DYNAMIC STABILITY OF SPINDLE BLADE IN RING SPINNING Zhou Bingrong(Department of Mechanical Engineering)

It is discussed that a tangential force T induces a self-excited vibration in the motion ofspindle blades of a ring spinning frame.Depending on the relative magnitude of the tangentialforce compared with the tangential damping force the motion of blade is either stable orunstable.The chief factors causing the self-excited vibration can also be traced from the charac-ter of the experimental locus.

Thermal and mechanical behavior of casting copper alloy wheel during wheel and belt continuous casting process

To investigate the thermal and mechanical behavior of casting wheel,a two-dimensional thermoelastic-plastic finite element model was used to predict the temperature,stress and distortion distribution of the casting wheel during the wheel and belt continuous casting process.The effects of grinding thickness and casting speed on the thermal and mechanical behaviors of the center of the hot face of the casting wheel were discussed in detail.In each rotation,the casting wheel passes through four different spray zones.The results show that the temperature distribution of the casting wheel in different spray zones is similar,the temperature of the hot face is the highest and the temperature reaches the peak in the spray zoneⅢ.The stress and distortion depend on the temperature distribution,and the maximum stress and distortion of the hot face are 358.2 MPa and 1.82 mm,respectively.The temperature at the center of the hot face decreases with increasing grinding thickness and increases with increasing casting speed.

Spark Plasma Sintering of Mg-based Alloys:Microstructure,Mechanical Properties,Corrosion Behavior,and Tribological Performance

Within the past ten years,spark plasma sintering(SPS)has become an increasingly popular process for Mg manufacturing.In the SPS process,interparticle diffusion of compressed particles is rapidly achieved due to the concept of Joule heating.Compared to traditional and additive manufacturing(AM)techniques,SPS gives unique control of the structural and microstructural features of Mg components.By doing so,their mechanical,tribological,and corrosion properties can be tailored.Although great advancements in this field have been made,these pieces of knowledge are scattered and have not been contextualized into a single work.The motivation of this work is to address this scientific gap and to provide a groundwork for understanding the basics of SPS manufacturing for Mg.To do so,the existing body of SPS Mg literature was first surveyed,with a focus on their structural formation and degradation mechanisms.It was found that successful Mg SPS fabrication highly depended on the processing temperature,particle size,and particle crystallinity.The addition of metal and ceramic composites also affected their microstructural features due to the Zener pinning effect.In degradative environments,their performance depends on their structural features and whether they have secondary phased composites.In industrial applications,SPS'd Mg was found to have great potential in biomedical,hydrogen storage,battery,automotive,and recycling sectors.The prospects to advance the field include using Mg as a doping agent for crystallite size refinement and using bulk metallic Mg-based glass powders for amorphous SPS components.Despite these findings,the interactions of multi-composites on the processing-structure-property relationships of SPS Mg is not well understood.In total,this work will provide a useful direction in the SPS field and serve as a milestone for future Mg-based SPS manufacturing.

Predicting the Mechanical Behavior of a Bioinspired Nanocomposite through Machine Learning

The bioinspired nacre or bone structure represents a remarkable example of tough,strong,lightweight,and multifunctional structures in biological materials that can be an inspiration to design bioinspired high-performance materials.The bioinspired structure consists of hard grains and soft material interfaces.While the material interface has a very low volume percentage,its property has the ability to determine the bulk material response.Machine learning technology nowadays is widely used in material science.A machine learning model was utilized to predict the material response based on the material interface properties in a bioinspired nanocomposite.This model was trained on a comprehensive dataset of material response and interface properties,allowing it to make accurate predictions.The results of this study demonstrate the efficiency and high accuracy of the machine learning model.The successful application of machine learning into the material property prediction process has the potential to greatly enhance both the efficiency and accuracy of the material design process.

Mechanical property of cylindrical sandwich shell with gradient core of entangled wire mesh

To explore the wide-frequency damping and vibration-attenuation performances in the application of aerospace components,the cylindrical sandwich shell structure with a gradient core of entangled wire mesh was proposed in this paper.Firstly,the gradient cores of entangled wire mesh in the axial and radial directions were prepared by using an in-house Numerical Control weaving machine,and the metallurgical connection between skin sheets and the gradient core was performed using vacuum brazing.Secondly,to investigate the mechanical properties of cylindrical sandwich shells with axial or radial gradient cores,quasi-static and dynamic mechanical experiments were carried out.The primary evaluations of mechanical properties include secant stiffness,natural frequency,Specific Energy Absorption(SEA),vibration acceleration level,and so on.The results suggest that the vibration-attenuation performance of the sandwich shell is remarkable when the high-density core layer is at the end of the shell or abuts the inner skin.The axial gradient material has almost no influence on the vibration frequencies of the shell,whereas the vibration frequencies increase dramatically when the high-density core layer approaches the skin.Moreover,compared to the conventional sandwich shells,the proposed functional grading cylindrical sandwich shell exhibits more potential in mass reduction,stiffness designing,and energy dissipation.

Experimental,Numerical,and Analytical Studies on the Bending of Mechanically Lined Pipe

Mechanically lined pipe(MLP)is often used for offshore oil and gas transport because of its low cost and corrosion resistance.During installation and operation,the pipe may undergo severe bending deformation,which causes the liner to separate from the outer pipe and buckles,affecting the stability of the whole line.In this paper,the buckling response of MLP subjected to bending is investigated to clarify its bending characteristics by employing both experiments,numerical simulation,as theoretical methods.Two types of MLPs were manufactured with GB 45 carbon steel(SLP)and Al 6061(ALP)used as the outer pipe material,respectively.The hydraulic expansion and bending experiments of small-scale MLPs are conducted.In addition to the ovalized shape of the cross-section for the SLP specimens,the copper liner was found to wrinkle on the compressive side.In contrast,the liner of ALP remains intact without developing any wrinkling and collapse mode.In addition,a dedicated numerical framework and theoretical models were also established.It was found both the manufacturing and bending responses of the MLP can be well reproduced,and the predicted maximum moment and critical curvatures are in good agreement with the experimental results.

Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.

Reinforcement learning based adaptive control for uncertain mechanical systems with asymptotic tracking

This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and satellites.All these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become unavailable.Motivated by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical dynamics.The approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control approach.Different from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control signal.The developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output tracking.Eventually,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.

Improving creep strength of the fine-grained heat-affected zone of novel 9Cr martensitic heat-resistant steel via modified thermo-mechanical treatment

The infamous type Ⅳ failure within the fine-grained heat-affected zone (FGHAZ) in G115 steel weldments seriously threatens the safe operation of ultra-supercritical (USC) power plants.In this work,the traditional thermo-mechanical treatment was modified via the replacement of hot-rolling with cold rolling,i.e.,normalizing,cold rolling,and tempering (NCT),which was developed to improve the creep strength of the FGHAZ in G115 steel weldments.The NCT treatment effectively promoted the dissolution of preformed M_(23)C_(6)particles and relieved the boundary segregation of C and Cr during welding thermal cycling,which accelerated the dispersed reprecipitation of M_(23)C_(6) particles within the fresh reaustenitized grains during post-weld heat treatment.In addition,the precipitation of Cu-rich phases and MX particles was promoted evidently due to the deformation-induced dislocations.As a result,the interacting actions between precipitates,dislocations,and boundaries during creep were reinforced considerably.Following this strategy,the creep rupture life of the FGHAZ in G115 steel weldments can be prolonged by 18.6%,which can further push the application of G115 steel in USC power plants.

Mechanical Behavior and Microstructure Evolution during Tensile Deformation of Twinning Induced Plasticity Steel Processed by Warm Forgings

The mechanical behavior and microstructural evolution of an Fe-30Mn-3Al-3Si twinninginduced plasticity(TWIP)steel processed using warm forging was investigated.It is found that steel processed via warm forging improves comprehensive mechanical properties compared to the TWIP steel processed via cold rolling,with a high tensile strength(R_(m))of 793 MPa,a yield strength(R_(P))of 682 MPa,an extremely large R_(P)/R_(m)ratio as high as 0.86 as well as an excellent elongation rate of 46.8%.The microstructure observation demonstrates that steel processed by warm forging consists of large and elongated grains together with fine,equiaxed grains.Complicated micro-defect configurations were also observed within the steel,including dense dislocation networks and a few coarse deformation twins.As the plastic deformation proceeds,the densities of dislocations and deformation twins significantly increase.Moreover,a great number of slip lines could be observed in the elongated grains.These findings reveal that a much more dramatic interaction between microstructural defect and dislocations glide takes place in the forging sample,wherein the fine and equiaxed grains propagated dislocations more rapidly,together with initial defect configurations,are responsible for enhanced strength properties.Meanwhile,larger,elongated grains with more prevalently activated deformation twins result in high plasticity.

Mechanical responses of anchoring structure under triaxial cyclic loading

Dynamic load on anchoring structures(AS)within deep roadways can result in cumulative damage and failure.This study develops an experimental device designed to test AS under triaxial loads.The device enables the investigation of the mechanical response,failure mode,instability assessment criteria,and anchorage effect of AS subjected to combined cyclic dynamic-static triaxial stress paths.The results show that the peak bearing strength is positively correlated with the anchoring matrix strength,anchorage length,and edgewise compressive strength.The bearing capacity decreases significantly when the anchorage direction is severely inclined.The free face failure modes are typically transverse cracking,concave fracturing,V-shaped slipping and detachment,and spallation detachment.Besides,when the anchoring matrix strength and the anchorage length decrease while the edgewise compressive strength,loading rate,and anchorage inclination angle increase,the failure intensity rises.Instability is determined by a negative tangent modulus of the displacement-strength curve or the continued deformation increase against the general downward trend.Under cyclic loads,the driving force that breaks the rock mass along the normal vector and the rigidity of the AS are the two factors that determine roadway stability.Finally,a control measure for surrounding rock stability is proposed to reduce the internal driving force via a pressure relief method and improve the rigidity of the AS by full-length anchorage and grouting modification.

Corrigendum to“Toward stable and highly reversible zinc anodes for aqueous batteries via electrolyte engineering”[J.Energy Chem.83(2023)209–228]

It is regretful that the Acknowledgments part was lost in the final process of publication.The Acknowledgments part should be added as follow.The work described in this paper was supported by the grants from the Research Grants Council of the Hong Kong Special Administrative Region,China(Project No.16205721).

Data-driven modeling on anisotropic mechanical behavior of brain tissue with internal pressure

Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function.Besides,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical properties.Whether white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical properties.As such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional dependence.Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging.Instead,this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue.We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach.The matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths.The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures.Finally,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces.Compared with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling efficiency.The predictions made by our trained model demonstrate sufficient accuracy.Specifically,we find that the level of internal blood pressure can greatly influence stress distribution and determine the possible related damage behaviors.

Microscopic defects formation and dynamic mechanical response analysis of Q345 steel plate subjected to explosive load

As the basic protective element, steel plate had attracted world-wide attention because of frequent threats of explosive loads. This paper reports the relationships between microscopic defects of Q345 steel plate under the explosive load and its macroscopic dynamics simulation. Firstly, the defect characteristics of the steel plate were investigated by stereoscopic microscope(SM) and scanning electron microscope(SEM). At the macroscopic level, the defect was the formation of cave which was concentrated in the range of 0-3.0 cm from the explosion center, while at the microscopic level, the cavity and void formation were the typical damage characteristics. It also explains that the difference in defect morphology at different positions was the combining results of high temperature and high pressure. Secondly, the variation rules of mechanical properties of steel plate under explosive load were studied. The Arbitrary Lagrange-Euler(ALE) algorithm and multi-material fluid-structure coupling method were used to simulate the explosion process of steel plate. The accuracy of the method was verified by comparing the deformation of the simulation results with the experimental results, the pressure and stress at different positions on the surface of the steel plate were obtained. The simulation results indicated that the critical pressure causing the plate defects may be approximately 2.01 GPa. On this basis, it was found that the variation rules of surface pressure and microscopic defect area of the Q345 steel plate were strikingly similar, and the corresponding mathematical relationship between them was established. Compared with Monomolecular growth fitting models(MGFM) and Logistic fitting models(LFM), the relationship can be better expressed by cubic polynomial fitting model(CPFM). This paper illustrated that the explosive defect characteristics of metal plate at the microscopic level can be explored by analyzing its macroscopic dynamic mechanical response.

Analysis of thermal management and anti-mechanical abuse of multi-functional battery modules based on magneto-sensitive shear thickening fluid

Electric vehicles(EVs)have garnered significant attention as a vital driver of economic growth and environmental sustainability.Nevertheless,ensuring the safety of high-energy batteries is now a top priority that cannot be overlooked during large-scale applications.This paper proposes an innovative active protection and cooling integrated battery module using smart materials,magneto-sensitive shear thickening fluid(MSTF),which is specifically designed to address safety threats posed by lithium-ion batteries(LIBs)exposed to harsh mechanical and environmental conditions.The theoretical framework introduces a novel approach for harnessing the smoothed-particle hydrodynamics(SPH)methodology that incorporates the intricate interplay of non-Newtonian fluid behavior,capturing the fluid-structure coupling inherent to the MSTF.This approach is further advanced by adopting an enhanced Herschel-Bulkley(H-B)model to encapsulate the intricate rheology of the MSTF under the influence of the magnetorheological effect(MRE)and shear thickening(ST)behavior.Numerical simulation results show that in the case of cooling,the MSTF is an effective cooling medium for rapidly reducing the temperature.In terms of mechanical abuse,the MSTF solidifies through actively applying the magnetic field during mechanical compression and impact within the battery module,resulting in 66%and 61.7%reductions in the maximum stress within the battery jellyroll,and 31.1%and 23%reductions in the reaction force,respectively.This mechanism effectively lowers the risk of short-circuit failure.The groundbreaking concepts unveiled in this paper for active protection battery modules are anticipated to be a valuable technological breakthrough in the areas of EV safety and lightweight/integrated design.

Mechanical behavior and response mechanism of porous metal structures manufactured by laser powder bed fusion under compressive loading

Al Si10Mg porous protective structure often produces different damage forms under compressive loading,and these damage modes affect its protective function.In order to well meet the service requirements,there is an urgent need to comprehensively understand the mechanical behavior and response mechanism of AlSi10Mg porous structures under compressive loading.In this paper,Al Si10Mg porous structures with three kinds of volume fractions are designed and optimized to meet the requirements of high-impact,strong-energy absorption,and lightweight characteristics.The mechanical behaviors of AlSi10Mg porous structures,including the stress-strain relationship,structural bearing state,deformation and damage modes,and energy absorption characteristics,were obtained through experimental studies at different loading rates.The damage pattern of the damage section indicates that AlSi10Mg porous structures have both ductile and brittle mechanical properties.Numerical simulation studies show that the AlSi10Mg porous structure undergoes shear damage due to relative misalignment along the diagonal cross-section,and the damage location is almost at 45°to the load direction,which is the most direct cause of its structural damage,revealing the damage mechanism of AlSi10Mg porous structures under the compressive load.The normalized energy absorption model constructed in the paper well interprets the energy absorption state of Al Si10Mg porous structures and gives the sensitive location of the structures,and the results of this paper provide important references for peers in structural design and optimization.

Influence of layer thickness on formation quality,microstructure,mechanical properties,and corrosion resistance of WE43 magnesium alloy fabricated by laser powder bed fusion

Laser powder bed fusion(L-PBF)of Mg alloys has provided tremendous opportunities for customized production of aeronautical and medical parts.Layer thickness(LT)is of great significance to the L-PBF process but has not been studied for Mg alloys.In this study,WE43 Mg alloy bulk cubes,porous scaffolds,and thin walls with layer thicknesses of 10,20,30,and 40μm were fabricated.The required laser energy input increased with increasing layer thickness and was different for the bulk cubes and porous scaffolds.Porosity tended to occur at the connection joints in porous scaffolds for LT40 and could be eliminated by reducing the laser energy input.For thin wall parts,a large overhang angle or a small wall thickness resulted in porosity when a large layer thicknesses was used,and the porosity disappeared by reducing the layer thickness or laser energy input.A deeper keyhole penetration was found in all occasions with porosity,explaining the influence of layer thickness,geometrical structure,and laser energy input on the porosity.All the samples achieved a high fusion quality with a relative density of over 99.5%using the optimized laser energy input.The increased layer thickness resulted to more precipitation phases,finer grain sizes and decreased grain texture.With the similar high fusion quality,the tensile strength and elongation of bulk samples were significantly improved from 257 MPa and 1.41%with the 10μm layer to 287 MPa and 15.12%with the 40μm layer,in accordance with the microstructural change.The effect of layer thickness on the compressive properties of porous scaffolds was limited.However,the corrosion rate of bulk samples accelerated with increasing the layer thickness,mainly attributed to the increased number of precipitation phases.

From VIB‑to VB‑Group Transition Metal Disulfides:Structure Engineering Modulation for Superior Electromagnetic Wave Absorption

The laminated transition metal disulfides(TMDs),which are well known as typical two-dimensional(2D)semiconductive materials,possess a unique layered structure,leading to their wide-spread applications in various fields,such as catalysis,energy storage,sensing,etc.In recent years,a lot of research work on TMDs based functional materials in the fields of electromagnetic wave absorption(EMA)has been carried out.Therefore,it is of great significance to elaborate the influence of TMDs on EMA in time to speed up the application.In this review,recent advances in the development of electromagnetic wave(EMW)absorbers based on TMDs,ranging from the VIB group to the VB group are summarized.Their compositions,microstructures,electronic properties,and synthesis methods are presented in detail.Particularly,the modulation of structure engineering from the aspects of heterostructures,defects,morphologies and phases are systematically summarized,focusing on optimizing impedance matching and increasing dielectric and magnetic losses in the EMA materials with tunable EMW absorption performance.Milestones as well as the challenges are also identified to guide the design of new TMDs based dielectric EMA materials with high performance.

Progress in Mechanical Modeling of Implantable Flexible Neural Probes

Implanted neural probes can detect weak discharges of neurons in the brain by piercing soft brain tissue,thus as important tools for brain science research,as well as diagnosis and treatment of brain diseases.However,the rigid neural probes,such as Utah arrays,Michigan probes,and metal microfilament electrodes,are mechanically unmatched with brain tissue and are prone to rejection and glial scarring after implantation,which leads to a significant degradation in the signal quality with the implantation time.In recent years,flexible neural electrodes are rapidly developed with less damage to biological tissues,excellent biocompatibility,and mechanical compliance to alleviate scarring.Among them,the mechanical modeling is important for the optimization of the structure and the implantation process.In this review,the theoretical calculation of the flexible neural probes is firstly summarized with the processes of buckling,insertion,and relative interaction with soft brain tissue for flexible probes from outside to inside.Then,the corresponding mechanical simulation methods are organized considering multiple impact factors to realize minimally invasive implantation.Finally,the technical difficulties and future trends of mechanical modeling are discussed for the next-generation flexible neural probes,which is critical to realize low-invasiveness and long-term coexistence in vivo.