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High-efficiency Carbonation Modification Methods of Recycled Coarse Aggregates
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作者 张美香 YANG Xiaolin +3 位作者 丁亚红 SUN Bo ZHANG Xianggang LÜXiuwen 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS CSCD 2024年第2期386-398,共13页
To solve the problem of only surface carbonation and realize high-efficiency carbonation of recycled coarse aggregate,the method of carbonated recycled coarse aggregate with nano materials pre-soaking was first put fo... To solve the problem of only surface carbonation and realize high-efficiency carbonation of recycled coarse aggregate,the method of carbonated recycled coarse aggregate with nano materials pre-soaking was first put forward.The carbonation effect of modified recycled coarse aggregate with three different carbonation methods was evaluated,and water absorption,apparent density and crush index of modified recycled coarse aggregate were measured.Combined with XRD,SEM,and MIP microscopic analysis,the high-efficiency carbonation strengthening mechanism of modified recycled coarse aggregate was revealed.The experimental results show that,compared with the non-carbonated recycled coarse aggregate,the physical and microscopic properties of carbonated recycled coarse aggregate are improved.The method of carbonation with nano-SiO_(2) pre-soaking can realize the high-efficiency carbonation of recycled coarse aggregate,for modified recycled coarse aggregate with the method,water absorption is reduced by 23.03%,porosity is reduced by 44.06%,and the average pore diameter is 21.82 nm.The high-efficiency carbonation strengthening mechanism show that the pre-socked nano-SiO_(2) is bound to the hydration product Ca(OH)_(2) of the old mortar with nano-scale C-S-H,which can improve the CO_(2) absorption rate,accelerate the carbonation reaction,generate more stable CaCO_(3) and nano-scale silica gel,and bond to the dense three-dimensional network structure to realize the bidirectional enhancement of nano-materials and pressurized carbonation.It is concluded that the method of carbonation with nano-SiO_(2) pre-soaking is a novel high-efficiency carbonation modification of recycled coarse aggregate. 展开更多
关键词 recycled coarse aggregate pressurized carbonation high-efficiency carbonation NANO-SIO2 strengthening mechanism
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On dry machining of AZ31B magnesium alloy using textured cutting tool inserts
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作者 Shailendra Pawanr Kapil Gupta 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2024年第4期1608-1618,共11页
Magnesium alloys have many advantages as lightweight materials for engineering applications,especially in the fields of automotive and aerospace.They undergo extensive cutting or machining while making products out of... Magnesium alloys have many advantages as lightweight materials for engineering applications,especially in the fields of automotive and aerospace.They undergo extensive cutting or machining while making products out of them.Dry cutting,a sustainable machining method,causes more friction and adhesion at the tool-chip interface.One of the promising solutions to this problem is cutting tool surface texturing,which can reduce tool wear and friction in dry cutting and improve machining performance.This paper aims to investigate the impact of dimple textures(made on the flank face of cutting inserts)on tool wear and chip morphology in the dry machining of AZ31B magnesium alloy.The results show that the cutting speed was the most significant factor affecting tool flank wear,followed by feed rate and cutting depth.The tool wear mechanism was examined using scanning electron microscope(SEM)images and energy dispersive X-ray spectroscopy(EDS)analysis reports,which showed that at low cutting speed,the main wear mechanism was abrasion,while at high speed,it was adhesion.The chips are discontinuous at low cutting speeds,while continuous at high cutting speeds.The dimple textured flank face cutting tools facilitate the dry machining of AZ31B magnesium alloy and contribute to ecological benefits. 展开更多
关键词 Magnesium alloy Dry machining Textured tools Flank wear SUSTAINABILITY
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Field-assisted machining of difficult-to-machine materials
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作者 Jianguo Zhang Zhengding Zheng +5 位作者 Kai Huang Chuangting Lin Weiqi Huang Xiao Chen Junfeng Xiao Jianfeng Xu 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2024年第3期39-89,共51页
Difficult-to-machine materials (DMMs) are extensively applied in critical fields such as aviation,semiconductor,biomedicine,and other key fields due to their excellent material properties.However,traditional machining... Difficult-to-machine materials (DMMs) are extensively applied in critical fields such as aviation,semiconductor,biomedicine,and other key fields due to their excellent material properties.However,traditional machining technologies often struggle to achieve ultra-precision with DMMs resulting from poor surface quality and low processing efficiency.In recent years,field-assisted machining (FAM) technology has emerged as a new generation of machining technology based on innovative principles such as laser heating,tool vibration,magnetic magnetization,and plasma modification,providing a new solution for improving the machinability of DMMs.This technology not only addresses these limitations of traditional machining methods,but also has become a hot topic of research in the domain of ultra-precision machining of DMMs.Many new methods and principles have been introduced and investigated one after another,yet few studies have presented a comprehensive analysis and summarization.To fill this gap and understand the development trend of FAM,this study provides an important overview of FAM,covering different assisted machining methods,application effects,mechanism analysis,and equipment design.The current deficiencies and future challenges of FAM are summarized to lay the foundation for the further development of multi-field hybrid assisted and intelligent FAM technologies. 展开更多
关键词 field-assisted machining difficult-to-machine materials materials removal mechanism surface integrity
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Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method
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作者 Xiaojia Yang Jinghuan Jia +5 位作者 Qing Li Renzheng Zhu Jike Yang Zhiyong Liu Xuequn Cheng Xiaogang Li 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2024年第6期1311-1321,共11页
Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for st... Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection. 展开更多
关键词 weathering steel stress-assisted corrosion gradient boosting decision tree machining learning
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Nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in aerospace community:a comparative analysis
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作者 Guolong Zhao Biao Zhao +5 位作者 Wenfeng Ding Lianjia Xin Zhiwen Nian Jianhao Peng Ning He Jiuhua Xu 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2024年第2期190-271,共82页
The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,su... The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,such as thin-walled structures,microchannels,and complex surfaces.Mechanical machining is the main material removal process for the vast majority of aerospace components.However,many problems exist,including severe and rapid tool wear,low machining efficiency,and poor surface integrity.Nontraditional energy-assisted mechanical machining is a hybrid process that uses nontraditional energies(vibration,laser,electricity,etc)to improve the machinability of local materials and decrease the burden of mechanical machining.This provides a feasible and promising method to improve the material removal rate and surface quality,reduce process forces,and prolong tool life.However,systematic reviews of this technology are lacking with respect to the current research status and development direction.This paper reviews the recent progress in the nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in the aerospace community.In addition,this paper focuses on the processing principles,material responses under nontraditional energy,resultant forces and temperatures,material removal mechanisms,and applications of these processes,including vibration-,laser-,electric-,magnetic-,chemical-,advanced coolant-,and hybrid nontraditional energy-assisted mechanical machining.Finally,a comprehensive summary of the principles,advantages,and limitations of each hybrid process is provided,and future perspectives on forward design,device development,and sustainability of nontraditional energy-assisted mechanical machining processes are discussed. 展开更多
关键词 difficult-to-cut materials geometrically complex components nontraditional energy mechanical machining aerospace community
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Effect of tool geometry on ultraprecision machining of soft-brittle materials:a comprehensive review 被引量:2
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作者 Weihai Huang Jiwang Yan 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2023年第1期60-98,共39页
Brittle materials are widely used for producing important components in the industry of optics,optoelectronics,and semiconductors.Ultraprecision machining of brittle materials with high surface quality and surface int... Brittle materials are widely used for producing important components in the industry of optics,optoelectronics,and semiconductors.Ultraprecision machining of brittle materials with high surface quality and surface integrity helps improve the functional performance and lifespan of the components.According to their hardness,brittle materials can be roughly divided into hard-brittle and soft-brittle.Although there have been some literature reviews for ultraprecision machining of hard-brittle materials,up to date,very few review papers are available that focus on the processing of soft-brittle materials.Due to the‘soft’and‘brittle’properties,this group of materials has unique machining characteristics.This paper presents a comprehensive overview of recent advances in ultraprecision machining of soft-brittle materials.Critical aspects of machining mechanisms,such as chip formation,surface topography,and subsurface damage for different machining methods,including diamond turning,micro end milling,ultraprecision grinding,and micro/nano burnishing,are compared in terms of tool-workpiece interaction.The effects of tool geometries on the machining characteristics of soft-brittle materials are systematically analyzed,and dominating factors are sorted out.Problems and challenges in the engineering applications are identified,and solutions/guidelines for future R&D are provided. 展开更多
关键词 ultraprecision machining soft-brittle materials ductile machining tool geometries material removal mechanisms surface integrity
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Laser machining fundamentals:micro,nano,atomic and close-to-atomic scales 被引量:5
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作者 Jinshi Wang Fengzhou Fang +4 位作者 Haojie An Shan Wu Huimin Qi Yuexuan Cai Guanyu Guo 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2023年第1期125-151,共27页
With the rapid development in advanced industries,such as microelectronics and optics sectors,the functional feature size of devises/components has been decreasing from micro to nanometric,and even ACS for higher perf... With the rapid development in advanced industries,such as microelectronics and optics sectors,the functional feature size of devises/components has been decreasing from micro to nanometric,and even ACS for higher performance,smaller volume and lower energy consumption.By this time,a great many quantum structures are proposed,with not only an extreme scale of several or even single atom,but also a nearly ideal lattice structure with no material defect.It is almost no doubt that such structures play critical role in the next generation products,which shows an urgent demand for the ACSM.Laser machining is one of the most important approaches widely used in engineering and scientific research.It is high-efficient and applicable for most kinds of materials.Moreover,the processing scale covers a huge range from millimeters to nanometers,and has already touched the atomic level.Laser–material interaction mechanism,as the foundation of laser machining,determines the machining accuracy and surface quality.It becomes much more sophisticated and dominant with a decrease in processing scale,which is systematically reviewed in this article.In general,the mechanisms of laser-induced material removal are classified into ablation,CE and atomic desorption,with a decrease in the scale from above microns to angstroms.The effects of processing parameters on both fundamental material response and machined surface quality are discussed,as well as theoretical methods to simulate and understand the underlying mechanisms.Examples at nanometric to atomic scale are provided,which demonstrate the capability of laser machining in achieving the ultimate precision and becoming a promising approach to ACSM. 展开更多
关键词 laser machining mechanism atomic and close-to-atomic scale manufacturing ACSM manufacturing III
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Toward high-efficiency perovskite solar cells with one-dimensional oriented nanostructured electron transport materials 被引量:1
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作者 Yinhua Lv Bing Cai +3 位作者 Ruihan Yuan Yihui Wu Quinn Qiao Wen-Hua Zhang 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2023年第7期66-87,I0003,共23页
The unique advantages of one-dimensional(1D)oriented nanostructures in light-trapping and chargetransport make them competitive candidates in photovoltaic(PV)devices.Since the emergence of perovskite solar cells(PSCs)... The unique advantages of one-dimensional(1D)oriented nanostructures in light-trapping and chargetransport make them competitive candidates in photovoltaic(PV)devices.Since the emergence of perovskite solar cells(PSCs),1D nanostructured electron transport materials(ETMs)have drawn tremendous interest.However,the power conversion efficiencies(PCEs)of these devices have always significantly lagged behind their mesoscopic and planar counterparts.High-efficiency PSCs with 1D ETMs showing efficiency over 22%were just realized in the most recent studies.It yet lacks a comprehensive review covering the development of 1D ETMs and their application in PSCs.We hence timely summarize the advances in 1D ETMs-based solar cells,emphasizing on the fundamental and optimization issues of charge separation and collection ability,and their influence on PV performance.After sketching the classification and requirements for high-efficiency 1D nanostructured solar cells,we highlight the applicability of 1D TiO_(2)nanostructures in PSCs,including nanotubes,nanorods,nanocones,and nanopyramids,and carefully analyze how the electrostatic field affects cell performance.Other kinds of oriented nanostructures,e.g.,ZnO and SnO_(2)ETMs,are also described.Finally,we discuss the challenges and propose some potential strategies to further boost device performance.This review provides a broad range of valuable work in this fast-developing field,which we hope will stimulate research enthusiasm to push PSCs to an unprecedented level. 展开更多
关键词 1D nanostructures Perovskite solar cells Electron transport materials Electrostatic field high-efficiency
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Failure mode change and material damage with varied machining speeds:a review 被引量:1
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作者 Jianqiu Zhang Binbin He Bi Zhang 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2023年第2期36-60,共25页
High-speed machining(HSM) has been studied for several decades and has potential application in various industries, including the automobile and aerospace industries. However,the underlying mechanisms of HSM have not ... High-speed machining(HSM) has been studied for several decades and has potential application in various industries, including the automobile and aerospace industries. However,the underlying mechanisms of HSM have not been formally reviewed thus far. This article focuses on the solid mechanics framework of adiabatic shear band(ASB) onset and material metallurgical microstructural evolutions in HSM. The ASB onset is described using partial differential systems. Several factors in HSM were considered in the systems, and the ASB onset conditions were obtained by solving these systems or applying the perturbation method to the systems. With increasing machining speed, an ASB can be depressed and further eliminated by shock pressure. The damage observed in HSM exhibits common features. Equiaxed fine grains produced by dynamic recrystallization widely cause damage to ductile materials, and amorphization is the common microstructural evolution in brittle materials. Based on previous studies, potential mechanisms for the phenomena in HSM are proposed. These include the thickness variation of the white layer of ductile materials. These proposed mechanisms would be beneficial to deeply understanding the various phenomena in HSM. 展开更多
关键词 high-speed machining adiabatic shear band subsurface damage dynamic recrystallization
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Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning 被引量:5
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作者 Ling Wang Deng-Yan Long 《World Journal of Clinical Cases》 SCIE 2024年第7期1235-1242,共8页
BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective pr... BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration. 展开更多
关键词 Intensive care unit-acquired weakness Risk factors machine learning PREVENTION Strategies
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Machine learning applications in stroke medicine:advancements,challenges,and future prospectives 被引量:2
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作者 Mario Daidone Sergio Ferrantelli Antonino Tuttolomondo 《Neural Regeneration Research》 SCIE CAS CSCD 2024年第4期769-773,共5页
Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning technique... Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease. 展开更多
关键词 cerebrovascular disease deep learning machine learning reinforcement learning STROKE stroke therapy supervised learning unsupervised learning
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Assessment of compressive strength of jet grouting by machine learning 被引量:1
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作者 Esteban Diaz Edgar Leonardo Salamanca-Medina Roberto Tomas 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第1期102-111,共10页
Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the prope... Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns. 展开更多
关键词 Jet grouting Ground improvement Compressive strength machine learning
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Selection of Greenhouse Zucchini Varieties and High-Quality,High-Yield and High-Efficiency Cultivation Techniques
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作者 Haijuan ZHANG Guanghui FENG +3 位作者 Lifeng YANG Bo GENG Xiangying HOU Dongwen SUN 《Asian Agricultural Research》 2023年第6期38-40,共3页
[Objectives]To select zucchini varieties suitable for cultivation in Zibo City and test its high-yield cultivation techniques.[Methods]Six zucchini varieties were introduced,and their commercial quality and yield were... [Objectives]To select zucchini varieties suitable for cultivation in Zibo City and test its high-yield cultivation techniques.[Methods]Six zucchini varieties were introduced,and their commercial quality and yield were determined.[Results]The yield of Shengfeier,Xiuyu 170 and Xihulu 309 increased by 11.4%,6.9%and 4.6%,respectively compared with S68(control),and zucchini was straight,looked pleasing to the eye,and had strong disease resistance.[Conclusions]The zucchini varieties were selected and the high-quality,high-yield and high-efficiency cultivation techniques were integrated. 展开更多
关键词 ZUCCHINI VARIETY HIGH-QUALITY high-yield and high-efficiency Cultivation techniques
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Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms 被引量:1
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作者 Jingou Kuang Zhilin Long 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2024年第2期337-350,共14页
This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while ... This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed model.Through optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction accuracy.The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost methods.The established ML models exhibited better predic-tion performance and generalization ability via property transformation descriptors.In addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion rate.The results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models. 展开更多
关键词 machine learning low-alloy steel atmospheric corrosion prediction corrosion rate feature fusion
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Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature 被引量:1
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作者 Mengwei Wu Wei Yong +2 位作者 Cunqin Fu Chunmei Ma Ruiping Liu 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2024年第4期773-785,共13页
The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important prac... The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys. 展开更多
关键词 machine learning support vector regression shape memory alloys martensitic transformation temperature
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Prediction of Wearing of Cutting Tools Using Real Time Machining Parameters and Temperature Using Rayleigh-Ham Method
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作者 Jean Nyatte Nyatte Fabrice Alban Epee +3 位作者 Wilba Christophe Kikmo Samuel Batambock Claude Valéry Ngayihi Abbe Robert Nzengwa 《Modern Mechanical Engineering》 2023年第2期35-54,共20页
Wear of cutting tools is a big concern for industrial manufacturers, because of their acquisition cost as well as the impact on the production lines when they are unavailable. Law of wear is very important in determin... Wear of cutting tools is a big concern for industrial manufacturers, because of their acquisition cost as well as the impact on the production lines when they are unavailable. Law of wear is very important in determining cutting tools lifespan, but most of the existing models don’t take into account the cutting temperature. In this work, the theoretical and experimental results of a dynamic study of metal machining against cutting temperature of a treated steel of grade S235JR with a high-speed steel tool are provided. This study is based on the analysis of two complementary approaches, an experimental approach with the measurement of the temperature and on the other hand, an approach using modeling. Based on unifactorial and multifactorial tests (speed of cut, feed, and depth of cut), this study allowed the highlighting of the influence of the cutting temperature on the machining time. To achieve this objective, two specific approaches have been selected. The first was to measure the temperature of the cutting tool and the second was to determine the wear law using Rayleigh-Ham dimensional analysis method. This study permitted the determination of a law that integrates the cutting temperature in the calculations of the lifespan of the tools during machining. 展开更多
关键词 machining Cutting Temperature Modeling Wear Cutting Tool
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ArcCHECK Machine QA工具在医用直线加速器质量保证中的应用效果
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作者 张上超 曾华驱 王思阳 《医疗装备》 2024年第7期19-24,共6页
目的探讨ArcCHECK Machine QA工具在医用直线加速器质量保证中的应用效果。方法利用ArcCHECK Machine QA工具和ArcCHECK体模对医用直线加速器进行性能测试,项目包括机架角度、机架旋转速度、机架旋转中心、多叶准直器和铅门位置的一致... 目的探讨ArcCHECK Machine QA工具在医用直线加速器质量保证中的应用效果。方法利用ArcCHECK Machine QA工具和ArcCHECK体模对医用直线加速器进行性能测试,项目包括机架角度、机架旋转速度、机架旋转中心、多叶准直器和铅门位置的一致性、机架旋转出束时的平坦度和对称性,评估该工具在医用直线加速器质量保证中的应用效果。结果旋转模式下机架平均旋转速度为3.6 deg/s,最大偏差约0.5 deg/s;机架旋转等中心形成的平均半径为0.4 mm,多叶准直器与铅门的最大距离正、负差异平均值分别为0.7 mm、-0.7 mm;旋转出束模式下Y方向的平坦度为1.8%,Y方向的对称性为1.1%,X方向的对称性为4.3%。结论ArcCHECK Machine QA工具可用于医用直线加速器常规及容积调强出束性能质量保证。 展开更多
关键词 ArcCHECK machine QA工具 质量保证 容积调强 等中心
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A New Dynamics Analysis Model for Five-Axis Machining of Curved Surface Based on Dimension Reduction and Mapping
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作者 Minglong Guo Zhaocheng Wei +2 位作者 Minjie Wang Zhiwei Zhao Shengxian Liu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2023年第6期172-184,共13页
The equipment used in various fields contains an increasing number of parts with curved surfaces of increasing size.Five-axis computer numerical control(CNC)milling is the main parts machining method,while dynamics an... The equipment used in various fields contains an increasing number of parts with curved surfaces of increasing size.Five-axis computer numerical control(CNC)milling is the main parts machining method,while dynamics analysis has always been a research hotspot.The cutting conditions determined by the cutter axis,tool path,and workpiece geometry are complex and changeable,which has made dynamics research a major challenge.For this reason,this paper introduces the innovative idea of applying dimension reduction and mapping to the five-axis machining of curved surfaces,and proposes an efficient dynamics analysis model.To simplify the research object,the cutter position points along the tool path were discretized into inclined plane five-axis machining.The cutter dip angle and feed deflection angle were used to define the spatial position relationship in five-axis machining.These were then taken as the new base variables to construct an abstract two-dimensional space and establish the mapping relationship between the cutter position point and space point sets to further simplify the dimensions of the research object.Based on the in-cut cutting edge solved by the space limitation method,the dynamics of the inclined plane five-axis machining unit were studied,and the results were uniformly stored in the abstract space to produce a database.Finally,the prediction of the milling force and vibration state along the tool path became a data extraction process that significantly improved efficiency.Two experiments were also conducted which proved the accuracy and efficiency of the proposed dynamics analysis model.This study has great potential for the online synchronization of intelligent machining of large surfaces. 展开更多
关键词 Curved surface Five-axis machining Dimension reduction and mapping Milling force DYNAMICS
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Assessment of Lubrication Property and Machining Performance of Nanofluid Composite Electrostatic Spraying(NCES)Using Different Types of Vegetable Oils as Base Fluids of External Fluid
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作者 Yu Su Zepeng Chu +2 位作者 Le Gong Bin Wang Zhiqiang Liu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2023年第4期97-110,共14页
The current study of minimum quantity lubrication(MQL)concentrates on its performance improvement.By contrast with nanofluid MQL and electrostatic atomization(EA),the proposed nanofluid composite electrostatic sprayin... The current study of minimum quantity lubrication(MQL)concentrates on its performance improvement.By contrast with nanofluid MQL and electrostatic atomization(EA),the proposed nanofluid composite electrostatic spraying(NCES)can enhance the performance of MQL more comprehensively.However,it is largely influenced by the base fluid of external fluid.In this paper,the lubrication property and machining performance of NCES with different types of vegetable oils(castor,palm,soybean,rapeseed,and LB2000 oil)as the base fluids of external fluid were compared and evaluated by friction and milling tests under different flow ratios of external and internal fluids.The spraying current and electrowetting angle were tested to analyze the influence of vegetable oil type as the base fluid of external fluid on NCES performances.The friction test results show that relative to NCES with other vegetable oils as the base fluids of external fluid,NCES with LB2000 as the base fluid of external fluid reduced the friction coefficient and wear loss by 9.4%-27.7%and 7.6%-26.5%,respectively.The milling test results display that the milling force and milling temperature for NCES with LB2000 as the base fluid of external fluid were 1.4%-13.2%and 3.6%-11.2%lower than those for NCES with other vegetable oils as the base fluids of external fluid,respectively.When LB2000/multi-walled carbon nanotube(MWCNT)water-based nanofluid was used as the external/internal fluid and the flow ratio of external and internal fluids was 2:1,NCES showed the best milling performance.This study provides theoretical and technical support for the selection of the base fluid of NCES external fluid. 展开更多
关键词 Nanofluid composite electrostatic spraying Lubrication property machining performance Vegetable oil External fluid
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Use of machine learning models for the prognostication of liver transplantation: A systematic review 被引量:1
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作者 Gidion Chongo Jonathan Soldera 《World Journal of Transplantation》 2024年第1期164-188,共25页
BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are p... BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication. 展开更多
关键词 Liver transplantation machine learning models PROGNOSTICATION Allograft allocation Artificial intelligence
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