Calculation and Analysis of TVMS Considering Profile Shifts and Surface Wear Evolution Process of Spur Gear

Profile shift is a highly effective technique for optimizing the performance of spur gear transmission systems.However,tooth surface wear is inevitable during gear meshing due to inadequate lubrication and long-term operation.Both profile shift and tooth surface wear(TSW)can impact the meshing characteristics by altering the involute tooth profile.In this study,a tooth stiffness model of spur gears that incorporates profile shift,TSW,tooth deformation,tooth contact deformation,fillet-foundation deformation,and gear body structure coupling is established.This model efficiently and accurately determines the time-varying mesh stiffness(TVMS).Additionally,an improved wear depth prediction method for spur gears is developed,which takes into consideration the mutually prime teeth numbers and more accurately reflects actual gear meshing conditions.Results show that consideration of the mutual prime of teeth numbers will have a certain impact on the TSW process.Furthermore,the finite element method(FEM)is employed to accurately verify the values of TVMS and load sharing ratio(LSR)of profile-shifted gears and worn gears.This study quantitatively analyzes the effect of profile shift on the surface wear process,which suggests that gear profile shift can partially alleviate the negative effects of TSW.The contribution of this study provides valuable insights into the design and maintenance of spur gear systems.

On the Polygonal Wear Evolution of Heavy-Haul Locomotive Wheels due to Wheel/Rail Flexibility and Its Mitigation Measures

Wheel polygonal wear can immensely worsen wheel/rail interactions and vibration performances of the train and track,and ultimately,lead to the shortening of service life of railway components.At present,wheel/rail medium-or high-frequency frictional interactions are perceived as an essential reason of the high-order polygonal wear of railway wheels,which are potentially resulted by the flexible deformations of the train/track system or other external excitations.In this work,the effect of wheel/rail flexibility on polygonal wear evolution of heavy-haul locomotive wheels is explored with aid of the long-term wheel polygonal wear evolution simulations,in which different flexible modeling of the heavy-haul wheel/rail coupled system is implemented.Further,the mitigation measures for the polygonal wear of heavy-haul locomotive wheels are discussed.The results point out that the evolution of polygonal wear of heavy-haul locomotive wheels can be veritably simulated with consideration of the flexible effect of both wheelset and rails.Execution of mixed-line operation of heavy-haul trains and application of multicut wheel re-profiling can effectively reduce the development of wheel polygonal wear.This research can provide a deep-going understanding of polygonal wear evolution mechanism of heavy-haul locomotive wheels and its mitigation measures.

Research on the design method for uniform wear of shield cutters in sand-pebble strata

During shield tunneling in highly abrasive formations such as sand–pebble strata,nonuniform wear of shield cutters is inevitable due to the different cutting distances.Frequent downtimes and cutter replacements have become major obstacles to long-distance shield driving in sand–pebble strata.Based on the cutter wear characteristics in sand–pebble strata in Beijing,a design methodology for the cutterhead and cutters was established in this study to achieve uniform wear of all cutters by the principle of frictional wear.The applicability of the design method was verified through three-dimensional simulations using the engineering discrete element method.The results show that uniform wear of all cutters on the cutterhead could be achieved by installing different numbers of cutters on each trajectory radius and designing a curved spoke with a certain arch height according to the shield diameter.Under the uniform wear scheme,the cutter wear coefficient is greatly reduced,and the largest shield driving distance is increased by approximately 47%over the engineering scheme.The research results indicate that the problem of nonuniform cutter wear in shield excavation could be overcome,thereby providing guiding significance for theoretical innovation and construction of long-distance shield excavation in highly abrasive strata.

The Impact of Storyline Complexity on L2 Learners’Written Performance

Task-based Language Teaching(TBLT)research has provided ample evidence that cognitive complexity is an important aspect of task design that influences learner’s performance in terms of fluency,accuracy,and syntactic complexity.Despite the substantial number of empirical investigations into task complexity in journal articles,storyline complexity,one of the features of it,is scarcely investigated.Previous research mainly focused on the impact of storyline complexity on learners’oral performance,but the impact on learners’written performance is less investigated.Thus,this study aims at investigating the effects of narrative complexity of storyline on senior high school students’written performance,as displayed by its complexity,fluency,and accuracy.The present study has important pedagogical implications.That is,task design and assessment should make a distinction between different types of narrative tasks.For example,the task with single or dual storyline.Results on task complexity may contribute to informing the pedagogical choices made by teachers when prioritizing work with a specific linguistic dimension.

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.

Novel Batch Polishing Method of Ceramic Cutting Inserts for Reducing Tool Wear

Ceramic cutting inserts are a type of cutting tool commonly used in high-speed metal cutting applications.However,the wear of these inserts caused by friction between the workpiece and cutting inserts limits their overall effectiveness.In order to improve the tool life and reduce wear,this study introduces an emerging method called magnetic field-assisted batch polishing(MABP)for simultaneously polishing multiple ceramic cutting inserts.Several polishing experiments were conducted under different conditions,and the wear characteristics were clarified by cutting S136H steel.The results showed that after 15 min of polishing,the surface roughness at the flank face,edge,and nose of the inserts was reduced to below 2.5 nm,6.25 nm,and 45.8 nm,respectively.Furthermore,the nose radii of the inserts did not change significantly,and there were no significant changes in the weight percentage of elements before and after polishing.Additionally,the tool life of the batch polished inserts was found to be up to 1.75 times longer than that of unpolished inserts.These findings suggest that the MABP method is an effective way to mass polish ceramic cutting inserts,resulting in significantly reduced tool wear.Furthermore,this novel method offers new possibilities for polishing other tools.

Experimental study on the influences of cutter geometry and material on scraper wear during shield TBM tunnelling in abrasive sandy ground

When shield TBM tunnelling in abrasive sandy ground,the rational design of cutter parameters is critical to reduce tool wear and improve tunnelling efficiency.However,the influence mechanism of cutter parameters on scraper wear remains unclear due to the lack of a reliable test method.Geometry and material optimisation are often based on subjective experience,which is unfavourable for improving scraper geological adaptability.In the present study,the newly developed WHU-SAT soil abrasion test was used to evaluate the variation in scraper wear with cutter geometry,material and hardness.The influence mechanism of cutter parameters on scraper wear has been revealed according to the scratch characteristics of the scraper surface.Cutter geometry and material parameters have been optimised to reduce scraper wear.The results indicate that the variation in scraper wear with cutter geometry is related to the cutting resistance,frictional resistance and stress distribution.An appropriate increase in the front angle(or back angle)reduces the cutting resistance(or frictional resistance),while an excessive increase in the front angle(or back angle)reduces the edge angle and causes stress concentration.The optimal front angle,back angle and edge angle for quartz sand samples areα=25°,β=10°andγ=55°,respectively.The wear resistance of the modelled scrapers made of different metal materials is related to the chemical elements and microstructure.The wear resistances of the modelled scrapers made of 45#,06Cr19Ni10,42CrMo4 and 40CrNiMoA are 0.569,0.661,0.691 and 0.728 times those made of WC-Co,respectively.When the alloy hardness is less than 47 HRC(or greater than 58 HRC),scraper wear decreases slowly with increasing alloy hardness as the scratch depth of the particle asperity on the metal surface stabilizes at a high(or low)level.However,when the alloy hardness is between 47 HRC and 58 HRC,scraper wear decreases rapidly with increasing alloy hardness as the scratch depth transitions from high to low levels.The sensitive hardness interval and recommended hardness interval for quartz sand are[47,58]and[58,62],respectively.The present study provides a reference for optimising scraper parameters and improving cutterhead adaptability in abrasive sandy ground tunnelling.

Erosive Wear Study of PPLSF/Glass Fiber Reinforced Hybrid Laminates

This work is focused to examine the erosive performance of hybrid Palmyra palm leaf stalk fiber(PPLSF)/glass polyester laminate against solid particle bombardment.A hand lay-up method was adopted for the fabricating four piles of five distinct laminates with different stacking order glass and PPLSF layers.Amongst them,one group of pure PPLSF and pure E-glass laminates were fabricated.The hybrid laminates were exposed to high speed stream of solid sand particle at three distinct impact velocities(48,70 and 82 m/s)and four different angles of impingement(30°,45°,60°and 90°).The effect of particle velocity,angle of impingement and stacking order on both wear rate and efficiency were highlighted.The experimental assessment reveals a significant improvement in erosive wear resistance properties due to hybridization of PPLSF with E-glass.Again,the laminates with PPLSF layer as skin and glass as core layer exhibited better erosive wear resistance properties than other types of laminates.Further,a maximum value of erosion at lower velocity(48 m/s)is also noticed at 45°impingement angle.However,at high velocity of impact 70 m/s and 82 m/s,the maximum rate of erosion has been shifted from 45°impact angle to 60°impact angle.The alternation of this semi-ductile character to semi-brittle character is evidenced by analyzing the experimental data.Further to justify the mode of erosion,the eroded surface samples were inspected by scanning electron microscope(SEM).

Erosion Wear Behaviour of Kenaf/Glass Hybrid Polymer Composites

The awareness amongst the researchers to develop an environment friendly sustainable material leads to explore new class of plant-based fiber for making composites. Hybridization of such plant-based fiber with inclusion of engineered fiber is one of the promising methods to not only enhanced the mechanical performance but also suppressed the drawbacks that associate with such plant-based fiber to some extent. A usual hand lay-up method was taken-up in this work to fabricate four layered of hybrid kenaf(K)/glass(G)polyester laminates with different stacking order such as KKKK,KGKG,KGGK,GKKG and GGGG. The erosive character of the laminates was examined under three distinct particle velocities(48m/s, 70m/s,82m/s)and four different impact angles(30°, 45°, 60°, 90°). All fabricated laminates exhibited a semiductile character at lower velocities(48m/s and70m/s)as peak wear rate was observed at45° impact angle. However,they showed a semi-brittle character at high velocity(82m/s)as maximum rate of erosion was noticed at60° impact angle. Again,the influence of stacking order of piles on erosion wear was also clearly noticed. Moreover,the semi-brittle/semi-ductile characterization was also evidenced in accordance to the range of erosion efficiencies. The micro-structures of worn surfaces were inspected thoroughly from the images of scanning electron microscope(SEM)to evident the mechanism of erosion.

HWD-YOLO:A New Vision-Based Helmet Wearing Detection Method

It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents,such as construction sites and mine tunnels.Although existing methods can achieve helmet detection in images,their accuracy and speed still need improvements since complex,cluttered,and large-scale scenes of real workplaces cause server occlusion,illumination change,scale variation,and perspective distortion.So,a new safety helmet-wearing detection method based on deep learning is proposed.Firstly,a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details of concerned objects in the backbone part of the deep neural network.Secondly,a new detection block combining the dilate convolution and attention mechanism is proposed and introduced into the prediction part.This block can effectively extract deep featureswhile retaining information on fine-grained details,such as edges and small objects.Moreover,some newly emerged modules are incorporated into the proposed network to improve safety helmetwearing detection performance further.Extensive experiments on open dataset validate the proposed method.It reaches better performance on helmet-wearing detection and even outperforms the state-of-the-art method.To be more specific,the mAP increases by 3.4%,and the speed increases from17 to 33 fps in comparison with the baseline,You Only Look Once(YOLO)version 5X,and themean average precision increases by 1.0%and the speed increases by 7 fps in comparison with the YOLO version 7.The generalization ability and portability experiment results show that the proposed improvements could serve as a springboard for deep neural network design to improve object detection performance in complex scenarios.

Tool Wear State Recognition with Deep Transfer Learning Based on Spindle Vibration for Milling Process

The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.

Applying the Shearlet-Based Complexity Measure for Analyzing Mass Transfer in Continuous-Flow Microchannels

Continuous-flow microchannels are widely employed for synthesizing various materials,including nanoparticles,polymers,and metal-organic frameworks(MOFs),to name a few.Microsystem technology allows precise control over reaction parameters,resulting in purer,more uniform,and structurally stable products due to more effective mass transfer manipulation.However,continuous-flow synthesis processes may be accompanied by the emergence of spatial convective structures initiating convective flows.On the one hand,convection can accelerate reactions by intensifying mass transfer.On the other hand,it may lead to non-uniformity in the final product or defects,especially in MOF microcrystal synthesis.The ability to distinguish regions of convective and diffusive mass transfer may be the key to performing higher-quality reactions and obtaining purer products.In this study,we investigate,for the first time,the possibility of using the information complexity measure as a criterion for assessing the intensity of mass transfer in microchannels,considering both spatial and temporal non-uniformities of liquid’s distributions resulting from convection formation.We calculate the complexity using shearlet transform based on a local approach.In contrast to existing methods for calculating complexity,the shearlet transform based approach provides a more detailed representation of local heterogeneities.Our analysis involves experimental images illustrating the mixing process of two non-reactive liquids in a Y-type continuous-flow microchannel under conditions of double-diffusive convection formation.The obtained complexity fields characterize the mixing process and structure formation,revealing variations in mass transfer intensity along the microchannel.We compare the results with cases of liquid mixing via a pure diffusive mechanism.Upon analysis,it was revealed that the complexity measure exhibits sensitivity to variations in the type of mass transfer,establishing its feasibility as an indirect criterion for assessing mass transfer intensity.The method presented can extend beyond flow analysis,finding application in the controlling of microstructures of various materials(porosity,for instance)or surface defects in metals,optical systems and other materials that hold significant relevance in materials science and engineering.

Exploring Biocomplexity in Cancer: A Comprehensive Review

Living objects have complex internal and external interactions. The complexity is regulated and controlled by homeostasis, which is the balance of multiple opposing influences. The environmental effects finally guide the self-organized structure. The living systems are open, dynamic structures performing random, stationary, stochastic, self-organizing processes. The self-organizing procedure is defined by the spatial-temporal fractal structure, which is self-similar both in space and time. The system’s complexity appears in its energetics, which tries the most efficient use of the available energies;for that, it organizes various well-connected networks. The controller of environmental relations is the Darwinian selection on a long-time scale. The energetics optimize the healthy processes tuned to the highest efficacy and minimal loss (minimalization of the entropy production). The organism is built up by morphogenetic rules and develops various networks from the genetic level to the organism. The networks have intensive crosstalk and form a balance in the Nash equilibrium, which is the homeostatic state in healthy conditions. Homeostasis may be described as a Nash equilibrium, which ensures energy distribution in a “democratic” way regarding the functions of the parts in the complete system. Cancer radically changes the network system in the organism. Cancer is a network disease. Deviation from healthy networking appears at every level, from genetic (molecular) to cells, tissues, organs, and organisms. The strong proliferation of malignant tissue is the origin of most of the life-threatening processes. The weak side of cancer development is the change of complex information networking in the system, being vulnerable to immune attacks. Cancer cells are masters of adaptation and evade immune surveillance. This hiding process can be broken by electromagnetic nonionizing radiation, for which the malignant structure has no adaptation strategy. Our objective is to review the different sides of living complexity and use the knowledge to fight against cancer.

Data complexity-based batch sanitization method against poison in distributed learning

The security of Federated Learning(FL)/Distributed Machine Learning(DML)is gravely threatened by data poisoning attacks,which destroy the usability of the model by contaminating training samples,so such attacks are called causative availability indiscriminate attacks.Facing the problem that existing data sanitization methods are hard to apply to real-time applications due to their tedious process and heavy computations,we propose a new supervised batch detection method for poison,which can fleetly sanitize the training dataset before the local model training.We design a training dataset generation method that helps to enhance accuracy and uses data complexity features to train a detection model,which will be used in an efficient batch hierarchical detection process.Our model stockpiles knowledge about poison,which can be expanded by retraining to adapt to new attacks.Being neither attack-specific nor scenario-specific,our method is applicable to FL/DML or other online or offline scenarios.

A Comparative Analysis of Syntactic Complexity in Applied Linguistics Abstracts Written by Chinese Novice Writers and Native English Advanced Writers

The rhetorical structure of abstracts has been a widely discussed topic, as it can greatly enhance the abstract writing skills of second-language writers. This study aims to provide guidance on the syntactic features that L2 learners can employ, as well as suggest which features they should focus on in English academic writing. To achieve this, all samples were analyzed for rhetorical moves using Hyland’s five-rhetorical move model. Additionally, all sentences were evaluated for syntactic complexity, considering measures such as global, clausal and phrasal complexity. The findings reveal that expert writers exhibit a more balanced use of syntactic complexity across moves, effectively fulfilling the rhetorical objectives of abstracts. On the other hand, MA students tend to rely excessively on embedded structures and dependent clauses in an attempt to increase complexity. The implications of these findings for academic writing research, pedagogy, and assessment are thoroughly discussed.

Analysis of the Role of Syntactic Complexity in Students’ Reading Comprehension: A Teacher’s Perspective

This study examines the role of the syntactic complexity of the text in the reading comprehension skills of students.Utilizing the qualitative method of research,this paper used structured interview questions as the main data-gathering instruments.English language teachers from Coral na Munti National High School were selected as the respondents of the study.Finding of the study suggests that the syntactic complexity of the text affects the reading comprehension of the students.Students found it challenging to understand the message that the author conveyed if he or she used a large number of phrases and clauses in one sentence.Furthermore,the complex sentence syntactic structure was deemed the most challenging for students to understand.To overcome said challenges in comprehending text,various reading intervention programs were utilized by teachers.These interventions include focused or targeted instruction and the implementation of the Project Dear,suggested by the Department of Education.These programs were proven to help students improve their comprehension as well as their knowledge in syntactical structure of sentences.This study underscores the importance of selecting appropriate reading materials and implementing suitable reading intervention programs to enhance students’comprehension skills.

The effectiveness of different wear indicators in quantifying wear on railway wheels of freight wagons

Railway infrastructure relies on the dynamic interaction between wheels and rails;thus,assessing wheel wear is a critical aspect of maintenance and safety.This paper focuses on the wheel-rail wear indicator T-gamma(Tγ).Amidst its use,it becomes apparent that Tγ,while valuable,fails to provide a comprehensive reflection of the actual material removal and actual contact format,which means that using only Tγas a target for optimization of profiles is not ideal.In this work,three different freight wagons are evaluated:a meter-gauge and a broad-gauge heavy haul vehicles from South American railways,and a standard-gauge freight vehicle operated in Europe,with different axle loads and dissimilar new wheel/rail profiles.These vehicles are subjected to comprehensive multibody simulations on various tracks.The simulations aimed to elucidate the intricate relationship between different wear indicators:Tγ,wear index,material removal,and maximum wear depth,under diverse curves,non-compensated lateral accelerations(A_(nc)),and speeds.Some findings showed a correlation of 0.96 between Tγand wear depth and 0.82 between wear index and material removed for the outer wheel.From the results,the Tγis better than the wear index to be used when analyzing wear depth while the wear index is more suited to foresee the material lost.The results also show the low influence of A_(nc)on wear index and Tγ.By considering these factors together,the study aims to improve the understanding of wheel-rail wear by selecting the best wear analysis approaches based on the effectiveness of each parameter.

Velocity-Incorporated Wear Model of Rolling Guide Shoe Material Selection

To ensure an accurate selection of rolling guide shoe materials,an analysis of the intricate relationship between linear speed and wear is imperative.Finite element simulations and experimental measurements are employed to evaluate four distinct types of materials:polyurethane,rubber,polytetrafluoroethylene(PTFE),and nylon.The speed-index of each material is measured,serving as a preparation for subsequent analysis.Furthermore,the velocity-wear factor is determined,providing insights into the resilience and durability of the material across varying speeds.Additionally,a wear model tailored specifically for viscoelastic bodies is explored,which is pivotal in understanding the wear mechanisms within the material.Leveraging this model,wear predictions are made under higher speed conditions,facilitating the choice of material for rolling guide shoes.To validate the accuracy of the model,the predicted degree of wear is compared with experimental data,ensuring its alignment with both theoretical principles and real-world performance.This comprehensive analysis has verified the effectiveness of the model in the selection of materials under high-speed conditions,thereby offering confidence in its reliability and ensuring optimal performance.

Advances in wearable and implantable bioelectronics for precision medicine

The continuous pursuit for a better quality of life promotes continuous advancements in intelligent technology.Flexible wearable and implantable bioelectronics have emerged as an innovative complement to rigid material-based electronic devices[1-3].Due to their distinct advantages in terms of ductile,ultrathin,and biocompatible features,these elastic and soft bioelectronic devices can be seamlessly mounted onto various real or artificial tissues and organs.

Revolutionizing wearable healthcare with solid-state epidermal biomarker monitoring

In modern medical research, the analysis of biomarkers in biofluids such as blood, urine, saliva, and sweat is indispensable for revealing human physiological processes and disease states[1-4]. However, existing detection methods face significant challenges. Blood and urine analyses are not only inconvenient and costly but also struggle to achieve continuous monitoring. Saliva analysis can be compromised by food residues and bacteria. Additionally, the long-term use of sweat-inducing drugs or iontophoresis [5], aimed at enabling continuous monitoring of biomarkers in sweat for sedentary individuals, may result in discomfort and side effects [6,7].