Zinc-ion batteries(ZIBs)are inexpensive and safe,but side reactions on the Zn anode and Zn dendrite growth hinder their practical applications.In this study,1,3,5-triformylphloroglycerol(Tp)and various diamine monomer...Zinc-ion batteries(ZIBs)are inexpensive and safe,but side reactions on the Zn anode and Zn dendrite growth hinder their practical applications.In this study,1,3,5-triformylphloroglycerol(Tp)and various diamine monomers(p-phenylenediamine(Pa),benzidine(BD),and 4,4"-diamino-p-terphenyl(DATP))were used to synthesize a series of two-dimensional covalent-organic frameworks(COFs).The resulting COFs were named TpPa,TpBD,and TpDATP,respectively,and they showed uniform zincophilic sites,different pore sizes,and high Young's moduli on the Zn anode.Among them,TpPa and TpBD showed lower surface work functions and higher ion transfer numbers,which were conducive to uniform galvanizing/stripping zinc and inhibited dendrite growth.Theoretical calculations showed that TpPa and TpBD had wider negative potential region and greater adsorption capacity for Zn2+than TpDATP,providing more electron donor sites to coordinate with Zn^(2+).Symmetric cells protected by TpPa and TpBD stably cycled for more than 2300 h,whereas TpDATP@Zn and the bare zinc symmetric cells failed after around 150 and200 h.The full cells containing TpPa and TpBD modification layers also showed excellent cycling capacity at 1 A/g.This study provides comprehensive insights into the construction of highly reversible Zn anodes via COF modification layers for advanced rechargeable ZIBs.展开更多
An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in mat...An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples, the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.展开更多
Gear transmissions are widely used in industrial drive systems.Fault diagnosis of gear transmissions is important for maintaining the system performance,reducing the maintenance cost,and providing a safe working envir...Gear transmissions are widely used in industrial drive systems.Fault diagnosis of gear transmissions is important for maintaining the system performance,reducing the maintenance cost,and providing a safe working environment.This paper presents a novel fault diagnosis approach for gear transmissions based on convolutional neural networks(CNNs)and decision-level sensor fusion.In the proposed approach,a CNN is first utilized to classify the faults of a gear transmission based on the acquired signals from each of the sensors.Raw sensory data is sent directly into the CNN models without manual feature extraction.Then,classifier level sensor fusion is carried out to achieve improved classification accuracy by fusing the classification results from the CNN models.Experimental study is conducted,which shows the superior performance of the developed method in the classification of different gear transmission conditions in an automated industrial machine.The presented approach also achieves end-to-end learning that ean be applied to the fault elassification of a gear transmission under various operating eonditions and with signals from different types of sensors.展开更多
Accurate and interpretable fault diagnosis in industrial gear systems is essential for ensuring safety,reliability,and predictive maintenance.This study presents an intelligent diagnostic framework utilizing Gradient ...Accurate and interpretable fault diagnosis in industrial gear systems is essential for ensuring safety,reliability,and predictive maintenance.This study presents an intelligent diagnostic framework utilizing Gradient Boosting(GB)for fault detection in gear systems,applied to the Aalto Gear Fault Dataset,which features a wide range of synthetic and realistic gear failure modes under varied operating conditions.The dataset was preprocessed and analyzed using an ensemble GB classifier,yielding high performance across multiple metrics:accuracy of 96.77%,precision of 95.44%,recall of 97.11%,and an F1-score of 96.22%.To enhance trust in model predictions,the study integrates an explainable AI(XAI)framework using SHAP(SHapley Additive exPlanations)to visualize feature contributions and support diagnostic transparency.A flowchart-based architecture is proposed to guide real-world deployment of interpretable fault detection pipelines.The results demonstrate the feasibility of combining predictive performance with interpretability,offering a robust approach for condition monitoring in safety-critical systems.展开更多
Nowadays, many kinds of software, which have succes sf ully created the integration of CAD, CAPP and CAM, find their disadvantages in p ractical manufacturing. As a result, it is welcomed to develop small CAD/CAPP sy ...Nowadays, many kinds of software, which have succes sf ully created the integration of CAD, CAPP and CAM, find their disadvantages in p ractical manufacturing. As a result, it is welcomed to develop small CAD/CAPP sy stems on a proper CAD platform, which aim at requirements of factories. Based on the one of the most popular three-dimensional design software SolidWorks, we successfully developed a set of gear pump CAD /CAPP software for Huaiyin Gene ral Factory of Mechanics. 1 The architecture and function of the software According to the need of the enterprises, the general structure of the software contain two subsystems, which are detailed as follows: · The gear pump CAD subsystem contains four main modules, including the optiona l design module of gear pump, module of part design, the module of entity modeli ng and the module of drawing which creates 2 D drawings of parts and assembly c orresponding 3 D modals. · The gear pump CAPP subsystem, which is a combination of creative mode and der ivate mode, also includes four modules, they are the module of access to feature s, the module of planning process route, the module of designing process sheets and the module of generating process documents. 2 The developing environment of the system Basing on Windows 98 and SolidWorks2001 platform, this system creates all functi ons by using Visual C++ 6.0 as developing tools. 3 The keys to the CAD/CAPP integration 3.1 The establishment of product information modal The information of parts is the basis of process planning. In this system, we us e feature modeling to describe the information of parts and assembly of gear pum p, and set up a fundamental basis for the whole system. The information module o f gear pump includes the information of both the assembly and the parts; the par t-describing module uses four-layer structure that is subassembly layer, part layer, feature layer and geometric layer. 3.2 The operation of the information on feature modeling After establishment of information mode of gear pump, the next key problem is ho w to transmit information of product to CAPP subsystem. In order to integrate pe rfectly, connecting CAD with CAPP directly can be a good method. In this system, all information of parts is store in the part drawing files, and the CAPP syste m can load these files to get information. The gear pumps, which are produced by Huaiyin General Factory of Mechanics, are usually belong to several similar typ es, and this means that most products possess large number of similar feature. T herefore, we can easily establish a specific feature for gear pump CAD/CAPP syst em. All parts are created by selecting these features and/or adding few special features. In the module of access to feature of CAPP subsystem, the main task is to find the features and the information will be easily got. 4 Conclusion This system successfully creates gear pump CAD/CAPP integration based on SolidWo rks. 3D CAD software provide more convenient platform for integration and will i nevitably become the mainstream of product design. By developing this system, we clearly realize the promising future of SolidWorks.展开更多
The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fau...The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fault characteristics under different loads is markedly inconsistent,and data is hard to label,which makes it difficult for the traditional diagnosis method based on single-condition training to generalize to different conditions.To address these issues,the paper proposes a novel transfer discriminant neural network(TDNN)for gear fault diagnosis.Specifically,an optimized joint distribution adaptive mechanism(OJDA)is designed to solve the distribution alignment problem between two domains.To improve the classification effect within the domain and the feature recognition capability for a few labeled data,metric learning is introduced to distinguish features from different fault categories.In addition,TDNN adopts a new pseudo-label training strategy to achieve label replacement by comparing the maximum probability of the pseudo-label with the test result.The proposed TDNN is verified in the experimental data set of the artillery manipulator device,and the diagnosis can achieve 99.5%,significantly outperforming other traditional adaptation methods.展开更多
During the past few decades,we have witnessed the phenomenon of constant warming occurring everywhere on the globe.Cities have suffered from urban warming to a greater extent than any other part of the world,and Kolka...During the past few decades,we have witnessed the phenomenon of constant warming occurring everywhere on the globe.Cities have suffered from urban warming to a greater extent than any other part of the world,and Kolkata has one of the highest levels of urban warming of any city around the world.In Kolkata,73%of the buildings are residential,and it is this type of building that contributes to a significant amount of this warming.With the city of Kolkata as the case study,this paper aims at understanding the multiple domains of urban heat islands and thermal comfort within the context of the city,from a macro perspective of an urban heat island down to a micro perspective of a building level,with the ultimate aim of mitigating global warming through this study.Various research works have been undertaken in India and abroad to understand the individual as well as composite effect of various building components on the indoor thermal comfort.Researches have also been undertaken to compare and comprehend the differential thermal comfort of old indigenous residences with that of the new residential buildings.Hence,this paper discusses methods that have been applied in past works to evaluate the thermal comfort of old and new residential buildings in a non-subjective manner,without having recourse to user feedback,in the final segment that views the process of learning from comparing old and new residential buildings.展开更多
Based on BERTopic Model,the paper combines qualitative and quantitative methods to explore the reception of Can Xue’s translated works by analyzing readers’book reviews posted on Goodreads and Lovereading.We first c...Based on BERTopic Model,the paper combines qualitative and quantitative methods to explore the reception of Can Xue’s translated works by analyzing readers’book reviews posted on Goodreads and Lovereading.We first collected book reviews from these two well-known websites by Python.Through topic analysis of these reviews,we identified recurring topics,including details of her translated works and appreciation of their translation quality.Then,employing sentiment and content analysis methods,the paper explored the emotional attitudes and the specific thoughts of readers toward Can Xue and her translated works.The fingdings revealed that,among the 408 reviews,though the reception of Can Xue’s translated works was relatively positive,the current level of attention and recognition remains insufficient.However,based on the research results,the paper can derive valuable insights into the translation and dissemination processes such as adjusting translation and dissemination strategies,so that the global reach of Chinese literature and culture can be better facilitated.展开更多
This study develops a contact performance-driven method for skiving face gear drives using a single cutter,eliminating the traditional need for separate cutters to reduce production costs and time.First,the mathematic...This study develops a contact performance-driven method for skiving face gear drives using a single cutter,eliminating the traditional need for separate cutters to reduce production costs and time.First,the mathematical models of the tooth flanks for the face gear drives are established based on the gear skiving processes.Then,load tooth contact analysis(LTCA)model is established to calculate the contact performance data.Next,a two-stage optimization model is employed to determine the optimal parameters of the cutting edge with improved contact performances.The effectiveness of this method is validated through simulations and rolling tests.Compared with the traditional method,the proposed method can machine both the face gear and its mating pinion with a single cutter.Simulation results show that the proposed method avoids tooth surface edge contact,with the maximum tooth surface contact stress reduced by 31.7%,the contact ratio decreases by 21.5%,and the transmission error increases by 22.3%.Rolling tests verify the consistency of tooth surface contact patterns between simulations and experiments.The proposed method provides a reference for the cutting edge design of skiving cutters for face gear pairs.展开更多
This study examined the relationship between inclusive leadership and authenticity at work in racial minority groups of South Africa,taking into account the mediating role of psychological safety and the moderator rol...This study examined the relationship between inclusive leadership and authenticity at work in racial minority groups of South Africa,taking into account the mediating role of psychological safety and the moderator role of gender,in that relationship.The sample was composed of 94 employees predominantly working in the professional services sector from South Africa(41.5%females;mean age=37.1),who self-identified as racial minority groups(coloured/black/Indian).Results indicate that inclusive leadership has no direct effect on authenticity at work;however,psychological safety fully mediates this relationship.Regarding the moderation effect of gender,results showed that males are more likely to diminish their self-alienation(a specific component of authenticity at work)when levels of psychological safety are higher.These results are consistent with Social Identity Theory,which posits that individuals derive part of their self-concept from their membership in social groups.In contexts where inclusive leadership fosters psychological safety,individuals(particularly men in traditionally male-dominated work environments)may feel a stronger sense of belonging and group identity,which in turn enhances their willingness to express their authentic selves and reduces self-alienation.Practical implications for companies include the need to improve leadership styles to foster more of an inclusive and psychologically safe culture,where minority groups can be authentic and flourish.展开更多
Early fault detection for spiral bevel gears is crucial to ensure normal operation and prevent accidents.The harmonic components,excited by the time-varying mesh stiffness,always appear in measured vibration signal.Ho...Early fault detection for spiral bevel gears is crucial to ensure normal operation and prevent accidents.The harmonic components,excited by the time-varying mesh stiffness,always appear in measured vibration signal.How to extract the periodical impulses that indicate gear localized fault buried in the intensive noise and interfered by harmonics is a challenging task.In this paper,a novel Periodical Sparse-Assisted Decoupling(PSAD)method is proposed as an optimization problem to extract fault feature from noisy vibration signal.The PSAD method decouples the impulsive fault feature and harmonic components based on the sparse representation method.The sparsity within and across groups property and the periodicity of the fault feature are incorporated into the regularizer as the prior information.The nonconvex penalty is employed to highlight the sparsity of fault features.Meanwhile,the weight factor based on2norm of each group is constructed to strengthen the amplitude of fault feature.An iterative algorithm with Majorization-Minimization(MM)is derived to solve the optimization problem.Simulation study and experimental analysis confirm the performance of the proposed PSAD method in extracting and enhancing defect impulses from noisy signal.The suggested method surpasses other comparative methods in extracting and enhancing fault features.展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
Background:As an important indicator of subjective well-being(SWB),decent work is a key guarantee for the sustainable development of teachers and their psychological health and work quality.Faced with the rapid develo...Background:As an important indicator of subjective well-being(SWB),decent work is a key guarantee for the sustainable development of teachers and their psychological health and work quality.Faced with the rapid development of artificial intelligence and the global labor market,vocational college teachers are facing challenges such as workload pressure and limited career development,which may harm their well-being.This study aims to localize the measurement method of decent work in Chinese vocational education based on the theory of the Psychology of Working Theory,and explore the relationship mechanism between organizational support,career adaptability,decent work,and job satisfaction among vocational college teachers.Methods:A cross-sectional survey was conducted with 422 HVCU teachers in China(202 male,220 female)using the localized Perceived Organizational Support Scale,Career Adaptability Scale,Decent Work Scale,and Job Satisfaction Scale.Results:The overall level of HVCU teachers’decent work was above the median(Mean=4.09,SD=0.69),laying a foundation for their SWB.Decent work significantly and positively predicted job satisfaction(β=0.620,p<0.001).Organizational support(r=0.58,p<0.001)and career adaptability(r=0.82,p<0.001)can positively affect decent work,and further improve job satisfaction(collective R2 rising from 38.3%to 41.1%).Bootstrap analysis confirmed these mediating effects were robust.Conclusions:This study confirms that the combined effects of organizational support and career adaptability can enhance decent work,further improving teachers’job satisfaction and subsequent subjective well-being.Besides,this study provides an empirical basis for improving the well-being of higher vocational teachers and the sustainable development of vocational education,and has practical significance for improving the teacher incentive policy.展开更多
In the quest to enhance energy efficiency and reduce environmental impact in the transportation sector,the recovery of waste heat from diesel engines has become a critical area of focus.This study provided an exhausti...In the quest to enhance energy efficiency and reduce environmental impact in the transportation sector,the recovery of waste heat from diesel engines has become a critical area of focus.This study provided an exhaustive thermodynamic analysis optimizing Organic Rankine Cycle(ORC)systems forwaste heat recovery fromdiesel engines.Thestudy assessed the performance of five candidateworking fluids—R11,R123,R113,R245fa,and R141b—under a range of operating conditions,specifically varying overheat temperatures and evaporation pressures.The results indicated that the choice of working fluid substantially influences the system’s exergetic efficiency,net output power,and thermal efficiency.R245fa showed an outstanding net output power of 30.39 kW at high overheat conditions,outperforming R11,which is significant for high-temperature waste heat recovery.At lower temperatures,R11 and R113 demonstrated higher exergetic efficiencies,with R11 reaching a peak exergetic efficiency of 7.4%at an evaporation pressure of 10 bar and an overheat of 10℃.The study also revealed that controlling the overheat and optimizing the evaporation pressure are crucial for enhancing the net output power of the ORC system.Specifically,at an evaporation pressure of 30 bar and an overheat of 0℃,R113 exhibited the lowest exergetic destruction of 544.5 kJ/kg,making it a suitable choice for minimizing irreversible losses.These findings are instrumental for understanding the performance of ORC systems in waste heat recovery applications and offer valuable insights for the design and operation of more efficient and environmentally friendly diesel engine systems.展开更多
Founded in 1919, Shanghai Hua Tong Switchgear Works ("HTSW" in short for below) is one of large enterprises on state level. With high and new technology, it is designed as an export base for mechanical and e...Founded in 1919, Shanghai Hua Tong Switchgear Works ("HTSW" in short for below) is one of large enterprises on state level. With high and new technology, it is designed as an export base for mechanical and electrical product of the Ministry of Machinery Industry as well. It has been awarded the honor of first grade factory in the competition for key and major project construction in Shanghai year by year,展开更多
基金financially supported by the National Natural Science Foundation of China(62464010)Spring City Plan-Special Program for Young Talents(K202005007)+3 种基金Yunnan Talents Support Plan for Yong Talents(XDYC-QNRC-2022-0482)Yunnan Local Colleges Applied Basic Research Projects(202101BA070001-138)Key Laboratory of Artificial Microstructures in Yunnan Higher EducationFrontier Research Team of Kunming University 2023。
文摘Zinc-ion batteries(ZIBs)are inexpensive and safe,but side reactions on the Zn anode and Zn dendrite growth hinder their practical applications.In this study,1,3,5-triformylphloroglycerol(Tp)and various diamine monomers(p-phenylenediamine(Pa),benzidine(BD),and 4,4"-diamino-p-terphenyl(DATP))were used to synthesize a series of two-dimensional covalent-organic frameworks(COFs).The resulting COFs were named TpPa,TpBD,and TpDATP,respectively,and they showed uniform zincophilic sites,different pore sizes,and high Young's moduli on the Zn anode.Among them,TpPa and TpBD showed lower surface work functions and higher ion transfer numbers,which were conducive to uniform galvanizing/stripping zinc and inhibited dendrite growth.Theoretical calculations showed that TpPa and TpBD had wider negative potential region and greater adsorption capacity for Zn2+than TpDATP,providing more electron donor sites to coordinate with Zn^(2+).Symmetric cells protected by TpPa and TpBD stably cycled for more than 2300 h,whereas TpDATP@Zn and the bare zinc symmetric cells failed after around 150 and200 h.The full cells containing TpPa and TpBD modification layers also showed excellent cycling capacity at 1 A/g.This study provides comprehensive insights into the construction of highly reversible Zn anodes via COF modification layers for advanced rechargeable ZIBs.
文摘An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples, the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.
基金supported byan ENGAGE Grant from the Natural Sciences and Engineering Research Council of Canada(NSERC),[funding reference number 11R01296].
文摘Gear transmissions are widely used in industrial drive systems.Fault diagnosis of gear transmissions is important for maintaining the system performance,reducing the maintenance cost,and providing a safe working environment.This paper presents a novel fault diagnosis approach for gear transmissions based on convolutional neural networks(CNNs)and decision-level sensor fusion.In the proposed approach,a CNN is first utilized to classify the faults of a gear transmission based on the acquired signals from each of the sensors.Raw sensory data is sent directly into the CNN models without manual feature extraction.Then,classifier level sensor fusion is carried out to achieve improved classification accuracy by fusing the classification results from the CNN models.Experimental study is conducted,which shows the superior performance of the developed method in the classification of different gear transmission conditions in an automated industrial machine.The presented approach also achieves end-to-end learning that ean be applied to the fault elassification of a gear transmission under various operating eonditions and with signals from different types of sensors.
文摘Accurate and interpretable fault diagnosis in industrial gear systems is essential for ensuring safety,reliability,and predictive maintenance.This study presents an intelligent diagnostic framework utilizing Gradient Boosting(GB)for fault detection in gear systems,applied to the Aalto Gear Fault Dataset,which features a wide range of synthetic and realistic gear failure modes under varied operating conditions.The dataset was preprocessed and analyzed using an ensemble GB classifier,yielding high performance across multiple metrics:accuracy of 96.77%,precision of 95.44%,recall of 97.11%,and an F1-score of 96.22%.To enhance trust in model predictions,the study integrates an explainable AI(XAI)framework using SHAP(SHapley Additive exPlanations)to visualize feature contributions and support diagnostic transparency.A flowchart-based architecture is proposed to guide real-world deployment of interpretable fault detection pipelines.The results demonstrate the feasibility of combining predictive performance with interpretability,offering a robust approach for condition monitoring in safety-critical systems.
文摘Nowadays, many kinds of software, which have succes sf ully created the integration of CAD, CAPP and CAM, find their disadvantages in p ractical manufacturing. As a result, it is welcomed to develop small CAD/CAPP sy stems on a proper CAD platform, which aim at requirements of factories. Based on the one of the most popular three-dimensional design software SolidWorks, we successfully developed a set of gear pump CAD /CAPP software for Huaiyin Gene ral Factory of Mechanics. 1 The architecture and function of the software According to the need of the enterprises, the general structure of the software contain two subsystems, which are detailed as follows: · The gear pump CAD subsystem contains four main modules, including the optiona l design module of gear pump, module of part design, the module of entity modeli ng and the module of drawing which creates 2 D drawings of parts and assembly c orresponding 3 D modals. · The gear pump CAPP subsystem, which is a combination of creative mode and der ivate mode, also includes four modules, they are the module of access to feature s, the module of planning process route, the module of designing process sheets and the module of generating process documents. 2 The developing environment of the system Basing on Windows 98 and SolidWorks2001 platform, this system creates all functi ons by using Visual C++ 6.0 as developing tools. 3 The keys to the CAD/CAPP integration 3.1 The establishment of product information modal The information of parts is the basis of process planning. In this system, we us e feature modeling to describe the information of parts and assembly of gear pum p, and set up a fundamental basis for the whole system. The information module o f gear pump includes the information of both the assembly and the parts; the par t-describing module uses four-layer structure that is subassembly layer, part layer, feature layer and geometric layer. 3.2 The operation of the information on feature modeling After establishment of information mode of gear pump, the next key problem is ho w to transmit information of product to CAPP subsystem. In order to integrate pe rfectly, connecting CAD with CAPP directly can be a good method. In this system, all information of parts is store in the part drawing files, and the CAPP syste m can load these files to get information. The gear pumps, which are produced by Huaiyin General Factory of Mechanics, are usually belong to several similar typ es, and this means that most products possess large number of similar feature. T herefore, we can easily establish a specific feature for gear pump CAD/CAPP syst em. All parts are created by selecting these features and/or adding few special features. In the module of access to feature of CAPP subsystem, the main task is to find the features and the information will be easily got. 4 Conclusion This system successfully creates gear pump CAD/CAPP integration based on SolidWo rks. 3D CAD software provide more convenient platform for integration and will i nevitably become the mainstream of product design. By developing this system, we clearly realize the promising future of SolidWorks.
文摘The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fault characteristics under different loads is markedly inconsistent,and data is hard to label,which makes it difficult for the traditional diagnosis method based on single-condition training to generalize to different conditions.To address these issues,the paper proposes a novel transfer discriminant neural network(TDNN)for gear fault diagnosis.Specifically,an optimized joint distribution adaptive mechanism(OJDA)is designed to solve the distribution alignment problem between two domains.To improve the classification effect within the domain and the feature recognition capability for a few labeled data,metric learning is introduced to distinguish features from different fault categories.In addition,TDNN adopts a new pseudo-label training strategy to achieve label replacement by comparing the maximum probability of the pseudo-label with the test result.The proposed TDNN is verified in the experimental data set of the artillery manipulator device,and the diagnosis can achieve 99.5%,significantly outperforming other traditional adaptation methods.
文摘During the past few decades,we have witnessed the phenomenon of constant warming occurring everywhere on the globe.Cities have suffered from urban warming to a greater extent than any other part of the world,and Kolkata has one of the highest levels of urban warming of any city around the world.In Kolkata,73%of the buildings are residential,and it is this type of building that contributes to a significant amount of this warming.With the city of Kolkata as the case study,this paper aims at understanding the multiple domains of urban heat islands and thermal comfort within the context of the city,from a macro perspective of an urban heat island down to a micro perspective of a building level,with the ultimate aim of mitigating global warming through this study.Various research works have been undertaken in India and abroad to understand the individual as well as composite effect of various building components on the indoor thermal comfort.Researches have also been undertaken to compare and comprehend the differential thermal comfort of old indigenous residences with that of the new residential buildings.Hence,this paper discusses methods that have been applied in past works to evaluate the thermal comfort of old and new residential buildings in a non-subjective manner,without having recourse to user feedback,in the final segment that views the process of learning from comparing old and new residential buildings.
基金supported by the 2023 Youth Fund for Humanities and Social Sciences Research by the Ministry of Education of the People’s Republic of China(Grant No.23YJC740004).
文摘Based on BERTopic Model,the paper combines qualitative and quantitative methods to explore the reception of Can Xue’s translated works by analyzing readers’book reviews posted on Goodreads and Lovereading.We first collected book reviews from these two well-known websites by Python.Through topic analysis of these reviews,we identified recurring topics,including details of her translated works and appreciation of their translation quality.Then,employing sentiment and content analysis methods,the paper explored the emotional attitudes and the specific thoughts of readers toward Can Xue and her translated works.The fingdings revealed that,among the 408 reviews,though the reception of Can Xue’s translated works was relatively positive,the current level of attention and recognition remains insufficient.However,based on the research results,the paper can derive valuable insights into the translation and dissemination processes such as adjusting translation and dissemination strategies,so that the global reach of Chinese literature and culture can be better facilitated.
基金Project(2024YFB3410402)supported by the National Key R&D Program of ChinaProject(52075558)supported by the National Natural Science Foundation of China+2 种基金Project(2021RC3012)supported by the Science and Technology Innovation Program of Hunan Province,ChinaProject(2023CXQD050)supported by the Central South University Innovation-Driven Research Program,ChinaProject(CX20230255)supported by the Fundamental Research Funds for the Central Universities,China。
文摘This study develops a contact performance-driven method for skiving face gear drives using a single cutter,eliminating the traditional need for separate cutters to reduce production costs and time.First,the mathematical models of the tooth flanks for the face gear drives are established based on the gear skiving processes.Then,load tooth contact analysis(LTCA)model is established to calculate the contact performance data.Next,a two-stage optimization model is employed to determine the optimal parameters of the cutting edge with improved contact performances.The effectiveness of this method is validated through simulations and rolling tests.Compared with the traditional method,the proposed method can machine both the face gear and its mating pinion with a single cutter.Simulation results show that the proposed method avoids tooth surface edge contact,with the maximum tooth surface contact stress reduced by 31.7%,the contact ratio decreases by 21.5%,and the transmission error increases by 22.3%.Rolling tests verify the consistency of tooth surface contact patterns between simulations and experiments.The proposed method provides a reference for the cutting edge design of skiving cutters for face gear pairs.
文摘This study examined the relationship between inclusive leadership and authenticity at work in racial minority groups of South Africa,taking into account the mediating role of psychological safety and the moderator role of gender,in that relationship.The sample was composed of 94 employees predominantly working in the professional services sector from South Africa(41.5%females;mean age=37.1),who self-identified as racial minority groups(coloured/black/Indian).Results indicate that inclusive leadership has no direct effect on authenticity at work;however,psychological safety fully mediates this relationship.Regarding the moderation effect of gender,results showed that males are more likely to diminish their self-alienation(a specific component of authenticity at work)when levels of psychological safety are higher.These results are consistent with Social Identity Theory,which posits that individuals derive part of their self-concept from their membership in social groups.In contexts where inclusive leadership fosters psychological safety,individuals(particularly men in traditionally male-dominated work environments)may feel a stronger sense of belonging and group identity,which in turn enhances their willingness to express their authentic selves and reduces self-alienation.Practical implications for companies include the need to improve leadership styles to foster more of an inclusive and psychologically safe culture,where minority groups can be authentic and flourish.
基金supported by the National Science Foundationof China(Nos.52305127 and 52475130)。
文摘Early fault detection for spiral bevel gears is crucial to ensure normal operation and prevent accidents.The harmonic components,excited by the time-varying mesh stiffness,always appear in measured vibration signal.How to extract the periodical impulses that indicate gear localized fault buried in the intensive noise and interfered by harmonics is a challenging task.In this paper,a novel Periodical Sparse-Assisted Decoupling(PSAD)method is proposed as an optimization problem to extract fault feature from noisy vibration signal.The PSAD method decouples the impulsive fault feature and harmonic components based on the sparse representation method.The sparsity within and across groups property and the periodicity of the fault feature are incorporated into the regularizer as the prior information.The nonconvex penalty is employed to highlight the sparsity of fault features.Meanwhile,the weight factor based on2norm of each group is constructed to strengthen the amplitude of fault feature.An iterative algorithm with Majorization-Minimization(MM)is derived to solve the optimization problem.Simulation study and experimental analysis confirm the performance of the proposed PSAD method in extracting and enhancing defect impulses from noisy signal.The suggested method surpasses other comparative methods in extracting and enhancing fault features.
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
基金funded by Nanjing University of Posts and Telecommunications Humanities and Social Sciences Research Fund Project(NYY222055)Special research project on teaching reform of innovation and entrepreneurship education in Nanjing University of Posts and Telecommunications(GCSJG202528)+2 种基金General Subject of Educational Science Planning in Jiangsu Province(C/2024/01/76)General project of educational science research in Shanghai(C24288)Key funded project of Shandong Vocational Education Teaching Reform Research in 2022(2022052).
文摘Background:As an important indicator of subjective well-being(SWB),decent work is a key guarantee for the sustainable development of teachers and their psychological health and work quality.Faced with the rapid development of artificial intelligence and the global labor market,vocational college teachers are facing challenges such as workload pressure and limited career development,which may harm their well-being.This study aims to localize the measurement method of decent work in Chinese vocational education based on the theory of the Psychology of Working Theory,and explore the relationship mechanism between organizational support,career adaptability,decent work,and job satisfaction among vocational college teachers.Methods:A cross-sectional survey was conducted with 422 HVCU teachers in China(202 male,220 female)using the localized Perceived Organizational Support Scale,Career Adaptability Scale,Decent Work Scale,and Job Satisfaction Scale.Results:The overall level of HVCU teachers’decent work was above the median(Mean=4.09,SD=0.69),laying a foundation for their SWB.Decent work significantly and positively predicted job satisfaction(β=0.620,p<0.001).Organizational support(r=0.58,p<0.001)and career adaptability(r=0.82,p<0.001)can positively affect decent work,and further improve job satisfaction(collective R2 rising from 38.3%to 41.1%).Bootstrap analysis confirmed these mediating effects were robust.Conclusions:This study confirms that the combined effects of organizational support and career adaptability can enhance decent work,further improving teachers’job satisfaction and subsequent subjective well-being.Besides,this study provides an empirical basis for improving the well-being of higher vocational teachers and the sustainable development of vocational education,and has practical significance for improving the teacher incentive policy.
基金funded by the Huaiyin Institute of Technology—Institute of Smart Energy.
文摘In the quest to enhance energy efficiency and reduce environmental impact in the transportation sector,the recovery of waste heat from diesel engines has become a critical area of focus.This study provided an exhaustive thermodynamic analysis optimizing Organic Rankine Cycle(ORC)systems forwaste heat recovery fromdiesel engines.Thestudy assessed the performance of five candidateworking fluids—R11,R123,R113,R245fa,and R141b—under a range of operating conditions,specifically varying overheat temperatures and evaporation pressures.The results indicated that the choice of working fluid substantially influences the system’s exergetic efficiency,net output power,and thermal efficiency.R245fa showed an outstanding net output power of 30.39 kW at high overheat conditions,outperforming R11,which is significant for high-temperature waste heat recovery.At lower temperatures,R11 and R113 demonstrated higher exergetic efficiencies,with R11 reaching a peak exergetic efficiency of 7.4%at an evaporation pressure of 10 bar and an overheat of 10℃.The study also revealed that controlling the overheat and optimizing the evaporation pressure are crucial for enhancing the net output power of the ORC system.Specifically,at an evaporation pressure of 30 bar and an overheat of 0℃,R113 exhibited the lowest exergetic destruction of 544.5 kJ/kg,making it a suitable choice for minimizing irreversible losses.These findings are instrumental for understanding the performance of ORC systems in waste heat recovery applications and offer valuable insights for the design and operation of more efficient and environmentally friendly diesel engine systems.
文摘Founded in 1919, Shanghai Hua Tong Switchgear Works ("HTSW" in short for below) is one of large enterprises on state level. With high and new technology, it is designed as an export base for mechanical and electrical product of the Ministry of Machinery Industry as well. It has been awarded the honor of first grade factory in the competition for key and major project construction in Shanghai year by year,
