Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Ext...Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.展开更多
The structure and the production process for flying shear machine are introduced first.Then,a quintic polynomial is applied to the design of an electronic cam system for the rotary knife axis in short materials cuttin...The structure and the production process for flying shear machine are introduced first.Then,a quintic polynomial is applied to the design of an electronic cam system for the rotary knife axis in short materials cutting.The dimensionless equation for a quintic polynomial cam curve is deduced.Finally,the curve is plotted with the cam constructor integrated into Siemens engineering development software SCOUT and it is tested with a laboratory platform,which consists of a motion controller SIMOTION and motor drivers SINAMICS S120.The results show that the running stability of the flying shear machine and the position control accuracy of the rotary knife can be effectively improved by using the curve designed in this paper.展开更多
In geotechnical and tunneling engineering,accurately determining the mechanical properties of jointed rock holds great significance for project safety assessments.Peak shear strength(PSS),being the paramount mechanica...In geotechnical and tunneling engineering,accurately determining the mechanical properties of jointed rock holds great significance for project safety assessments.Peak shear strength(PSS),being the paramount mechanical property of joints,has been a focal point in the research field.There are limitations in the current peak shear strength(PSS)prediction models for jointed rock:(i)the models do not comprehensively consider various influencing factors,and a PSS prediction model covering seven factors has not been established,including the sampling interval of the joints,the surface roughness of the joints,the normal stress,the basic friction angle,the uniaxial tensile strength,the uniaxial compressive strength,and the joint size for coupled joints;(ii)the datasets used to train the models are relatively limited;and(iii)there is a controversy regarding whether compressive or tensile strength should be used as the strength term among the influencing factors.To overcome these limitations,we developed four machine learning models covering these seven influencing factors,three relying on Support Vector Regression(SVR)with different kernel functions(linear,polynomial,and Radial Basis Function(RBF))and one using deep learning(DL).Based on these seven influencing factors,we compiled a dataset comprising the outcomes of 493 published direct shear tests for the training and validation of these four models.We compared the prediction performance of these four machine learning models with Tang’s and Tatone’s models.The prediction errors of Tang’s and Tatone’s models are 21.8%and 17.7%,respectively,while SVR_linear is at 16.6%,SVR_poly is at 14.0%,and SVR_RBF is at 12.1%.DL outperforms the two existing models with only an 8.5%error.Additionally,we performed shear tests on granite joints to validate the predictive capability of the DL-based model.With the DL approach,the results suggest that uniaxial tensile strength is recommended as the material strength term in the PSS model for more reliable outcomes.展开更多
Shear deformation mechanisms of diamond-like carbon(DLC)are commonly unclear since its thickness of several micrometers limits the detailed analysis of its microstructural evolution and mechanical performance,which fu...Shear deformation mechanisms of diamond-like carbon(DLC)are commonly unclear since its thickness of several micrometers limits the detailed analysis of its microstructural evolution and mechanical performance,which further influences the improvement of the friction and wear performance of DLC.This study aims to investigate this issue utilizing molecular dynamics simulation and machine learning(ML)techniques.It is indicated that the changes in the mechanical properties of DLC are mainly due to the expansion and reduction of sp3 networks,causing the stick-slip patterns in shear force.In addition,cluster analysis showed that the sp2-sp3 transitions arise in the stick stage,while the sp3-sp2 transitions occur in the slip stage.In order to analyze the mechanisms governing the bond breaking/re-formation in these transitions,the Random Forest(RF)model in ML identifies that the kinetic energies of sp3 atoms and their velocities along the loading direction have the highest influence.This is because high kinetic energies of atoms can exacerbate the instability of the bonding state and increase the probability of bond breaking/re-formation.Finally,the RF model finds that the shear force of DLC is highly correlated to its potential energy,with less correlation to its content of sp3 atoms.Since the changes in potential energy are caused by the variances in the content of sp3 atoms and localized strains,potential energy is an ideal parameter to evaluate the shear deformation of DLC.The results can enhance the understanding of the shear deformation of DLC and support the improvement of its frictional and wear performance.展开更多
Investigations made by the authors and collaborators into the microstructural and fracture aspects of adiabatic shear bands （ASBs） of the hardened steels and Ti alloys induced by high speed machining （HSM） are bri...Investigations made by the authors and collaborators into the microstructural and fracture aspects of adiabatic shear bands （ASBs） of the hardened steels and Ti alloys induced by high speed machining （HSM） are briefly reviewed. The principal findings are the following： （a） the microstructure inside the ASBs varies from the band center to the normal chip material, the gradient microstructures are found; （b） the HSM can produce two types of ASBs with increasing in cutting speed, the deformed shear bands formed at lower cutting speed and the transformed shear bands formed at higher cutting speed; （c） the very small equiaxed recrystallized grains are observed in the center of the ASBs, the dynamic recrystallization and phase transformation may occur simultaneously during the formation of the transformed ASBs; （d） The dynamic rotational recrystallization is the origin of the equiaxed grains in the center of the ASBs. A microstructural evolution model in ASBs produced during HSM for the harden steel is proposed; （e） the microstructural pattern of fracture surface is characterised by the elongated dimples. A microcosmic adiabatic shear fracture model during HSM of the hardened steel is built up.展开更多
As the cutting speed goes higher, the mechanism of chip deformation will be changed significantly, i.e., continuous chip in low cutting speed will shift to serrated chip with shear localization. For the shear localize...As the cutting speed goes higher, the mechanism of chip deformation will be changed significantly, i.e., continuous chip in low cutting speed will shift to serrated chip with shear localization. For the shear localized chip, the parameters used to assess the chip deformation for continuous chip, such as shorten coefficient ξ, shear angle φ and shear strain ε, can not describe the chip deformation correctly or comprehensively. This paper deals with the assessment of chip deformation of shear localization. There are two deformation regions in shear localized chip, one is the chip segment body with relative smaller plastic deformation, another one is the boundary between segments with shear localization, so called shear band. Considering the two distinct deformation regions, two parameters are used to define their deformation respectively. According to the analysis of chip formation process, the equations have been deduced to calculate the shear strains of shear band ε, shear strain of chip segment ε 1 and shear rate so that the shear localized chip deformation can be assessed correctly and comprehensively. By use of this assessment, the chip deformation in machining selenium treated stainless steel (STSS) and common stainless steel at various cutting conditions is investigated. The experiment results obtained by the machining of stainless steel prove that: (1) the shear strain and strain rate increase with the increasing of cutting speed; (2) the shear strain in shear band can be over 10 when cutting speed exceeding 200 m/min for both types of stainless steel, and it is much higher than the strain of chip segment. The difference will be enlarged as the cutting speed increasing; (3) As the comparison, the shear strain for the STSS is a little lower than that for JIS304; (4) The stain rate is extremely high (= 2.5×10 5 1/s ). In range of cutting speed less than 180 m/min, the strain rate for STSS is lower than that for JIS304. However, when the cutting speed is higher than 180 m/min, the strain rate for STSS is higher than that for JIS304.展开更多
Mar-M247 is a nickel-based alloy which is well known as difficult-to-machine material due to its characteristics of high strength, poor thermal diffusion and work hardening. Calculation of shear stress by an analytica...Mar-M247 is a nickel-based alloy which is well known as difficult-to-machine material due to its characteristics of high strength, poor thermal diffusion and work hardening. Calculation of shear stress by an analytical force model to indicate the effect of coating material, cutting speed, feed rate on tool life and surface roughness was conducted experimentally. Cutting tests were performed using round inserts, with cutting speeds ranging from 50 to 300 rn/min, and feed rates from 0.1 to 0.4 mm/tooth, without using cooling liquids. The behavior of the TiN and TiCN layers using various cutting conditions was analyzed with orthogonal machining force model. Cutting results indicate that different coated tools, together with cutting variables, play a significant role in determining the machinability when milling Mar-M247.展开更多
Earthquakes can cause violent liquefaction of the soil, resulting in unstable foundations that can cause serious damage to facilities such as buildings, roads, and dikes. This is a primary cause of major earthquake di...Earthquakes can cause violent liquefaction of the soil, resulting in unstable foundations that can cause serious damage to facilities such as buildings, roads, and dikes. This is a primary cause of major earthquake disasters. Therefore, the discrimination and prediction of earthquake-induced soil liquefaction has been a hot issue in geohazard research. The soil liquefaction assessment is an integral part of engineering practice. This paper evaluated a dataset of 435 seismic sand liquefaction events using machine learning algorithms. The dataset was analyzed using seven potential assessment parameters. Ten machine learning algorithms are evaluated for their ability to assess seismic sand liquefaction potential, including Linear Discriminant Analysis(LDA), Quadratic Discriminant Analysis(QDA), Naive Bayes(NB), KNearest Neighbor(KNN), Artificial Neural Network(ANN), Classification Tree(CT), Support Vector Machine(SVM), Random Forest(RF), e Xtreme Gradient Boosting(XGBoost), Light Gradient Boosting Machine(Light GBM). A 10-fold cross-validation(CV) method was used in the modeling process to verify the predictive performance of the machine learning models. The final percentages of significant parameters that influenced the prediction results were obtained as Cyclic Stress Ratio(CSR) and Shear-Wave Velocity( VS1) with 56% and 38%, respectively. The final machine learning algorithms identified as suitable for seismic sand liquefaction assessment were the CT, RF, XGBoost algorithms, with the RF algorithm performing best.展开更多
Knowledge about the spatial distribution of the soil physical and mechanical properties is crucial for soil management,water yield,and sustainability at the watershed scale;however,the lack of soil data hinders the ap...Knowledge about the spatial distribution of the soil physical and mechanical properties is crucial for soil management,water yield,and sustainability at the watershed scale;however,the lack of soil data hinders the application of this tool,thus urging the need to estimate soil properties and consequently,to perform the spatial distribution.This research attempted to examine the proficiency of three machine learning methods(RF:Random Forest;Cubist:Regression Tree;and SVM:Support Vector Machine)to predict soil physical and mechanical properties,saturated hydraulic conductivity(Ks),Cohesion measured by fall-cone at the saturated(Psat)and dry(Pdry)states,hardness index(HI)and dry shear strength(SS)by integrating environmental variables and soil features in the Zayandeh-Rood dam watershed,central Iran.To determine the best combination of input variables,three scenarios were examined as follows:scenarioⅠ,terrain attributes derivative from a digital elevation model(DEM)+remotely sensed data;scenarioⅡ,covariates of scenarioⅠ+selected climatic data and some thematic maps;scenarioⅢ,covariates in scenarioⅡ+intrinsic soil properties(Clay,Silt,Sand,bulk density(BD),soil organic matter(SOM),calcium carbonate equivalent(CCE),mean weight diameter(MWD)and geometric weight diameter(GWD)).The results showed that for Ks,Psat Pdry and SS,the best performance was found by the RF model in the third scenario,with R2=0.53,0.32,0.31 and 0.41,respectively,while for soil hardness index(HI),Cubist model in the third scenario with R2=0.25 showed the highest performance.For predicting Ks and Psat,soil characteristics(i.e.clay and soil SOM and BD),and land use were the most important variables.For predicting Pdry,HI,and SS,some topographical characteristics(Valley depth,catchment area,mltiresolution of ridge top flatness index),and some soil characteristics(i.e.clay,SOM and MWD)were the most important input variables.The results of this research present moderate accuracy,however,the methodology employed provides quick and costeffective information serving as the scientific basis for decision-making goals.展开更多
Artificial intelligence(AI)is revolutionizing medical imaging,particularly in chronic liver diseases assessment.AI technologies,including machine learning and deep learning,are increasingly integrated with multiparame...Artificial intelligence(AI)is revolutionizing medical imaging,particularly in chronic liver diseases assessment.AI technologies,including machine learning and deep learning,are increasingly integrated with multiparametric ultrasound(US)techniques to provide more accurate,objective,and non-invasive evaluations of liver fibrosis and steatosis.Analyzing large datasets from US images,AI enhances diagnostic precision,enabling better quantification of liver stiffness and fat content,which are essential for diagnosing and staging liver fibrosis and steatosis.Combining advanced US modalities,such as elastography and doppler imaging with AI,has demonstrated improved sensitivity in identifying different stages of liver disease and distinguishing various degrees of steatotic liver.These advancements also contribute to greater reproducibility and reduced operator dependency,addressing some of the limitations of traditional methods.The clinical implications of AI in liver disease are vast,ranging from early detection to predicting disease progression and evaluating treatment response.Despite these promising developments,challenges such as the need for large-scale datasets,algorithm transparency,and clinical validation remain.The aim of this review is to explore the current applications and future potential of AI in liver fibrosis and steatosis assessment using multiparametric US,highlighting the technological advances and clinical relevance of this emerging field.展开更多
Accurate prediction of the shear strength of rock joints is crucial for assessing the stability of civil and mining engineering projects.Traditional methods for determining the shear strength of rock joints are time-c...Accurate prediction of the shear strength of rock joints is crucial for assessing the stability of civil and mining engineering projects.Traditional methods for determining the shear strength of rock joints are time-consuming,costly,and computationally complex.Machine learning methods,which are driven by data,provide a costeffective and rapid approach to predicting rock joint shear strength,overcoming the limitations of traditional techniques.This study employs nine machine learning models:eXtreme gradient boosting(XGBoost),random forest(RF),Support vector regression(SVR),decision tree(DT),Gaussian process regression(GPR),K-nearest neighbors(KNN),categorical boosting(CatBoost),extreme learning machine(ELM),and adaptive boosting(AdaBoost).A dataset of 288 data points was compiled from an extensive set of literature.Five input features,namely,normal stress,uniaxial compressive strength,Young’s modulus,joint roughness coefficient(JRC),and specimen length,were selected,with shear strength of the rock joints as the output variable.The performance of the nine ML models was assessed using the root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute error(MAE).Due to its unique ordered boosting mechanism and symmetric tree structure,Cat-Boost outperformed the other models,achieving RMSE,R^(2),and MAE values of 0.4663,0.9765,and 0.3508,respectively.Compared with the experimental results,the model yielded a mean square error(MSE)of 0.0360.The proposed ML method offers a cost-effective and efficient solution for predicting rock joint shear strength.展开更多
There have been various theoretical attempts by researchers worldwide to link up different scales of plasticity studies from the nano-, micro- and macro-scale of observation, based on molecular dynamics, crystal plast...There have been various theoretical attempts by researchers worldwide to link up different scales of plasticity studies from the nano-, micro- and macro-scale of observation, based on molecular dynamics, crystal plasticity and continuum mechanics. Very few attempts, however, have been reported in ultra-precision machining studies. A mesoplasticity approach advocated by Lee and Yang is adopted by the authors and is successfully applied to studies of the micro-cutting mechanisms in ultra-precision machining. Traditionally, the shear angle in metal cutting, as well as the cutting force variation, can only be determined from cutting tests. In the pioneering work of the authors, the use of mesoplasticity theory enables prediction of the fluctuation of the shear angle and micro-cutting force, shear band formation, chip morphology in diamond turning and size effect in nano-indentation. These findings are verified by experiments. The mesoplasticity formulation opens up a new direction of studies to enable how the plastic behaviour of materials and their constitutive representations in deformation processing, such as machining can be predicted, assessed and deduced from the basic properties of the materials measurable at the microscale.展开更多
Accurate measurement of acoustic velocities of sedimentary rocks is essential for prediction of rock elastic constants and well failure analysis during drilling operations.Direct measurement by advanced logging tools ...Accurate measurement of acoustic velocities of sedimentary rocks is essential for prediction of rock elastic constants and well failure analysis during drilling operations.Direct measurement by advanced logging tools such as dipole sonic imager is not always possible.For older wells,such data are not available in most cases.Therefore,it is an alternate way to develop a reliable correlation to estimate the shear wave velocity from existing log and/or core data.The objective of this research is to investigate the nature of dependency of different reservoir parameters on the shear wave velocity(VS)of clastic sedimentary rocks,and to identify the parameter/variable which shows the highest level of dependency.In the study,data-driven connectionist models are developed using machine learning approach of least square support vector machine(LSSVM).The coupled simulated annealing(CSA)approach is utilized to optimize the tuning and kernel parameters in the model development.The performance of the simulation-based model is evaluated using statistical parameters.It is found that the most dependency predictor variable is the compressional wave velocity,followed by the rock porosity,bulk density and shale volume in turn.A new correlation is developed to estimate VS,which captures the most influential parameters of sedimentary rocks.The new correlation is verified and compared with existing models using measured data of sandstone,and it exhibits a minimal error and high correlation coefficient(R^(2)-0.96).The hybridized LSSVM-CSA connectionist model development strategy can be applied for further analysis to predict rock mechanical properties.Additionally,the improved correlation of VS can be adopted to estimate rock elastic constants and conduct wellbore failure analysis for safe drilling and field development decisions,reducing the exploration costs.展开更多
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
基金the University of Transport Technology under grant number DTTD2022-12.
文摘Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.
基金the Inner Mongolia Science&Technology Plan Project(No.102-510001)the Inner Mongolia Autonomous Region Science&Technology Innovation to Guide the Reward Project(No.102-413128)the Inner Mongolia Science and Technology Achievement Conversion Project(No.CHZH2018130)。
文摘The structure and the production process for flying shear machine are introduced first.Then,a quintic polynomial is applied to the design of an electronic cam system for the rotary knife axis in short materials cutting.The dimensionless equation for a quintic polynomial cam curve is deduced.Finally,the curve is plotted with the cam constructor integrated into Siemens engineering development software SCOUT and it is tested with a laboratory platform,which consists of a motion controller SIMOTION and motor drivers SINAMICS S120.The results show that the running stability of the flying shear machine and the position control accuracy of the rotary knife can be effectively improved by using the curve designed in this paper.
基金supported by the National Key Research and Development Program of China(2022YFC3080100)the National Natural Science Foundation of China(Nos.52104090,52208328 and 12272353)+1 种基金the Open Fund of Anhui Province Key Laboratory of Building Structure and Underground Engineering,Anhui Jianzhu University(No.KLBSUE-2022-06)the Open Research Fund of Key Laboratory of Construction and Safety of Water Engineering of the Ministry of Water Resources,China Institute of Water Resources and Hydropower Research(Grant No.IWHR-ENGI-202302)。
文摘In geotechnical and tunneling engineering,accurately determining the mechanical properties of jointed rock holds great significance for project safety assessments.Peak shear strength(PSS),being the paramount mechanical property of joints,has been a focal point in the research field.There are limitations in the current peak shear strength(PSS)prediction models for jointed rock:(i)the models do not comprehensively consider various influencing factors,and a PSS prediction model covering seven factors has not been established,including the sampling interval of the joints,the surface roughness of the joints,the normal stress,the basic friction angle,the uniaxial tensile strength,the uniaxial compressive strength,and the joint size for coupled joints;(ii)the datasets used to train the models are relatively limited;and(iii)there is a controversy regarding whether compressive or tensile strength should be used as the strength term among the influencing factors.To overcome these limitations,we developed four machine learning models covering these seven influencing factors,three relying on Support Vector Regression(SVR)with different kernel functions(linear,polynomial,and Radial Basis Function(RBF))and one using deep learning(DL).Based on these seven influencing factors,we compiled a dataset comprising the outcomes of 493 published direct shear tests for the training and validation of these four models.We compared the prediction performance of these four machine learning models with Tang’s and Tatone’s models.The prediction errors of Tang’s and Tatone’s models are 21.8%and 17.7%,respectively,while SVR_linear is at 16.6%,SVR_poly is at 14.0%,and SVR_RBF is at 12.1%.DL outperforms the two existing models with only an 8.5%error.Additionally,we performed shear tests on granite joints to validate the predictive capability of the DL-based model.With the DL approach,the results suggest that uniaxial tensile strength is recommended as the material strength term in the PSS model for more reliable outcomes.
基金The simulations in this work are supported by the High-Performance Computing Center of Central South University.
文摘Shear deformation mechanisms of diamond-like carbon(DLC)are commonly unclear since its thickness of several micrometers limits the detailed analysis of its microstructural evolution and mechanical performance,which further influences the improvement of the friction and wear performance of DLC.This study aims to investigate this issue utilizing molecular dynamics simulation and machine learning(ML)techniques.It is indicated that the changes in the mechanical properties of DLC are mainly due to the expansion and reduction of sp3 networks,causing the stick-slip patterns in shear force.In addition,cluster analysis showed that the sp2-sp3 transitions arise in the stick stage,while the sp3-sp2 transitions occur in the slip stage.In order to analyze the mechanisms governing the bond breaking/re-formation in these transitions,the Random Forest(RF)model in ML identifies that the kinetic energies of sp3 atoms and their velocities along the loading direction have the highest influence.This is because high kinetic energies of atoms can exacerbate the instability of the bonding state and increase the probability of bond breaking/re-formation.Finally,the RF model finds that the shear force of DLC is highly correlated to its potential energy,with less correlation to its content of sp3 atoms.Since the changes in potential energy are caused by the variances in the content of sp3 atoms and localized strains,potential energy is an ideal parameter to evaluate the shear deformation of DLC.The results can enhance the understanding of the shear deformation of DLC and support the improvement of its frictional and wear performance.
基金supported by the National Natural Science Foundation of China(Nos.50875033, 50775018 and 51175063)
文摘Investigations made by the authors and collaborators into the microstructural and fracture aspects of adiabatic shear bands （ASBs） of the hardened steels and Ti alloys induced by high speed machining （HSM） are briefly reviewed. The principal findings are the following： （a） the microstructure inside the ASBs varies from the band center to the normal chip material, the gradient microstructures are found; （b） the HSM can produce two types of ASBs with increasing in cutting speed, the deformed shear bands formed at lower cutting speed and the transformed shear bands formed at higher cutting speed; （c） the very small equiaxed recrystallized grains are observed in the center of the ASBs, the dynamic recrystallization and phase transformation may occur simultaneously during the formation of the transformed ASBs; （d） The dynamic rotational recrystallization is the origin of the equiaxed grains in the center of the ASBs. A microstructural evolution model in ASBs produced during HSM for the harden steel is proposed; （e） the microstructural pattern of fracture surface is characterised by the elongated dimples. A microcosmic adiabatic shear fracture model during HSM of the hardened steel is built up.
文摘As the cutting speed goes higher, the mechanism of chip deformation will be changed significantly, i.e., continuous chip in low cutting speed will shift to serrated chip with shear localization. For the shear localized chip, the parameters used to assess the chip deformation for continuous chip, such as shorten coefficient ξ, shear angle φ and shear strain ε, can not describe the chip deformation correctly or comprehensively. This paper deals with the assessment of chip deformation of shear localization. There are two deformation regions in shear localized chip, one is the chip segment body with relative smaller plastic deformation, another one is the boundary between segments with shear localization, so called shear band. Considering the two distinct deformation regions, two parameters are used to define their deformation respectively. According to the analysis of chip formation process, the equations have been deduced to calculate the shear strains of shear band ε, shear strain of chip segment ε 1 and shear rate so that the shear localized chip deformation can be assessed correctly and comprehensively. By use of this assessment, the chip deformation in machining selenium treated stainless steel (STSS) and common stainless steel at various cutting conditions is investigated. The experiment results obtained by the machining of stainless steel prove that: (1) the shear strain and strain rate increase with the increasing of cutting speed; (2) the shear strain in shear band can be over 10 when cutting speed exceeding 200 m/min for both types of stainless steel, and it is much higher than the strain of chip segment. The difference will be enlarged as the cutting speed increasing; (3) As the comparison, the shear strain for the STSS is a little lower than that for JIS304; (4) The stain rate is extremely high (= 2.5×10 5 1/s ). In range of cutting speed less than 180 m/min, the strain rate for STSS is lower than that for JIS304. However, when the cutting speed is higher than 180 m/min, the strain rate for STSS is higher than that for JIS304.
文摘Mar-M247 is a nickel-based alloy which is well known as difficult-to-machine material due to its characteristics of high strength, poor thermal diffusion and work hardening. Calculation of shear stress by an analytical force model to indicate the effect of coating material, cutting speed, feed rate on tool life and surface roughness was conducted experimentally. Cutting tests were performed using round inserts, with cutting speeds ranging from 50 to 300 rn/min, and feed rates from 0.1 to 0.4 mm/tooth, without using cooling liquids. The behavior of the TiN and TiCN layers using various cutting conditions was analyzed with orthogonal machining force model. Cutting results indicate that different coated tools, together with cutting variables, play a significant role in determining the machinability when milling Mar-M247.
基金financial support from the Doctoral Innovative Talent Cultivation Fund at China University of Mining and Technology (Beijing)(No. BBJ2023049)。
文摘Earthquakes can cause violent liquefaction of the soil, resulting in unstable foundations that can cause serious damage to facilities such as buildings, roads, and dikes. This is a primary cause of major earthquake disasters. Therefore, the discrimination and prediction of earthquake-induced soil liquefaction has been a hot issue in geohazard research. The soil liquefaction assessment is an integral part of engineering practice. This paper evaluated a dataset of 435 seismic sand liquefaction events using machine learning algorithms. The dataset was analyzed using seven potential assessment parameters. Ten machine learning algorithms are evaluated for their ability to assess seismic sand liquefaction potential, including Linear Discriminant Analysis(LDA), Quadratic Discriminant Analysis(QDA), Naive Bayes(NB), KNearest Neighbor(KNN), Artificial Neural Network(ANN), Classification Tree(CT), Support Vector Machine(SVM), Random Forest(RF), e Xtreme Gradient Boosting(XGBoost), Light Gradient Boosting Machine(Light GBM). A 10-fold cross-validation(CV) method was used in the modeling process to verify the predictive performance of the machine learning models. The final percentages of significant parameters that influenced the prediction results were obtained as Cyclic Stress Ratio(CSR) and Shear-Wave Velocity( VS1) with 56% and 38%, respectively. The final machine learning algorithms identified as suitable for seismic sand liquefaction assessment were the CT, RF, XGBoost algorithms, with the RF algorithm performing best.
基金the Iranian National Science Foundation(INSF)for the financial support of this research under Project Number 4004169Isfahan University of Technology。
文摘Knowledge about the spatial distribution of the soil physical and mechanical properties is crucial for soil management,water yield,and sustainability at the watershed scale;however,the lack of soil data hinders the application of this tool,thus urging the need to estimate soil properties and consequently,to perform the spatial distribution.This research attempted to examine the proficiency of three machine learning methods(RF:Random Forest;Cubist:Regression Tree;and SVM:Support Vector Machine)to predict soil physical and mechanical properties,saturated hydraulic conductivity(Ks),Cohesion measured by fall-cone at the saturated(Psat)and dry(Pdry)states,hardness index(HI)and dry shear strength(SS)by integrating environmental variables and soil features in the Zayandeh-Rood dam watershed,central Iran.To determine the best combination of input variables,three scenarios were examined as follows:scenarioⅠ,terrain attributes derivative from a digital elevation model(DEM)+remotely sensed data;scenarioⅡ,covariates of scenarioⅠ+selected climatic data and some thematic maps;scenarioⅢ,covariates in scenarioⅡ+intrinsic soil properties(Clay,Silt,Sand,bulk density(BD),soil organic matter(SOM),calcium carbonate equivalent(CCE),mean weight diameter(MWD)and geometric weight diameter(GWD)).The results showed that for Ks,Psat Pdry and SS,the best performance was found by the RF model in the third scenario,with R2=0.53,0.32,0.31 and 0.41,respectively,while for soil hardness index(HI),Cubist model in the third scenario with R2=0.25 showed the highest performance.For predicting Ks and Psat,soil characteristics(i.e.clay and soil SOM and BD),and land use were the most important variables.For predicting Pdry,HI,and SS,some topographical characteristics(Valley depth,catchment area,mltiresolution of ridge top flatness index),and some soil characteristics(i.e.clay,SOM and MWD)were the most important input variables.The results of this research present moderate accuracy,however,the methodology employed provides quick and costeffective information serving as the scientific basis for decision-making goals.
文摘Artificial intelligence(AI)is revolutionizing medical imaging,particularly in chronic liver diseases assessment.AI technologies,including machine learning and deep learning,are increasingly integrated with multiparametric ultrasound(US)techniques to provide more accurate,objective,and non-invasive evaluations of liver fibrosis and steatosis.Analyzing large datasets from US images,AI enhances diagnostic precision,enabling better quantification of liver stiffness and fat content,which are essential for diagnosing and staging liver fibrosis and steatosis.Combining advanced US modalities,such as elastography and doppler imaging with AI,has demonstrated improved sensitivity in identifying different stages of liver disease and distinguishing various degrees of steatotic liver.These advancements also contribute to greater reproducibility and reduced operator dependency,addressing some of the limitations of traditional methods.The clinical implications of AI in liver disease are vast,ranging from early detection to predicting disease progression and evaluating treatment response.Despite these promising developments,challenges such as the need for large-scale datasets,algorithm transparency,and clinical validation remain.The aim of this review is to explore the current applications and future potential of AI in liver fibrosis and steatosis assessment using multiparametric US,highlighting the technological advances and clinical relevance of this emerging field.
基金supported by the Nagasaki University Global Human Resource Development Scholarship and the JST SPRING,Japan Grant Number JPMJSP2172.
文摘Accurate prediction of the shear strength of rock joints is crucial for assessing the stability of civil and mining engineering projects.Traditional methods for determining the shear strength of rock joints are time-consuming,costly,and computationally complex.Machine learning methods,which are driven by data,provide a costeffective and rapid approach to predicting rock joint shear strength,overcoming the limitations of traditional techniques.This study employs nine machine learning models:eXtreme gradient boosting(XGBoost),random forest(RF),Support vector regression(SVR),decision tree(DT),Gaussian process regression(GPR),K-nearest neighbors(KNN),categorical boosting(CatBoost),extreme learning machine(ELM),and adaptive boosting(AdaBoost).A dataset of 288 data points was compiled from an extensive set of literature.Five input features,namely,normal stress,uniaxial compressive strength,Young’s modulus,joint roughness coefficient(JRC),and specimen length,were selected,with shear strength of the rock joints as the output variable.The performance of the nine ML models was assessed using the root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute error(MAE).Due to its unique ordered boosting mechanism and symmetric tree structure,Cat-Boost outperformed the other models,achieving RMSE,R^(2),and MAE values of 0.4663,0.9765,and 0.3508,respectively.Compared with the experimental results,the model yielded a mean square error(MSE)of 0.0360.The proposed ML method offers a cost-effective and efficient solution for predicting rock joint shear strength.
基金the Research Committee of The Hong Kong Polytechnic University and the Innovation Technology Commission of The Hong Kong SAR Government for their financial support of the Hong Kong Partner State Key Laboratory of Ultra-Precision Machining Technology
文摘There have been various theoretical attempts by researchers worldwide to link up different scales of plasticity studies from the nano-, micro- and macro-scale of observation, based on molecular dynamics, crystal plasticity and continuum mechanics. Very few attempts, however, have been reported in ultra-precision machining studies. A mesoplasticity approach advocated by Lee and Yang is adopted by the authors and is successfully applied to studies of the micro-cutting mechanisms in ultra-precision machining. Traditionally, the shear angle in metal cutting, as well as the cutting force variation, can only be determined from cutting tests. In the pioneering work of the authors, the use of mesoplasticity theory enables prediction of the fluctuation of the shear angle and micro-cutting force, shear band formation, chip morphology in diamond turning and size effect in nano-indentation. These findings are verified by experiments. The mesoplasticity formulation opens up a new direction of studies to enable how the plastic behaviour of materials and their constitutive representations in deformation processing, such as machining can be predicted, assessed and deduced from the basic properties of the materials measurable at the microscale.
文摘Accurate measurement of acoustic velocities of sedimentary rocks is essential for prediction of rock elastic constants and well failure analysis during drilling operations.Direct measurement by advanced logging tools such as dipole sonic imager is not always possible.For older wells,such data are not available in most cases.Therefore,it is an alternate way to develop a reliable correlation to estimate the shear wave velocity from existing log and/or core data.The objective of this research is to investigate the nature of dependency of different reservoir parameters on the shear wave velocity(VS)of clastic sedimentary rocks,and to identify the parameter/variable which shows the highest level of dependency.In the study,data-driven connectionist models are developed using machine learning approach of least square support vector machine(LSSVM).The coupled simulated annealing(CSA)approach is utilized to optimize the tuning and kernel parameters in the model development.The performance of the simulation-based model is evaluated using statistical parameters.It is found that the most dependency predictor variable is the compressional wave velocity,followed by the rock porosity,bulk density and shale volume in turn.A new correlation is developed to estimate VS,which captures the most influential parameters of sedimentary rocks.The new correlation is verified and compared with existing models using measured data of sandstone,and it exhibits a minimal error and high correlation coefficient(R^(2)-0.96).The hybridized LSSVM-CSA connectionist model development strategy can be applied for further analysis to predict rock mechanical properties.Additionally,the improved correlation of VS can be adopted to estimate rock elastic constants and conduct wellbore failure analysis for safe drilling and field development decisions,reducing the exploration costs.
基金support of the Shenzhen Science and Technology Innovation Commission under Project Numbers KQTD20190929172505711,JSGG20210420091802007, and JCYJ20210324115413036Guangdong Provincial Department of Science and Technology under Project Number K22333004。
文摘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.
