Larger and larger proportions of aluminium castings,especially those produced by the die casting process,can be observed during recent years in the automotive industry,house-hold articles and others.In case of the aut...Larger and larger proportions of aluminium castings,especially those produced by the die casting process,can be observed during recent years in the automotive industry,house-hold articles and others.In case of the automotive industry,apart from the traditional elements produced by the die pressure method such as engine blocks or crank shaft bedplates,aluminium is displacing steel from structural parts of cars('body in white').The current state and development directions of the structural solutions of cold-chamber die castings are analysed in this paper.These solutions drive the prospective development of these machines and die casting technology.The focus is mainly on essential functional systems such as:hydraulic drives of closing and locking units,as well as pressing in die machines of known companies present on the European market.展开更多
SiC is the most common reinforcement in magnesium matrix composites,and the tensile strength of SiC-reinforced magnesium matrix composites is closely related to the distribution of SiC.Achieving a uniform distribution...SiC is the most common reinforcement in magnesium matrix composites,and the tensile strength of SiC-reinforced magnesium matrix composites is closely related to the distribution of SiC.Achieving a uniform distribution of SiC requires fine control over the parameters of SiC and the processing and preparation process.However,due to the numerous adjustable parameters,using traditional experimental methods requires a considerable amount of experimentation to obtain a uniformly distributed composite material.Therefore,this study adopts a machine learning approach to explore the tensile strength of SiC-reinforced magnesium matrix composites in the mechanical stirring casting process.By analyzing the influence of SiC parameters and processing parameters on composite material performance,we have established an effective predictive model.Furthermore,six different machine learning regression models have been developed to predict the tensile strength of SiC-reinforced magnesium matrix composites.Through validation and comparison,our models demonstrate good accuracy and reliability in predicting the tensile strength of the composite material.The research findings indicate that hot extrusion treatment,SiC content,and stirring time have a significant impact on the tensile strength.展开更多
Conventional fractional slot concentrated winding three-phase axial flux permanent magnet machines have an abundance of armature reaction magnetic field harmonics which deteriorate the torque performance of the machin...Conventional fractional slot concentrated winding three-phase axial flux permanent magnet machines have an abundance of armature reaction magnetic field harmonics which deteriorate the torque performance of the machine.This paper presents a double-stator dislocated axial flux permanent magnet machine with combined wye-delta winding.A wye-delta(Y-△)winding connection method is designed to eliminate the 6 th ripple torque generated by air gap magnetic field harmonics.Then,the accurate subdomain method is adopted to acquire the no-load and armature magnetic fields of the machine,respectively,and the magnetic field harmonics and torque performance of the designed machine are analyzed.Finally,a 6 k W,4000 r/min,18-slot/16-pole axial flux permanent magnet machine is designed.The finite element simulation results show that the proposed machine can effectively eliminate the 6 th ripple torque and greatly reduce the torque ripple while the average torque is essentially identical to that of the conventional three-phase machines with wye-winding connection.展开更多
Conventional fatigue tests on complex components are difficult to sample,time-consuming and expensive.To avoid such problems,several popular machine learning(ML)algorithms were used and compared to predict fatigue lif...Conventional fatigue tests on complex components are difficult to sample,time-consuming and expensive.To avoid such problems,several popular machine learning(ML)algorithms were used and compared to predict fatigue life of gray cast iron(GCI)with the complex microstructures.The feature analysis shows that the fatigue life of GCI is mainly influenced by the external environment such as the stress amplitude,and the internal microstructure parameters such as the percentage of graphite,graphite length,stress concentration factor at the graphite tip,matrix microhardness and Brinell hardness.For simplicity,collected datasets with some of the above features were used to train ML models including back-propagation neural network(BPNN),random forest(RF)and eXtreme gradient boosting(XGBoost).The comparison results suggest that the three models could predict the fatigue lives of GCI,while the implemented RF algorithm is the best performing model.Moreover,the S–N curves fitted by the Basquin relation in the predicted data have a mean relative error of 15%compared to the measured data.The results have demonstrated the advantages of ML,which provides a generic way to predict the fatigue life of GCI for reducing time and cost.展开更多
Casting technology is a fundamental and irreplaceable method in advanced manufacturing.The design and optimization of casting processes are crucial for producing high-performance,complex metal components.Transitioning...Casting technology is a fundamental and irreplaceable method in advanced manufacturing.The design and optimization of casting processes are crucial for producing high-performance,complex metal components.Transitioning from traditional process design based on"experience+experiment"to an integrated,intelligent approach is essential for achieving precise control over microstructure and properties.This paper provides a comprehensive and systematic review of intelligent casting process design and optimization for the first time.First,it explores process design methods based on casting simulation and integrated computational materials engineering(ICME).It then examines the application of machine learning(ML)in process design,highlighting its efficiency and existing challenges,along with the development of integrated intelligent design platforms.Finally,future research directions are discussed to drive further advancements and sustainable development in intelligent casting design and optimization.展开更多
A computer control system for drawing machine in horizontal continuous cast set was introduced.The operation features of the drawing machine were analyzed»the hardware configuration and principles of interface ci...A computer control system for drawing machine in horizontal continuous cast set was introduced.The operation features of the drawing machine were analyzed»the hardware configuration and principles of interface circuit for stroke measurements were given out.An effective method was provided,which made the process parameters progressively optimize under the software environment using friendly interface of person-and-computer communication.This method was also adaptable to optimize parameters of other production process which are hard to model.展开更多
Gray cast irons were inoculated with FeSi75＋RE and FeSi75＋Sr inoculants. The changes of apex angle of the drills before and after being used were used to evaluate machinability of gray cast irons. Effect of FeSi75＋...Gray cast irons were inoculated with FeSi75＋RE and FeSi75＋Sr inoculants. The changes of apex angle of the drills before and after being used were used to evaluate machinability of gray cast irons. Effect of FeSi75＋RE and FeSi75＋Sr inoculants on mechanical properties, machinability and sensibility of gray cast iron used in cylinder block were investigated. Experimental results showed that gray cast iron treated with 60%FeSi75＋40% RE inoculants exhibited tensile strength consistently at about 295 MPa along with good hardness and improved metallurgical quality. While gray cast iron inoculated with 20%FeSi75＋80% Sr inoculants exhibited the best machinability, the lowest cross-section sensibility and the least microhardness difference. The tool flank wear of the drill increased correspondingly with the increase of the microhardness difference of the matrix, indicating the great effect of homogeneity of the matrix on the machinability of gray cast iron.展开更多
A356 alloys are widely used in industries due to their excellent comprehensive performance.Sr is usually added in A356 alloys to improve their mechanical properties.There have been various experimental reports on the ...A356 alloys are widely used in industries due to their excellent comprehensive performance.Sr is usually added in A356 alloys to improve their mechanical properties.There have been various experimental reports on the optimal additional amount of Sr in A356 alloys,but their results are inevitably inconsistent.In this paper,a combination of computational thermodynamic and machine learning approaches was employed to determine the optimal Sr content in A356 alloys with the best mechanical properties.First,a self-consistent thermodynamic database of quaternary Al-Si-Mg-Sr system was established by means of the Calculation of PHAse Diagram technique supported by key experiments.Second,the fractions for solidified phase/structures of A356-xSr alloys predicted by Scheil simulation,together with the measured mechanical properties were set as the input dataset in the machine learning model to train the relation of“composition-microstructure-properties”.The optimal addition of Sr in A356 alloy was designed as 0.005 wt.%and validated by key experiments.Furthermore,such a combinatorial approach can help to understand the strengthening/toughening mechanisms of Sr-modified A356 alloys.It is also anticipated that the present approach may provide a feasible means for efficient and accurate design of various casting alloys and understanding the alloy strengthening/toughening mechanisms.展开更多
Owing to its exceptional casting performance,substantial utilization of recycled sand,and environmen-tally sustainable characteristics,frozen sand mold casting technology has found extensive application across diverse...Owing to its exceptional casting performance,substantial utilization of recycled sand,and environmen-tally sustainable characteristics,frozen sand mold casting technology has found extensive application across diverse sectors,including aerospace,power machinery,and the automotive industry.The focus of the present study was on the development of frozen sand mold formulations tailored for efficient machin-ing,guided by the performance and cutting fracture mechanism of frozen sand molds.A regional tem-perature control device was developed for the purpose of conducting cryogenic cutting experiments on frozen sand molds with varying geometrical characteristics and molding materials.The impact of milling process parameters on the dimension accuracy of both sand molds and castings,as well as castings’surface roughness,were systematically investigated by a whole-process error flow control method.The findings indicate that precise and efficient processing of complicated sand molds was achievable by using sand particles with sizes ranging from 106 to 212μm,and water content between 4 and 5 wt.%,freezing temperature below-25℃,and cutting temperature within the range of-5 to 0℃.Through the frozen-casting of representative components,it was validated that the machining error of the frozen sand mold was within±0.25 mm.Additionally,the dimensional accuracy of the flywheel shell casting conformed to the CT8 specifications.This study provides theoretical guidance for the selection of frozen-casting sand formulations and close-loop control of process size chains for complex metal parts,as well as an overall solution for the realization of sustainable development of green casting.展开更多
Silicon-based aluminum casting alloys are known to be one of the most widely used alloy systems mainly due to their superior casting characteristics and unique combination of mechanical and physical properties. Howeve...Silicon-based aluminum casting alloys are known to be one of the most widely used alloy systems mainly due to their superior casting characteristics and unique combination of mechanical and physical properties. However,manufacturing of thin-walled aluminum die-casting components,less than 1.0 mm in thickness,is generally known to be very difficult task to achieve aluminum casting alloys with high fluidity.Therefore,in this study,the optimal die-casting conditions for producing 297 mm×210 mm×0.7 mm thin-walled aluminum component was examined experimentally by using 2 different gating systems,tangential and split type,and vent design.Furthermore,computational solidification simulation was also conducted.The results showed that split type gating system was preferable gating design than tangential type gating system at the point of view of soundness of casting and distortion generated after solidification.It was also found that proper vent design was one of the most important factors for producing thin-wall casting components because it was important for the fulfillment of the thin-wall cavity and the minimization of the casting distortion.展开更多
Die casting machines, dies, die castings, peripheral equipments, die lubricants, raw materials for die casting, melting & holding furnaces, cleaning equipments, etc. were exhibited during the 4th China Internation...Die casting machines, dies, die castings, peripheral equipments, die lubricants, raw materials for die casting, melting & holding furnaces, cleaning equipments, etc. were exhibited during the 4th China International Die Casting Exhibition, which was surveyed in the paper.展开更多
In steel continuous casting(CC),the choice of the appropriate speed at which the slab is cast can be influenced by many different factors and phenomena.While the slab thickness seems to have the biggest impact,other f...In steel continuous casting(CC),the choice of the appropriate speed at which the slab is cast can be influenced by many different factors and phenomena.While the slab thickness seems to have the biggest impact,other features like the slab width have been consistently overlooked.In fact,the slab width practically limits the casting speed via the mass flow constraint which governs the input and output balance at the tundish.Here,we present a case study that aims at analyzing steel production data from the perspective of casting speed constraints.By studying the speed fluctuations of an industrial CC machine,we identify a strategic regime change toward a stricter consideration of the mass flow constraint.The regime change manifests itself in a significant increase in the correlation between the actual casting speed and the maximal speed associated with the mass flow constraint.On the surface,taking greater account of the input and output balance at the tundish has reduced the productivity of the continuous caster;however,one can argue that the lessened yield is compensated by a diminished risk of eventual slab breaking.From the perspective of this trade-off,we establish a visualization technique that enables us to pinpoint the boundary beyond which one strategic regime becomes economically more advantageous than the other.展开更多
The copper disc casting machine is core equipment for producing copper anode plates in the copper metallurgy industry.The copper disc casting machine casting package motion curve(CPMC) is significant for precise casti...The copper disc casting machine is core equipment for producing copper anode plates in the copper metallurgy industry.The copper disc casting machine casting package motion curve(CPMC) is significant for precise casting and efficient production.However,the lack of exact casting modeling and real-time simulation information severely restricts dynamic CPMC optimization.To this end,a liquid copper droplet model describes the casting package copper flow pattern in the casting process.Furthermore,a CPMC optimization model is proposed for the first time.On top of this,a digital twin dual closed-loop self-optimization application framework(DT-DCS) is constructed for optimizing the copper disc casting process to achieve self-optimization of the CPMC and closed-loop feedback of manufacturing information during the casting process.Finally,a case study is carried out based on the proposed methods in the industrial field.展开更多
Achieving optimal mechanical performance in high-pressure die-cast(HPDC)Mg-based alloys through experimental methods is both costly and time-intensive due to significant variations in composition.This study leverages ...Achieving optimal mechanical performance in high-pressure die-cast(HPDC)Mg-based alloys through experimental methods is both costly and time-intensive due to significant variations in composition.This study leverages machine learning(ML)techniques to accelerate the development of high-performance Mg-based alloys.Data on alloy composition and mechanical properties were collected from literature sources,focusing on HPDC Mg-based alloys.Six ML models—extra trees,CatBoost,k-nearest neighbors,random forest,gradient boosting,and decision tree—were trained to predict mechanical behavior.Cat Boost yielded the highest prediction accuracy with R^(2) scores of 0.95 for ultimate tensile strength(UTS)and 0.92 for yield strength(YS).Further validation using published datasets reaffirmed its reliability,demonstrating R^(2) values of 0.956(UTS)and 0.936(YS),MAE of 1%and 2.8%,and RMSE of 1%and 3.5%,respectively.Among these,the CatBoost model demonstrated the highest predictive accuracy,outperforming other ML techniques across multiple optimization metrics.展开更多
文摘Larger and larger proportions of aluminium castings,especially those produced by the die casting process,can be observed during recent years in the automotive industry,house-hold articles and others.In case of the automotive industry,apart from the traditional elements produced by the die pressure method such as engine blocks or crank shaft bedplates,aluminium is displacing steel from structural parts of cars('body in white').The current state and development directions of the structural solutions of cold-chamber die castings are analysed in this paper.These solutions drive the prospective development of these machines and die casting technology.The focus is mainly on essential functional systems such as:hydraulic drives of closing and locking units,as well as pressing in die machines of known companies present on the European market.
基金supported by the National Natural Science Foundation of China (Nos.52375394 and 52074246)the National Defense Basic Scientific Research Program of China (No.JCKY2020408B002)Key Research and Development Program of Shanxi Province (No.202102050201011)。
文摘SiC is the most common reinforcement in magnesium matrix composites,and the tensile strength of SiC-reinforced magnesium matrix composites is closely related to the distribution of SiC.Achieving a uniform distribution of SiC requires fine control over the parameters of SiC and the processing and preparation process.However,due to the numerous adjustable parameters,using traditional experimental methods requires a considerable amount of experimentation to obtain a uniformly distributed composite material.Therefore,this study adopts a machine learning approach to explore the tensile strength of SiC-reinforced magnesium matrix composites in the mechanical stirring casting process.By analyzing the influence of SiC parameters and processing parameters on composite material performance,we have established an effective predictive model.Furthermore,six different machine learning regression models have been developed to predict the tensile strength of SiC-reinforced magnesium matrix composites.Through validation and comparison,our models demonstrate good accuracy and reliability in predicting the tensile strength of the composite material.The research findings indicate that hot extrusion treatment,SiC content,and stirring time have a significant impact on the tensile strength.
基金supported in part by the National Natural Science Foundation of China Grant No.51877139。
文摘Conventional fractional slot concentrated winding three-phase axial flux permanent magnet machines have an abundance of armature reaction magnetic field harmonics which deteriorate the torque performance of the machine.This paper presents a double-stator dislocated axial flux permanent magnet machine with combined wye-delta winding.A wye-delta(Y-△)winding connection method is designed to eliminate the 6 th ripple torque generated by air gap magnetic field harmonics.Then,the accurate subdomain method is adopted to acquire the no-load and armature magnetic fields of the machine,respectively,and the magnetic field harmonics and torque performance of the designed machine are analyzed.Finally,a 6 k W,4000 r/min,18-slot/16-pole axial flux permanent magnet machine is designed.The finite element simulation results show that the proposed machine can effectively eliminate the 6 th ripple torque and greatly reduce the torque ripple while the average torque is essentially identical to that of the conventional three-phase machines with wye-winding connection.
基金This work is supported by the National Natural Science Foundation of China(NSFC)under Grant Nos.51871224 and 52130002.
文摘Conventional fatigue tests on complex components are difficult to sample,time-consuming and expensive.To avoid such problems,several popular machine learning(ML)algorithms were used and compared to predict fatigue life of gray cast iron(GCI)with the complex microstructures.The feature analysis shows that the fatigue life of GCI is mainly influenced by the external environment such as the stress amplitude,and the internal microstructure parameters such as the percentage of graphite,graphite length,stress concentration factor at the graphite tip,matrix microhardness and Brinell hardness.For simplicity,collected datasets with some of the above features were used to train ML models including back-propagation neural network(BPNN),random forest(RF)and eXtreme gradient boosting(XGBoost).The comparison results suggest that the three models could predict the fatigue lives of GCI,while the implemented RF algorithm is the best performing model.Moreover,the S–N curves fitted by the Basquin relation in the predicted data have a mean relative error of 15%compared to the measured data.The results have demonstrated the advantages of ML,which provides a generic way to predict the fatigue life of GCI for reducing time and cost.
基金supported by the National Natural Science Foundation of China(No.52074246)the National Defense Basic Scientific Research Program of China(No.JCKY2020408B002)+1 种基金the Key R&D Program of Shanxi Province(No.202102050201011)the Shanxi Province Graduate Innovation Project(No.2021Y591).
文摘Casting technology is a fundamental and irreplaceable method in advanced manufacturing.The design and optimization of casting processes are crucial for producing high-performance,complex metal components.Transitioning from traditional process design based on"experience+experiment"to an integrated,intelligent approach is essential for achieving precise control over microstructure and properties.This paper provides a comprehensive and systematic review of intelligent casting process design and optimization for the first time.First,it explores process design methods based on casting simulation and integrated computational materials engineering(ICME).It then examines the application of machine learning(ML)in process design,highlighting its efficiency and existing challenges,along with the development of integrated intelligent design platforms.Finally,future research directions are discussed to drive further advancements and sustainable development in intelligent casting design and optimization.
文摘A computer control system for drawing machine in horizontal continuous cast set was introduced.The operation features of the drawing machine were analyzed»the hardware configuration and principles of interface circuit for stroke measurements were given out.An effective method was provided,which made the process parameters progressively optimize under the software environment using friendly interface of person-and-computer communication.This method was also adaptable to optimize parameters of other production process which are hard to model.
基金supported by Program for Scientific and Technological Renovation Talents in University of Henan Province (2009HASTIT023)the National Natural Science Foundation of China (50771042)
文摘Gray cast irons were inoculated with FeSi75＋RE and FeSi75＋Sr inoculants. The changes of apex angle of the drills before and after being used were used to evaluate machinability of gray cast irons. Effect of FeSi75＋RE and FeSi75＋Sr inoculants on mechanical properties, machinability and sensibility of gray cast iron used in cylinder block were investigated. Experimental results showed that gray cast iron treated with 60%FeSi75＋40% RE inoculants exhibited tensile strength consistently at about 295 MPa along with good hardness and improved metallurgical quality. While gray cast iron inoculated with 20%FeSi75＋80% Sr inoculants exhibited the best machinability, the lowest cross-section sensibility and the least microhardness difference. The tool flank wear of the drill increased correspondingly with the increase of the microhardness difference of the matrix, indicating the great effect of homogeneity of the matrix on the machinability of gray cast iron.
基金supported by the National Key Research and Development Program of China(Grant No.2019YFB2006500)the Youth Talent Project of Innovation-driven Plan at Central South University(Grant No.2282019SYLB026)+2 种基金the financial support from the Fundamental Research Funds for the Central Universities of Central South University(Grant No.2021zzts0094)the financial support from the Natural Science Foundation of China(Grant No.52061007)the Guangxi Natural Science Foundation(Grant No.2019GXNSFAA245003)。
文摘A356 alloys are widely used in industries due to their excellent comprehensive performance.Sr is usually added in A356 alloys to improve their mechanical properties.There have been various experimental reports on the optimal additional amount of Sr in A356 alloys,but their results are inevitably inconsistent.In this paper,a combination of computational thermodynamic and machine learning approaches was employed to determine the optimal Sr content in A356 alloys with the best mechanical properties.First,a self-consistent thermodynamic database of quaternary Al-Si-Mg-Sr system was established by means of the Calculation of PHAse Diagram technique supported by key experiments.Second,the fractions for solidified phase/structures of A356-xSr alloys predicted by Scheil simulation,together with the measured mechanical properties were set as the input dataset in the machine learning model to train the relation of“composition-microstructure-properties”.The optimal addition of Sr in A356 alloy was designed as 0.005 wt.%and validated by key experiments.Furthermore,such a combinatorial approach can help to understand the strengthening/toughening mechanisms of Sr-modified A356 alloys.It is also anticipated that the present approach may provide a feasible means for efficient and accurate design of various casting alloys and understanding the alloy strengthening/toughening mechanisms.
基金supported by the National Key R&D Program of China(grant No.2021YFB3401200)the Jiangsu Provincial Basic Research Program(Natural Science Foundation)Youth Fund(grant No.BK20230885)the Special Technical Project for Equipment Pre-research(grantNo.30104040302).
文摘Owing to its exceptional casting performance,substantial utilization of recycled sand,and environmen-tally sustainable characteristics,frozen sand mold casting technology has found extensive application across diverse sectors,including aerospace,power machinery,and the automotive industry.The focus of the present study was on the development of frozen sand mold formulations tailored for efficient machin-ing,guided by the performance and cutting fracture mechanism of frozen sand molds.A regional tem-perature control device was developed for the purpose of conducting cryogenic cutting experiments on frozen sand molds with varying geometrical characteristics and molding materials.The impact of milling process parameters on the dimension accuracy of both sand molds and castings,as well as castings’surface roughness,were systematically investigated by a whole-process error flow control method.The findings indicate that precise and efficient processing of complicated sand molds was achievable by using sand particles with sizes ranging from 106 to 212μm,and water content between 4 and 5 wt.%,freezing temperature below-25℃,and cutting temperature within the range of-5 to 0℃.Through the frozen-casting of representative components,it was validated that the machining error of the frozen sand mold was within±0.25 mm.Additionally,the dimensional accuracy of the flywheel shell casting conformed to the CT8 specifications.This study provides theoretical guidance for the selection of frozen-casting sand formulations and close-loop control of process size chains for complex metal parts,as well as an overall solution for the realization of sustainable development of green casting.
基金Acknowledgement This work was supported by Korea Institute of Industrial Technology and Gwangju Metropolitan City through ＂The Advanced Materials and Components Industry Development Program＂.
文摘Silicon-based aluminum casting alloys are known to be one of the most widely used alloy systems mainly due to their superior casting characteristics and unique combination of mechanical and physical properties. However,manufacturing of thin-walled aluminum die-casting components,less than 1.0 mm in thickness,is generally known to be very difficult task to achieve aluminum casting alloys with high fluidity.Therefore,in this study,the optimal die-casting conditions for producing 297 mm×210 mm×0.7 mm thin-walled aluminum component was examined experimentally by using 2 different gating systems,tangential and split type,and vent design.Furthermore,computational solidification simulation was also conducted.The results showed that split type gating system was preferable gating design than tangential type gating system at the point of view of soundness of casting and distortion generated after solidification.It was also found that proper vent design was one of the most important factors for producing thin-wall casting components because it was important for the fulfillment of the thin-wall cavity and the minimization of the casting distortion.
文摘Die casting machines, dies, die castings, peripheral equipments, die lubricants, raw materials for die casting, melting & holding furnaces, cleaning equipments, etc. were exhibited during the 4th China International Die Casting Exhibition, which was surveyed in the paper.
文摘In steel continuous casting(CC),the choice of the appropriate speed at which the slab is cast can be influenced by many different factors and phenomena.While the slab thickness seems to have the biggest impact,other features like the slab width have been consistently overlooked.In fact,the slab width practically limits the casting speed via the mass flow constraint which governs the input and output balance at the tundish.Here,we present a case study that aims at analyzing steel production data from the perspective of casting speed constraints.By studying the speed fluctuations of an industrial CC machine,we identify a strategic regime change toward a stricter consideration of the mass flow constraint.The regime change manifests itself in a significant increase in the correlation between the actual casting speed and the maximal speed associated with the mass flow constraint.On the surface,taking greater account of the input and output balance at the tundish has reduced the productivity of the continuous caster;however,one can argue that the lessened yield is compensated by a diminished risk of eventual slab breaking.From the perspective of this trade-off,we establish a visualization technique that enables us to pinpoint the boundary beyond which one strategic regime becomes economically more advantageous than the other.
基金supported in part by the National Major Scientific Research Equipment of China (61927803)the National Natural Science Foundation of China Basic Science Center Project (61988101)+1 种基金Science and Technology Innovation Program of Hunan Province (2021RC4054)the China Postdoctoral Science Foundation (2021M691681)。
文摘The copper disc casting machine is core equipment for producing copper anode plates in the copper metallurgy industry.The copper disc casting machine casting package motion curve(CPMC) is significant for precise casting and efficient production.However,the lack of exact casting modeling and real-time simulation information severely restricts dynamic CPMC optimization.To this end,a liquid copper droplet model describes the casting package copper flow pattern in the casting process.Furthermore,a CPMC optimization model is proposed for the first time.On top of this,a digital twin dual closed-loop self-optimization application framework(DT-DCS) is constructed for optimizing the copper disc casting process to achieve self-optimization of the CPMC and closed-loop feedback of manufacturing information during the casting process.Finally,a case study is carried out based on the proposed methods in the industrial field.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950)。
文摘Achieving optimal mechanical performance in high-pressure die-cast(HPDC)Mg-based alloys through experimental methods is both costly and time-intensive due to significant variations in composition.This study leverages machine learning(ML)techniques to accelerate the development of high-performance Mg-based alloys.Data on alloy composition and mechanical properties were collected from literature sources,focusing on HPDC Mg-based alloys.Six ML models—extra trees,CatBoost,k-nearest neighbors,random forest,gradient boosting,and decision tree—were trained to predict mechanical behavior.Cat Boost yielded the highest prediction accuracy with R^(2) scores of 0.95 for ultimate tensile strength(UTS)and 0.92 for yield strength(YS).Further validation using published datasets reaffirmed its reliability,demonstrating R^(2) values of 0.956(UTS)and 0.936(YS),MAE of 1%and 2.8%,and RMSE of 1%and 3.5%,respectively.Among these,the CatBoost model demonstrated the highest predictive accuracy,outperforming other ML techniques across multiple optimization metrics.
