Digitization precision analysis is an important tool to ensure the design precision of machine tool currently. The correlative research about precision modeling and analysis mainly focuses on the geometry precision an...Digitization precision analysis is an important tool to ensure the design precision of machine tool currently. The correlative research about precision modeling and analysis mainly focuses on the geometry precision and motion precision of machine tool, and the forming motion precision of workpiece surface. For the machine tool with complex forming motion, there is not accurate corresponding relationship between the existing criterion on precision design and the machining precision of workpiece. Therefore, a design scheme on machine tool precision based on error prediction is proposed, which is divided into two-stage digitization precision analysis crucially. The first stage aims at the technology system to complete the precision distribution and inspection from the workpiece to various component parts of technology system and achieve the total output precision of machine tool under the specified machining precision; the second stage aims at the machine tool system to complete the precision distribution and inspection from the output precision of machine tool to the machine tool components. This article serves YK3610 gear hobber as the example to describe the error model of two systems and basic application method, and the practical cutting precision of this machine tool achieves to 5-4-4 grade. The proposed method can provide reliable guidance to the precision design of machine tool with complex forming motion.展开更多
With the continuous improvement of signal processing accuracy requirements in modern electronic systems,the demand for high-precision analog-to-digital converters(ADCs)is increasing.Sigma-Delta modulator,as the most i...With the continuous improvement of signal processing accuracy requirements in modern electronic systems,the demand for high-precision analog-to-digital converters(ADCs)is increasing.Sigma-Delta modulator,as the most important component of high-precision ADC,is widely used in high-quality audio,high-precision instrument measurement,and other fields due to its advantages of high precision,strong noise resistance,and low hardware cost.This article designs a discrete structure third-order four-bit high-precision Sigma-Delta modulator through modeling,with an oversampling rate set to 512.Under ideal conditions,the simulation results show that the SDNR reaches 152.7db and the ENOB is 25.24bits.After introducing non-ideal noise,the system performance has decreased.The simulation results show that the SDNR is as high as 124.5db and the ENOB is 20.39bits.This indicates that the design can achieve high-precision conversion and provide assistance for further research in the future.展开更多
This paper highlights the innovative approach and findings of the recently published study by Xu et al,which underscores the integration of radiomics and clinicoradiological factors to enhance the preoperative predict...This paper highlights the innovative approach and findings of the recently published study by Xu et al,which underscores the integration of radiomics and clinicoradiological factors to enhance the preoperative prediction of microvascular invasion in patients with hepatitis B virus-related hepatocellular carcinoma(HBV-HCC).The study’s use of contrast-enhanced computed tomography radiomics to construct predictive models offers a significant advancement in the surgical planning and management of HBV-HCC,potentially transforming patient outcomes through more personalized treatment strategies.This editorial commends the study's contribution to precision medicine and discusses its implic-ations for future research and clinical practice.展开更多
Ni-based superalloys play a critical role in the aerospace industry due to their exceptional mechanical properties and oxidation resistance.However,the conventional development of new superalloys is often constrained ...Ni-based superalloys play a critical role in the aerospace industry due to their exceptional mechanical properties and oxidation resistance.However,the conventional development of new superalloys is often constrained by lengthy experimental cycles and high costs.To address these challenges,machine learning has emerged as an effective strategy for accelerating alloy design by efficiently exploring composition-property relationship,optimizing processing parameters,and enhancing predictive accuracy.This review summarizes recent progress in applying machine learning to composition optimization and mechanical property prediction of Ni-based superalloys,emphasizing the integration of theoretical modeling and experimental validation.The importance of feature engineering,including data collection,preprocessing,feature construction,and dimensionality reduction,was first highlighted.Subsequently,the machine learning approaches for novel alloy design and prediction of key properties including fatigue resistance,creep resistance,and oxidation resistance were discussed.Through data-driven approaches,machine learning not only enhances predictive capabilities but also uncovers complex composition-property relationship,which accelerates the development of next-generation Ni-based superalloys.We anticipate that the continued advancements in this field will drive more efficient and cost-effective alloy design,ultimately accelerating the transition from computational predictions to experimental realizations.展开更多
To develop more efficient catalysts and discover new materials to work towards efficient solutions to the growing environmental problems,machine learning(ML)offers viable new ideas.Due to its ability to process large-...To develop more efficient catalysts and discover new materials to work towards efficient solutions to the growing environmental problems,machine learning(ML)offers viable new ideas.Due to its ability to process large-scale data and mine underlying patterns,ML has been widely used in the design and development of materials in recent years.The purpose of this manuscript is to summarize the research progress of ML to guide the development of materials in the environmental field and open new horizons for environmental pollution control.This manuscript firstly details the basic ML definitions and operational procedures.Secondly,it summarizes the main ways of applying ML in materials.Then it unfolds to introduce the specific application examples of ML in different materials.Finally,we summarize the shortcomings and research trends of ML in predicting material design.展开更多
The reverse operation of existing centrifugal pumps,commonly referred to as“Pump as Turbine”(PAT),is a key approach for recovering liquid pressure energy.As a type of hydraulic machinery characterized by a simple st...The reverse operation of existing centrifugal pumps,commonly referred to as“Pump as Turbine”(PAT),is a key approach for recovering liquid pressure energy.As a type of hydraulic machinery characterized by a simple structure and user-friendly operation,PAT holds significant promise for application in industrial waste energy recovery systems.This paper reviews recent advancements in this field,with a focus on pump type selection,performance prediction,and optimization design.First,the advantages of various prototype pumps,including centrifugal,axial-flow,mixed-flow,screw,and plunger pumps,are examined in specific application scenarios while analyzing their suitability for turbine operation.Next,performance prediction techniques for PATs are discussed,encompassing theoretical calculations,numerical simulations,and experimental testing.Special emphasis is placed on the crucial role of Computational Fluid Dynamics(CFD)and internal flow field testing technologies in analyzing PAT internal flow characteristics.Additionally,the impact of multi-objective optimization methods and the application of advanced materials on PAT performance enhancement is addressed.Finally,based on current research findings and existing technical challenges,this review also indicates future development directions;in particular,four key breakthrough areas are identified:advanced materials,innovative design methodologies,internal flow diagnostics,and in-depth analysis of critical components.展开更多
In this article,we comment paper by Wang et al published recently.The study represents a notable step in the pursuit of precision medicine for inflammatory bowel diseases,offering valuable insights into the potential ...In this article,we comment paper by Wang et al published recently.The study represents a notable step in the pursuit of precision medicine for inflammatory bowel diseases,offering valuable insights into the potential of noninvasive biomarkers for Crohn’s disease(CD)management.This article highlights the significance of the findings,particularly the identification of albumin and fibrinogen amplitude changes as effective,noninvasive biomarkers for predicting endoscopic improvement in CD.The authors introduce a reliable nomogram model,constructed through careful logistic regression analyses,that demonstrates high predictive accuracy across training,internal validation,and external validation cohorts.With further validation through calibration and decision curve analyses,this model shows its clinical relevance and applicability.By incorporating albumin and fibrinogen fluctuations into clinical decision-making,this model addresses a critical gap in noninvasive monitoring tools for CD,offering a practical,patient-centered alternative to guide therapeutic strategies.These findings not only validate the utility of the model but also pave the way for broader integration of biomarker-driven decision-making in the management of CD.This article discusses the broader implications of these advancements,emphasizing their potential to refine patient care and improve outcomes in CD management.展开更多
This article provides a comprehensive review of the advancements in the application of artificial intelligence(AI)technology in the modernization of traditional Chinese medicine(TCM)compound prescriptions,and emphasiz...This article provides a comprehensive review of the advancements in the application of artificial intelligence(AI)technology in the modernization of traditional Chinese medicine(TCM)compound prescriptions,and emphasizes recent research developments,including intelligent design,prediction of mechanisms of action,and precise application of TCM compound prescriptions.The integration of multi-omics data,deep learning algorithms,and knowledge graph technologies has established novel technical avenues for the modernization research of TCM.This study systematically analyzes the advantages and challenges associated with AI technologies in the research of TCM compound prescriptions,highlighting issues such as data heterogeneity,limited interpretability of AI models,and the absence of standardized procedures.Furthermore,this article examines the prospective developmental trajectories within this field,highlighting the importance of synergistic collaboration between AI and traditional pharmacology to improve the clinical applicability and effectiveness of TCM.The objective is to offer valuable insights into the modernization of TCM driven by AI and to stimulate further research in this area.展开更多
Substantially lightweight brake discs with high wear resistance are highly desirable in the automotive industry.This paper presents an investigation of the precision-engineering design and development of automotive br...Substantially lightweight brake discs with high wear resistance are highly desirable in the automotive industry.This paper presents an investigation of the precision-engineering design and development of automotive brake discs using nonhomogeneous Al/SiC metal-matrixcomposite materials.The design and development are based on modeling and analysis following stringent precision-engineering principles,i.e.,brake-disc systems that operate repeatably and stably over time as enabled by precision-engineering design.The design and development are further supported by tribological experimental testing and finite-element simulations.The results show the industrial feasibility of the innovative design approach and the application merits of using advanced metal-matrix-composite materials for next-generation automotive and electric vehicles.展开更多
The Blended-Wing-Body(BWB) is an unconventional configuration of aircraft and considered as a potential configuration for future commercial aircraft. One of the difficulties in conceptual design of a BWB aircraft is s...The Blended-Wing-Body(BWB) is an unconventional configuration of aircraft and considered as a potential configuration for future commercial aircraft. One of the difficulties in conceptual design of a BWB aircraft is structural mass prediction due to its unique structural feature. This paper presents a structural mass prediction method for conceptual design of BWB aircraft using a structure analysis and optimization method combined with empirical calibrations. The total BWB structural mass is divided into the ideal load-carrying structural mass, non-ideal mass, and secondary structural mass. Structural finite element analysis and optimization are used to predict the ideal primary structural mass, while the non-ideal mass and secondary structural mass are estimated by empirical methods. A BWB commercial aircraft is used to demonstrate the procedure of the BWB structural mass prediction method. The predicted mass of structural components of the BWB aircraft is presented, and the ratios of the structural component mass to the Maximum TakeOff Mass(MTOM) are discussed. It is found that the ratio of the fuselage mass to the MTOM for the BWB aircraft is much higher than that for a conventional commercial aircraft, and the ratio of the wing mass to the MTOM for the BWB aircraft is slightly lower than that for a conventional aircraft.展开更多
The finite element analysis （FEA） software Ansys was employed to study the stress state of the dies of both plane and non-plane parting face structures with uniform interference and the die of plane parting face str...The finite element analysis （FEA） software Ansys was employed to study the stress state of the dies of both plane and non-plane parting face structures with uniform interference and the die of plane parting face structure with non-uniform interference. Considering the symmetry of the die, a half gear tooth model of the two-ring assembled die with 2.5 GPa inner pressure was constructed. Four paths were defined to investigate the stress state at the bottom comer of the die where stress concentration was serious. FEA results show that the change of parting face from non-plane to plane can greatly reduce the stress at the teeth tips of the die so that the tip fracture is avoided. The interference structure of the die is the most important influencing factor for the stress concentration at the bottom comer. When non-uniform interference is adopted the first principal stress at the comer on the defined paths of the die is much lower than that with uniform interference. The bottom hole radius is another important influencing factor for the comer stress concentration. The first principal stress at the comer of the plane parting face die with non-uniform interference is reduced from 2.3 to 1.9 GPa when the hole radius increases from 12.5 to 16.0 mm. The optimization of the die structure increases the life of the die from 100 to 6 000 hits.展开更多
With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of i...With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers.Machine learning,one of the core technologies of artificial intelligence,is rapidly changing many fields with its ability to learn from historical data and solve complex tasks,and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries.This review begins with the introduction of the conceptual framework of machine learning and the general process of its application,then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state,and finally points out the current application problems of machine learning and future research directions.It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.展开更多
Ultra-precision machine tool is the most important physical tool to machining the workpiece with the frequency domain error requirement, in the design process of which the dynamic accuracy design(DAD) is indispensable...Ultra-precision machine tool is the most important physical tool to machining the workpiece with the frequency domain error requirement, in the design process of which the dynamic accuracy design(DAD) is indispensable and the related research is rarely available. In light of above reasons, a DAD method of ultra-precision machine tool is proposed in this paper, which is based on the frequency domain error allocation.The basic procedure and enabling knowledge of the DAD method is introduced. The application case of DAD method in the ultra-precision flycutting machine tool for KDP crystal machining is described to show the procedure detailedly. In this case, the KDP workpiece surface has the requirements in four different spatial frequency bands, and the emphasis for this study is put on the middle-frequency band with the PSD specifications. The results of the application case basically show the feasibility of the proposed DAD method. The DAD method of ultra-precision machine tool can effectively minimize the technical risk and improve the machining reliability of the designed machine tool. This paper will play an important role in the design and manufacture of new ultra-precision machine tool.展开更多
Various kinds of data are used in new product design and more accurate datamake the design results more reliable. Even though part of product data can be available directlyfrom the existing similar products, there sti...Various kinds of data are used in new product design and more accurate datamake the design results more reliable. Even though part of product data can be available directlyfrom the existing similar products, there still leaves a great deal of data unavailable. This makesdata prediction a valuable work. A method that can predict data of product under development basedon the existing similar products is proposed. Fuzzy theory is used to deal with the uncertainties indata prediction process. The proposed method can be used in life cycle design, life cycleassessment (LCA) etc. Case study on current refrigerator is used as a demonstration example.展开更多
Inertial system platforms are a kind of important precision devices,which have the characteristics of difficult acquisition for state data and small sample scale.Focusing on the model optimization for data-driven faul...Inertial system platforms are a kind of important precision devices,which have the characteristics of difficult acquisition for state data and small sample scale.Focusing on the model optimization for data-driven fault state prediction and quantitative degreemeasurement,a fast small-sample supersphere one-class SVMmodelingmethod using support vectors pre-selection is systematically studied in this paper.By theorem-proving the irrelevance between themodel’s learning result and the non-support vectors(NSVs),the distribution characters of the support vectors are analyzed.On this basis,a modeling method with selected samples having specific geometry character fromthe training sets is also proposed.The method can remarkably eliminate theNSVs and improve the algorithm’s efficiency.The experimental results testify that the scale of training samples and the modeling time consumption both give a sharply decrease using the support vectors pre-selection method.The experimental results on inertial devices also show good fault prediction capability and effectiveness of quantitative anomaly measurement.展开更多
According to the characteristic of the sensor inertia, the dynamic prediction to improve the system dynamic precision is presented in this paper. With the recurrence calculation of time constant of the sensor, the sys...According to the characteristic of the sensor inertia, the dynamic prediction to improve the system dynamic precision is presented in this paper. With the recurrence calculation of time constant of the sensor, the system dynamic precision is greatly improved. The example using this method is given.展开更多
Polylactic acid(PLA)is a potential polymer material used as a substitute for traditional plastics,and the accurate molecular weight distribution range of PLA is strictly required in practical applications.Therefore,ex...Polylactic acid(PLA)is a potential polymer material used as a substitute for traditional plastics,and the accurate molecular weight distribution range of PLA is strictly required in practical applications.Therefore,exploring the relationship between synthetic conditions and PLA molecular weight is crucially important.In this work,direct polycondensation combined with overlay sampling uniform design(OSUD)was applied to synthesize the low molecular weight PLA.Then a multiple regression model and two artificial neural network models on PLA molecular weight versus reaction temperature,reaction time,and catalyst dosage were developed for PLA molecular weight prediction.The characterization results indicated that the low molecular weight PLA was efficiently synthesized under this method.Meanwhile,the experimental dataset acquired from OSUD successfully established three predictive models for PLA molecular weight.Among them,both artificial neural network models had significantly better predictive performance than the regression model.Notably,the radial basis function neural network model had the best predictive accuracy with only 11.9%of mean relative error on the validation dataset,which improved by 67.7%compared with the traditional multiple regression model.This work successfully predicted PLA molecular weight in a direct polycondensation process using artificial neural network models combined with OSUD,which provided guidance for the future implementation of molecular weight-controlled polymer's synthesis.展开更多
This article discusses some views on the relationship between carrying out and applying standards and precision design and the teaching of a course on interchangeability and measurement techniques. It points out that ...This article discusses some views on the relationship between carrying out and applying standards and precision design and the teaching of a course on interchangeability and measurement techniques. It points out that while emphasizing precision design, we should not underrate the significance of interchangeability and standardization. Although there are presently many teaching models available for such courses, each course should be designed separately to preserve its systematic character and integrality. As well, the development of students' abilities in precision design and the application of standards should be strengthened in experimental lessons within each course.展开更多
The structure stiffness of presses has great effects on the forming precision of workpieces, especially in near-net or net shape forming. Conventionally the stiffness specification of presses is empirically determined...The structure stiffness of presses has great effects on the forming precision of workpieces, especially in near-net or net shape forming. Conventionally the stiffness specification of presses is empirically determined, resulting in poor designs with insufficient or over sufficient stiffness of press structures. In this paper, an approach for the structure design of hydraulic presses is proposed, which is forming-precision-driven and can make presses costeffective by lightweight optimization. The approach consists of five steps:(1)the determination of the press stiffness specification in terms of the forming precision requirement of workpieces;(2)the conceptual design of the press structures according to the stiffness and workspace specifications, and the structure configuration of the press;(3)the prototype design of the press structures by equivalently converting the conceptual design to prototypes;(4)the selection of key structure parameters by sensitivity analysis of the prototype design; and(5)the optimization of the prototype design. The approach is demonstrated and validated through a case study of the structure design of a 100 MN hydraulic press.展开更多
基金supported by National Natural Science Foundation of China (Grant No. 51075419)Chongqing Municipal Natural Science Foundation of China (Grant No. CSTC,2009BB3234)
文摘Digitization precision analysis is an important tool to ensure the design precision of machine tool currently. The correlative research about precision modeling and analysis mainly focuses on the geometry precision and motion precision of machine tool, and the forming motion precision of workpiece surface. For the machine tool with complex forming motion, there is not accurate corresponding relationship between the existing criterion on precision design and the machining precision of workpiece. Therefore, a design scheme on machine tool precision based on error prediction is proposed, which is divided into two-stage digitization precision analysis crucially. The first stage aims at the technology system to complete the precision distribution and inspection from the workpiece to various component parts of technology system and achieve the total output precision of machine tool under the specified machining precision; the second stage aims at the machine tool system to complete the precision distribution and inspection from the output precision of machine tool to the machine tool components. This article serves YK3610 gear hobber as the example to describe the error model of two systems and basic application method, and the practical cutting precision of this machine tool achieves to 5-4-4 grade. The proposed method can provide reliable guidance to the precision design of machine tool with complex forming motion.
文摘With the continuous improvement of signal processing accuracy requirements in modern electronic systems,the demand for high-precision analog-to-digital converters(ADCs)is increasing.Sigma-Delta modulator,as the most important component of high-precision ADC,is widely used in high-quality audio,high-precision instrument measurement,and other fields due to its advantages of high precision,strong noise resistance,and low hardware cost.This article designs a discrete structure third-order four-bit high-precision Sigma-Delta modulator through modeling,with an oversampling rate set to 512.Under ideal conditions,the simulation results show that the SDNR reaches 152.7db and the ENOB is 25.24bits.After introducing non-ideal noise,the system performance has decreased.The simulation results show that the SDNR is as high as 124.5db and the ENOB is 20.39bits.This indicates that the design can achieve high-precision conversion and provide assistance for further research in the future.
基金Supported by Shandong Province Medical and Health Science and Technology Development Plan Project,No.202203030713Science and Technology Program of Yantai Affiliated Hospital of Binzhou Medical University,No.YTFY2022KYQD06。
文摘This paper highlights the innovative approach and findings of the recently published study by Xu et al,which underscores the integration of radiomics and clinicoradiological factors to enhance the preoperative prediction of microvascular invasion in patients with hepatitis B virus-related hepatocellular carcinoma(HBV-HCC).The study’s use of contrast-enhanced computed tomography radiomics to construct predictive models offers a significant advancement in the surgical planning and management of HBV-HCC,potentially transforming patient outcomes through more personalized treatment strategies.This editorial commends the study's contribution to precision medicine and discusses its implic-ations for future research and clinical practice.
基金financially supported by the National Natural Science Foundation of China(Nos.52201203 and 52471004)the Fundamental Research Funds for the Central Universities(No.N2423030)the Science and Technology Project of Hebei Education Department(No.QN2023155).
文摘Ni-based superalloys play a critical role in the aerospace industry due to their exceptional mechanical properties and oxidation resistance.However,the conventional development of new superalloys is often constrained by lengthy experimental cycles and high costs.To address these challenges,machine learning has emerged as an effective strategy for accelerating alloy design by efficiently exploring composition-property relationship,optimizing processing parameters,and enhancing predictive accuracy.This review summarizes recent progress in applying machine learning to composition optimization and mechanical property prediction of Ni-based superalloys,emphasizing the integration of theoretical modeling and experimental validation.The importance of feature engineering,including data collection,preprocessing,feature construction,and dimensionality reduction,was first highlighted.Subsequently,the machine learning approaches for novel alloy design and prediction of key properties including fatigue resistance,creep resistance,and oxidation resistance were discussed.Through data-driven approaches,machine learning not only enhances predictive capabilities but also uncovers complex composition-property relationship,which accelerates the development of next-generation Ni-based superalloys.We anticipate that the continued advancements in this field will drive more efficient and cost-effective alloy design,ultimately accelerating the transition from computational predictions to experimental realizations.
基金the National Natural Science Foundation of China(Nos.52370083 and 52170088)Sichuan Science and Technology Program(No.2024NSFTD0014)Key R&D Program of Heilongjiang Province(No.2023ZX02C01)for financial support。
文摘To develop more efficient catalysts and discover new materials to work towards efficient solutions to the growing environmental problems,machine learning(ML)offers viable new ideas.Due to its ability to process large-scale data and mine underlying patterns,ML has been widely used in the design and development of materials in recent years.The purpose of this manuscript is to summarize the research progress of ML to guide the development of materials in the environmental field and open new horizons for environmental pollution control.This manuscript firstly details the basic ML definitions and operational procedures.Secondly,it summarizes the main ways of applying ML in materials.Then it unfolds to introduce the specific application examples of ML in different materials.Finally,we summarize the shortcomings and research trends of ML in predicting material design.
基金supported by Science and Technology Project of Quzhou(Nos.2023K256,2023NC08,2022K41)Research Grants Program of Department of Education of Zhejiang Province(Nos.Y202455709,Y202456243)Hunan Province Key Field R&D Plan Project(No.2022GK2068).
文摘The reverse operation of existing centrifugal pumps,commonly referred to as“Pump as Turbine”(PAT),is a key approach for recovering liquid pressure energy.As a type of hydraulic machinery characterized by a simple structure and user-friendly operation,PAT holds significant promise for application in industrial waste energy recovery systems.This paper reviews recent advancements in this field,with a focus on pump type selection,performance prediction,and optimization design.First,the advantages of various prototype pumps,including centrifugal,axial-flow,mixed-flow,screw,and plunger pumps,are examined in specific application scenarios while analyzing their suitability for turbine operation.Next,performance prediction techniques for PATs are discussed,encompassing theoretical calculations,numerical simulations,and experimental testing.Special emphasis is placed on the crucial role of Computational Fluid Dynamics(CFD)and internal flow field testing technologies in analyzing PAT internal flow characteristics.Additionally,the impact of multi-objective optimization methods and the application of advanced materials on PAT performance enhancement is addressed.Finally,based on current research findings and existing technical challenges,this review also indicates future development directions;in particular,four key breakthrough areas are identified:advanced materials,innovative design methodologies,internal flow diagnostics,and in-depth analysis of critical components.
文摘In this article,we comment paper by Wang et al published recently.The study represents a notable step in the pursuit of precision medicine for inflammatory bowel diseases,offering valuable insights into the potential of noninvasive biomarkers for Crohn’s disease(CD)management.This article highlights the significance of the findings,particularly the identification of albumin and fibrinogen amplitude changes as effective,noninvasive biomarkers for predicting endoscopic improvement in CD.The authors introduce a reliable nomogram model,constructed through careful logistic regression analyses,that demonstrates high predictive accuracy across training,internal validation,and external validation cohorts.With further validation through calibration and decision curve analyses,this model shows its clinical relevance and applicability.By incorporating albumin and fibrinogen fluctuations into clinical decision-making,this model addresses a critical gap in noninvasive monitoring tools for CD,offering a practical,patient-centered alternative to guide therapeutic strategies.These findings not only validate the utility of the model but also pave the way for broader integration of biomarker-driven decision-making in the management of CD.This article discusses the broader implications of these advancements,emphasizing their potential to refine patient care and improve outcomes in CD management.
文摘This article provides a comprehensive review of the advancements in the application of artificial intelligence(AI)technology in the modernization of traditional Chinese medicine(TCM)compound prescriptions,and emphasizes recent research developments,including intelligent design,prediction of mechanisms of action,and precise application of TCM compound prescriptions.The integration of multi-omics data,deep learning algorithms,and knowledge graph technologies has established novel technical avenues for the modernization research of TCM.This study systematically analyzes the advantages and challenges associated with AI technologies in the research of TCM compound prescriptions,highlighting issues such as data heterogeneity,limited interpretability of AI models,and the absence of standardized procedures.Furthermore,this article examines the prospective developmental trajectories within this field,highlighting the importance of synergistic collaboration between AI and traditional pharmacology to improve the clinical applicability and effectiveness of TCM.The objective is to offer valuable insights into the modernization of TCM driven by AI and to stimulate further research in this area.
文摘Substantially lightweight brake discs with high wear resistance are highly desirable in the automotive industry.This paper presents an investigation of the precision-engineering design and development of automotive brake discs using nonhomogeneous Al/SiC metal-matrixcomposite materials.The design and development are based on modeling and analysis following stringent precision-engineering principles,i.e.,brake-disc systems that operate repeatably and stably over time as enabled by precision-engineering design.The design and development are further supported by tribological experimental testing and finite-element simulations.The results show the industrial feasibility of the innovative design approach and the application merits of using advanced metal-matrix-composite materials for next-generation automotive and electric vehicles.
基金supported by the National Natural Science Foundation of China (No. 11432007)
文摘The Blended-Wing-Body(BWB) is an unconventional configuration of aircraft and considered as a potential configuration for future commercial aircraft. One of the difficulties in conceptual design of a BWB aircraft is structural mass prediction due to its unique structural feature. This paper presents a structural mass prediction method for conceptual design of BWB aircraft using a structure analysis and optimization method combined with empirical calibrations. The total BWB structural mass is divided into the ideal load-carrying structural mass, non-ideal mass, and secondary structural mass. Structural finite element analysis and optimization are used to predict the ideal primary structural mass, while the non-ideal mass and secondary structural mass are estimated by empirical methods. A BWB commercial aircraft is used to demonstrate the procedure of the BWB structural mass prediction method. The predicted mass of structural components of the BWB aircraft is presented, and the ratios of the structural component mass to the Maximum TakeOff Mass(MTOM) are discussed. It is found that the ratio of the fuselage mass to the MTOM for the BWB aircraft is much higher than that for a conventional commercial aircraft, and the ratio of the wing mass to the MTOM for the BWB aircraft is slightly lower than that for a conventional aircraft.
基金Project(2006BAF04B06) supported by the National Key Technology R & D Program of ChinaProject(2005AA101B19) supported by the Key Technology R & D Program of Hubei Province, China
文摘The finite element analysis （FEA） software Ansys was employed to study the stress state of the dies of both plane and non-plane parting face structures with uniform interference and the die of plane parting face structure with non-uniform interference. Considering the symmetry of the die, a half gear tooth model of the two-ring assembled die with 2.5 GPa inner pressure was constructed. Four paths were defined to investigate the stress state at the bottom comer of the die where stress concentration was serious. FEA results show that the change of parting face from non-plane to plane can greatly reduce the stress at the teeth tips of the die so that the tip fracture is avoided. The interference structure of the die is the most important influencing factor for the stress concentration at the bottom comer. When non-uniform interference is adopted the first principal stress at the comer on the defined paths of the die is much lower than that with uniform interference. The bottom hole radius is another important influencing factor for the comer stress concentration. The first principal stress at the comer of the plane parting face die with non-uniform interference is reduced from 2.3 to 1.9 GPa when the hole radius increases from 12.5 to 16.0 mm. The optimization of the die structure increases the life of the die from 100 to 6 000 hits.
基金financial supports from the National Key Research and Development Program of China(2018YFA0209600)the Natural Science Foundation of China(22022813,21878268,52075481)。
文摘With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers.Machine learning,one of the core technologies of artificial intelligence,is rapidly changing many fields with its ability to learn from historical data and solve complex tasks,and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries.This review begins with the introduction of the conceptual framework of machine learning and the general process of its application,then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state,and finally points out the current application problems of machine learning and future research directions.It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.
基金Supported by Zhejiang Provincial Natural Science Foundation of China(Grant No.LQ16E050012)National Natural Science Foundation of China(Grant Nos.51705462 and 51275115)International Science and Technology Cooperation Program of China(Grant No.2015DFA70630)
文摘Ultra-precision machine tool is the most important physical tool to machining the workpiece with the frequency domain error requirement, in the design process of which the dynamic accuracy design(DAD) is indispensable and the related research is rarely available. In light of above reasons, a DAD method of ultra-precision machine tool is proposed in this paper, which is based on the frequency domain error allocation.The basic procedure and enabling knowledge of the DAD method is introduced. The application case of DAD method in the ultra-precision flycutting machine tool for KDP crystal machining is described to show the procedure detailedly. In this case, the KDP workpiece surface has the requirements in four different spatial frequency bands, and the emphasis for this study is put on the middle-frequency band with the PSD specifications. The results of the application case basically show the feasibility of the proposed DAD method. The DAD method of ultra-precision machine tool can effectively minimize the technical risk and improve the machining reliability of the designed machine tool. This paper will play an important role in the design and manufacture of new ultra-precision machine tool.
基金This project is supported by Ministry of Education, Culture, Sports, Science and Technology (MONBUSHO), Japan.
文摘Various kinds of data are used in new product design and more accurate datamake the design results more reliable. Even though part of product data can be available directlyfrom the existing similar products, there still leaves a great deal of data unavailable. This makesdata prediction a valuable work. A method that can predict data of product under development basedon the existing similar products is proposed. Fuzzy theory is used to deal with the uncertainties indata prediction process. The proposed method can be used in life cycle design, life cycleassessment (LCA) etc. Case study on current refrigerator is used as a demonstration example.
基金the National Natural Science Foundation of China(Grant No.61403397)the Natural Science Basic Research Plan in Shaanxi Province of China(Grant Nos.2020JM-358,2015JM6313).
文摘Inertial system platforms are a kind of important precision devices,which have the characteristics of difficult acquisition for state data and small sample scale.Focusing on the model optimization for data-driven fault state prediction and quantitative degreemeasurement,a fast small-sample supersphere one-class SVMmodelingmethod using support vectors pre-selection is systematically studied in this paper.By theorem-proving the irrelevance between themodel’s learning result and the non-support vectors(NSVs),the distribution characters of the support vectors are analyzed.On this basis,a modeling method with selected samples having specific geometry character fromthe training sets is also proposed.The method can remarkably eliminate theNSVs and improve the algorithm’s efficiency.The experimental results testify that the scale of training samples and the modeling time consumption both give a sharply decrease using the support vectors pre-selection method.The experimental results on inertial devices also show good fault prediction capability and effectiveness of quantitative anomaly measurement.
文摘According to the characteristic of the sensor inertia, the dynamic prediction to improve the system dynamic precision is presented in this paper. With the recurrence calculation of time constant of the sensor, the system dynamic precision is greatly improved. The example using this method is given.
基金funded by the Zhejiang Provincial Natural Science Foundation of China(LD21B060001)the National Natural Science Foundation of China(22078296,21576240).
文摘Polylactic acid(PLA)is a potential polymer material used as a substitute for traditional plastics,and the accurate molecular weight distribution range of PLA is strictly required in practical applications.Therefore,exploring the relationship between synthetic conditions and PLA molecular weight is crucially important.In this work,direct polycondensation combined with overlay sampling uniform design(OSUD)was applied to synthesize the low molecular weight PLA.Then a multiple regression model and two artificial neural network models on PLA molecular weight versus reaction temperature,reaction time,and catalyst dosage were developed for PLA molecular weight prediction.The characterization results indicated that the low molecular weight PLA was efficiently synthesized under this method.Meanwhile,the experimental dataset acquired from OSUD successfully established three predictive models for PLA molecular weight.Among them,both artificial neural network models had significantly better predictive performance than the regression model.Notably,the radial basis function neural network model had the best predictive accuracy with only 11.9%of mean relative error on the validation dataset,which improved by 67.7%compared with the traditional multiple regression model.This work successfully predicted PLA molecular weight in a direct polycondensation process using artificial neural network models combined with OSUD,which provided guidance for the future implementation of molecular weight-controlled polymer's synthesis.
文摘This article discusses some views on the relationship between carrying out and applying standards and precision design and the teaching of a course on interchangeability and measurement techniques. It points out that while emphasizing precision design, we should not underrate the significance of interchangeability and standardization. Although there are presently many teaching models available for such courses, each course should be designed separately to preserve its systematic character and integrality. As well, the development of students' abilities in precision design and the application of standards should be strengthened in experimental lessons within each course.
基金Supported by the National Natural Science Foundation of China(No.50805101 and No.51275347)the National Key S&T Special Projects of China on CNC Machine Tools and Fundamental Manufacturing Equipment(No.2010ZX04001-191 and No.2011ZX04002-032)the Science and Technology R&D Program of Tianjin(No.13JCZDJC35000 and No.12ZCDZGX45000)
文摘The structure stiffness of presses has great effects on the forming precision of workpieces, especially in near-net or net shape forming. Conventionally the stiffness specification of presses is empirically determined, resulting in poor designs with insufficient or over sufficient stiffness of press structures. In this paper, an approach for the structure design of hydraulic presses is proposed, which is forming-precision-driven and can make presses costeffective by lightweight optimization. The approach consists of five steps:(1)the determination of the press stiffness specification in terms of the forming precision requirement of workpieces;(2)the conceptual design of the press structures according to the stiffness and workspace specifications, and the structure configuration of the press;(3)the prototype design of the press structures by equivalently converting the conceptual design to prototypes;(4)the selection of key structure parameters by sensitivity analysis of the prototype design; and(5)the optimization of the prototype design. The approach is demonstrated and validated through a case study of the structure design of a 100 MN hydraulic press.
