.Puberty was suppressed by orchidectomy in male mice at 5 weeks of age.At 3 weeks post-surgery,male-to-female mice were treated with a high dose of estradiol(~0.85 mg)by intraperitoneal silastic implantation for 12 weeks.Controls included intact and orchidectomized males at 3 weeks post-surgery,vehicle-treated intact males,intact females and orchidectomized males at 12 weeks post-treatment.Compared to male controls,orchidectomized males exhibited decreased peak bone mass accrual and a decreased maximal force the bone could withstand prior to fracture.Estradiol treatment in orchidectomized male-to-female mice compared to mice in all control groups was associated with an increased cortical thickness in the mid-diaphysis,while the periosteal circumference increased to a level that was intermediate between intact male and female controls,resulting in increased maximal force and stiffness.In trabecular bone,estradiol treatment increased newly formed trabeculae arising from the growth plate as well as mineralizing surface/bone surface and bone formation rate,consistent with the anabolic action of estradiol on osteoblast proliferation.These data support the concept that skeletal integrity can be preserved and that long-term fractures may be prevented in trans girls treated with GnRHa and a sufficiently high dose of GAHT.Further study is needed to identify an optimal dose of estradiol that protects the bone without adverse side effects.

基于Design-Expert的电链锯锯齿结构参数优化

为提高电链锯的锯切效率,对电链锯锯齿的齿形结构参数进行研究,以单位锯切功为电链锯锯切效率的衡量指标,通过对电链锯锯切过程的仿真研究,得出不同结构锯齿的单位锯切功。以电链锯锯齿中的外形前角、侧刃楔角和顶刃楔角等结构参数作为影响因子,采用Box-Benhnken中心组合试验方法设计多因素正交试验,使用Design-expert软件进行数据分析得出最优齿形结构参数组合。研究结果表明,所选取的齿形结构参数对单位锯切功影响程度由大到小顺序依次为外形前角、侧刃楔角、顶刃楔角,并获得最优齿形结构参数组合,外形前角为10.92°、侧刃楔角为45.7°、顶刃楔角为45.41°。

Prediction models of burst strength degradation for casing with considerations of both wear and corrosion

Casing wear and casing corrosion are serious problems affecting casing integrity failure in deep and ultra-deep wells.This paper aims to predict the casing burst strength with considerations of both wear and corrosion.Firstly,the crescent wear shape is simplified into three categories according to common mathematical models.Then,based on the mechano-electrochemical(M-E)interaction,the prediction model of corrosion depth is built with worn depth as the initial condition,and the prediction models of burst strength of the worn casing and corroded casing are obtained.Secondly,the accuracy of different prediction models is validated by numerical simulation,and the main influence factors on casing strength are obtained.At last,the theoretical models are applied to an ultra-deep well in Northwest China,and the dangerous well sections caused by wear and corrosion are predicted,and the corrosion rate threshold to ensure the safety of casing is obtained.The results show that the existence of wear defects results in a stress concentration and enhanced M-E interaction on corrosion depth growth.The accuracy of different mathematical models is different:the slot ring model is most accurate for predicting corrosion depth,and the eccentric model is most accurate for predicting the burst strength of corroded casing.The burst strength of the casing will be overestimated by more than one-third if the M-E interaction is neglected,so the coupling effect of wear and corrosion should be sufficiently considered in casing integrity evaluation.