The optimization system, which was the subject of our study, is an autonomous chain for the automatic management of cyanide consumption. It is in the phase of industrial automation which made it possible to use the ma...The optimization system, which was the subject of our study, is an autonomous chain for the automatic management of cyanide consumption. It is in the phase of industrial automation which made it possible to use the machines in order to reduce the workload of the worker while keeping a high productivity and a quality in great demand. Furthermore, the use of cyanide in leaching tanks is a necessity in the gold recovery process. This consumption of cyanide must be optimal in these tanks in order to have a good recovery while controlling the concentration of cyanide. Cyanide is one of the most expensive products for mining companies. On a completely different note, we see huge variations during the addition of cyanide. Following a recommendation from the metallurgical and operations teams, the control team carried out an analysis of the problem while proposing a solution to reduce the variability around plus or minus 10% of the addition setpoint through automation. It should be noted that this automatic optimization by monitoring the concentration of cyanide, made use of industrial automation which is a technique which ensures the operation of the ore processing chain without human intervention. In other words, it made it possible to substitute a machine for man. So, this leads us to conduct a study on concentration levels in the real world. The results show that the analysis of the modeling of the cyanide consumption optimization system is an appropriate solution to eradicate failures in the mineral processing chain. The trend curves demonstrate this resolution perfectly.展开更多
Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this work, we calculate the excitation probability of a simple model of a bistable biolo...Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this work, we calculate the excitation probability of a simple model of a bistable biological unit in response to pulsatile inputs, and its spontaneous excitation rate due to noise perturbation. Then we analytically calculate the mutual information, energy cost, and energy efficiency of an array of these bistable units. We find that the optimal number of units could maximize this array＇s energy efficiency in encoding pulse inputs, which depends on the fixed energy cost. We conclude that demand for energy efficiency in biological systems may strongly influence the size of these systems under the pressure of natural selection.展开更多
Notable advancements have been made in the additive manufacturing(AM)of aerospace materials,driven by the needs for integrated components with intricate geometries and small-lot production of high-value components.Nic...Notable advancements have been made in the additive manufacturing(AM)of aerospace materials,driven by the needs for integrated components with intricate geometries and small-lot production of high-value components.Nickel-based superalloys,pivotal materials for high-temperature bearing components in aeroengines,present significant challenges in the fabrication of complex parts due to their great hardness.Huge attention and rapid progress have been garnered in AM processing of nicklebased superalloys,largely owing to its distinct benefits in the freedom of fabrication and reduced manufacturing lifecycle.Despite extensive research into AM in nickel-based superalloys,the corresponding results and conclusions are scattered attributed to the variety of nickel-based superalloys and complex AM processing parameters.Therefore,there is still a pressing need for a comprehensive and deep understanding of the relationship between the AM processing and microstructures and mechanical performance of nickel-based superalloys.This review introduces the processing characteristics of four primary AM technologies utilized for superalloys and summarizes the microstructures and mechanical properties prior to and post-heat treatments.Additionally,this review presents innovative superalloys specifically accommodated to AM processing and offers insights into the material development and performance improvement,aiming to provide a valuable assessment on AM processing of nickel-based superalloys and an effective guidance for the future research.展开更多
Biomass-based hydrocarbon fuels,as one of the alternatives to traditional fossil fuels,have attracted considerable attention in the energy field due to their renewability and environmental benefits.This article provid...Biomass-based hydrocarbon fuels,as one of the alternatives to traditional fossil fuels,have attracted considerable attention in the energy field due to their renewability and environmental benefits.This article provides a systematic review of recent research progress in the chemical synthesis of biomass-based hydrocarbon fuels.It outlines the conversion pathways using feedstocks such as lipids,terpenoids,cellulose/hemicellulose,and lignin.Depending on the feedstock,various products with distinct structural characteristics can be prepared through reactions such as cyclization,condensation,and catalytic hydrogenation.Throughout the synthesis process,three key factors play a critical role:efficient catalyst development,production process optimization,and computational-chemistry-based molecular design.Finally,the article discusses future perspectives for biomass-based hydrocarbon fuel synthesis research.展开更多
Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durabili...Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durability,and corrosion resistance.These metals have body-centered cubic crystal structure,characterized by limited slip systems and impeded dislocation motion,resulting in significant low-temperature brittleness,which poses challenges for the conventional processing.Additive manufacturing technique provides an innovative approach,enabling the production of intricate parts without molds,which significantly improves the efficiency of material usage.This review provides a comprehensive overview of the advancements in additive manufacturing techniques for the production of refractory metals,such as W,Ta,Mo,and Nb,particularly the laser powder bed fusion.In this review,the influence mechanisms of key process parameters(laser power,scan strategy,and powder characteristics)on the evolution of material microstructure,the formation of metallurgical defects,and mechanical properties were discussed.Generally,optimizing powder characteristics,such as sphericity,implementing substrate preheating,and formulating alloying strategies can significantly improve the densification and crack resistance of manufactured parts.Meanwhile,strictly controlling the oxygen impurity content and optimizing the energy density input are also the key factors to achieve the simultaneous improvement in strength and ductility of refractory metals.Although additive manufacturing technique provides an innovative solution for processing refractory metals,critical issues,such as residual stress control,microstructure and performance anisotropy,and process stability,still need to be addressed.This review not only provides a theoretical basis for the additive manufacturing of high-performance refractory metals,but also proposes forward-looking directions for their industrial application.展开更多
Selective Laser Melting(SLM),an advanced metal additive manufacturing technology,offers high precision and personalized customization advantages.However,selecting reasonable SLM parameters is challenging due to comple...Selective Laser Melting(SLM),an advanced metal additive manufacturing technology,offers high precision and personalized customization advantages.However,selecting reasonable SLM parameters is challenging due to complex relationships.This study proposes a method for identifying the optimal process window by combining the simulation model with an optimization algorithm.JAYA is guided by the principle of preferential behavior towards best solutions and avoidance of worst ones,but it is prone to premature convergence thus leading to insufficient global search.To overcome limitations,this research proposes a Differential Evolution-framed JAYA algorithm(DEJAYA).DEJAYA incorporates four key enhancements to improve the flexibility of the original algorithm,which include DE framework design,horizontal crossover operator,longitudinal crossover operator,and global greedy strategy.The effectiveness of DEJAYA is rigorously evaluated by a suite of 23 distinct benchmark functions.Furthermore,the numerical simulation establishes AlSi10Mg single-track formation models,and DEJAYA successfully identified the optimal process window for this problem.Experimental results validate that DEJAYA effectively guides SLM parameter selection for AlSi10Mg.展开更多
The volatilization characteristics and kinetic mechanisms of arsenic were investigated in the temperature range of 623−773 K and pressure ranges of 10−10000 Pa.The experimental results reveal that the evaporation rate...The volatilization characteristics and kinetic mechanisms of arsenic were investigated in the temperature range of 623−773 K and pressure ranges of 10−10000 Pa.The experimental results reveal that the evaporation rate increases with increasing temperature and decreasing pressure.Surface reaction control dominates at low pressures(<100 Pa),whereas diffusion control dominates at high pressures(>5000 Pa).The evaporation behavior is successfully described by an Arrhenius-type model for temperature dependence and Logistic model for pressure dependence.Key kinetic parameters,including the critical pressure,maximum evaporation rate and evaporation coefficient,were calculated.The evaporation coefficient varies between 0.010 and 0.223,and the critical pressures vary between 281 and 478 Pa with temperature.展开更多
Ultra-high-strength aluminumalloy profile is an ideal choice for aerospace structuralmaterials due to its excellent specific strength and corrosion resistance.However,issues such as uneven metal flow,stress concentrat...Ultra-high-strength aluminumalloy profile is an ideal choice for aerospace structuralmaterials due to its excellent specific strength and corrosion resistance.However,issues such as uneven metal flow,stress concentration,and forming defects are prone to occur during their extrusion.This study focuses on an Al-Zn-Mg-Cu ultra-high-strength aluminum alloy profile with a double-U,multi-cavity thin-walled structure.Firstly,hot compression experiments were conducted at temperatures of 350○C,400○C,and 450○C,with strain rates of 0.01 and 1.0 s^(−1),to investigate the plastic deformation behavior of the material.Subsequently,a 3D coupled thermo-mechanical extrusion simulation model was established using Deform-3D to systematically analyze the influence of die structure and process parameters on metal flow velocity,effective stress/strain,and temperature distribution.The simulation revealed significant velocity differences,stress concentration,and uneven temperature distribution.Key parameters,including mesh density,extrusion ratio,die fillet,and bearing length,were optimized through full-factorial experiments.This optimization,combined with a stepped flow-guiding die design,effectively improved the metal flow pattern during extrusion.Trial production based on both the initial and optimized parameters were carried out.A comparative analysis demonstrates that the optimized scheme results in a final profile whose cross-section matches the target design closely,with complete filling of complex features and no obvious forming defects.This research provides a valuable reference for the extrusion process optimization and die design of complex-section profiles made from ultra-high-strength aluminum alloys.展开更多
Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy...Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.展开更多
Variable stiffness composites present a promising solution for mitigating impact loads via varying the fiber volume fraction layer-wise,thereby adjusting the panel's stiffness.Since each layer of the composite may...Variable stiffness composites present a promising solution for mitigating impact loads via varying the fiber volume fraction layer-wise,thereby adjusting the panel's stiffness.Since each layer of the composite may be affected by a different failure mode,the optimal fiber volume fraction to suppress damage initiation and evolution is different across the layers.This research examines how re-allocating the fibers layer-wise enhances the composites'impact resistance.In this study,constant stiffness panels with the same fiber volume fraction throughout the layers are compared to variable stiffness ones by varying volume fraction layer-wise.A method is established that utilizes numerical analysis coupled with optimization techniques to determine the optimal fiber volume fraction in both scenarios.Three different reinforcement fibers(Kevlar,carbon,and glass)embedded in epoxy resin were studied.Panels were manufactured and tested under various loading conditions to validate results.Kevlar reinforcement revealed the highest tensile toughness,followed by carbon and then glass fibers.Varying reinforcement volume fraction significantly influences failure modes.Higher fractions lead to matrix cracking and debonding,while lower fractions result in more fiber breakage.The optimal volume fraction for maximizing fiber breakage energy is around 45%,whereas it is about 90%for matrix cracking and debonding.A drop tower test was used to examine the composite structure's behavior under lowvelocity impact,confirming the superiority of Kevlar-reinforced composites with variable stiffness.Conversely,glass-reinforced composites with constant stiffness revealed the lowest performance with the highest deflection.Across all reinforcement materials,the variable stiffness structure consistently outperformed its constant stiffness counterpart.展开更多
Understanding and clarifying the evolution of microstructure and performance of Al-Zr-Sc alloy wires during processing paths is a crucial issue in developing heat-resistant conductors with high strength and high elect...Understanding and clarifying the evolution of microstructure and performance of Al-Zr-Sc alloy wires during processing paths is a crucial issue in developing heat-resistant conductors with high strength and high electrical conductivity(EC).In this study,the microstructure evolution and corresponding performance changes of Al-0.2Zr-0.06Sc alloy wires produced by three processing paths are investigated.Results indicate that ageing treatment+hot extrusion+cold drawing processing path can produce the highest strength Al-Zr-Sc wires attributed to favorable interactions among precipitation strengthening of Al_(3)(Zr,Sc)phases,grain boundary strengthening and dislocation strengthening.High EC is attained by the hot extrusion+ageing treatment+cold drawing processing path,which reveals the importance of dynamic precipitation of Al_(3)Sc phases during hot extrusion and further precipitation of solute atoms during ageing treatment for improving the EC.The processing path using hot extrusion+cold drawing+ageing treatment achieves the highest EC of the Al-Zr-Sc wire,but the strength decreases significantly due to the loss of dislocation strengthening.Additionally,the pinning effect of Al_(3)Sc and Al_(3)(Zr,Sc)ensures good heat resistance of Al-Zr-Sc wires.These results provide guidance for the process design of Al-Zr-Sc wires with variable combinations of strength and EC.展开更多
The isothermal compression test for Ti-6Al-7Nb alloy was conducted by using Gleeble-3800 thermal simulator.The hot deformation behavior of Ti-6Al-7Nb alloy was investigated in the deformation temperature ranges of 940...The isothermal compression test for Ti-6Al-7Nb alloy was conducted by using Gleeble-3800 thermal simulator.The hot deformation behavior of Ti-6Al-7Nb alloy was investigated in the deformation temperature ranges of 940-1030℃and the strain rate ranges of 0.001-10 s^(-1).Meanwhile,the activation energy of thermal deformation was computed.The results show that the flow stress of Ti-6Al-7Nb alloy increases with increasing the strain rate and decreasing the deformation temperature.The activation energy of thermal deformation for Ti-6Al-7Nb alloy is much greater than that for self-diffusion ofα-Ti andβ-Ti.Considering the influence of strain on flow stress,the strain-compensated Arrhenius constitutive model of Ti-6Al-7Nb alloy was established.The error analysis shows that the model has higher accuracy,and the correlation coefficient r and average absolute relative error are 0.9879 and 4.11%,respectively.The processing map(PM)of Ti-6Al-7Nb alloy was constructed by the dynamic materials model and Prasad instability criterion.According to PM and microstructural observation,it is found that the main form of instability zone is local flow,and the deformation mechanisms of the stable zone are mainly superplasticity and dynamic recrystallization.The optimal processing parameters of Ti-6Al-7Nb alloy are determined as follows:960-995℃/0.01-0.18 s^(-1)and 1000-1030℃/0.001-0.01 s^(-1).展开更多
Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard ...Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process.Besides,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the MVs.In this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch process.The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data.Unlike the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties.The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.展开更多
On the principle of non-incremental algorithm, some basic ideas of process optimal control iterative algorithm, based on the Optimal Control Variational Principle in Mechanics, is proposed in this paper. Then the esse...On the principle of non-incremental algorithm, some basic ideas of process optimal control iterative algorithm, based on the Optimal Control Variational Principle in Mechanics, is proposed in this paper. Then the essential governing equations are presented. This work provides a new method to achieve the numerical solutions of the mechanic of finite deformation.展开更多
This paper studies rotor spinning blended yam produced of spun silk and cashmere. Nine samples were spun, from three different opening rollers and different navels. According to the Uster test results of yam quality p...This paper studies rotor spinning blended yam produced of spun silk and cashmere. Nine samples were spun, from three different opening rollers and different navels. According to the Uster test results of yam quality properties, optimum selection is done by using analysis method of combining fuzzy decision-making and fuzzy pattern classification. Experimental plan is designed based on universal rotated experimental design, and the method of confined optimization is used to optimize the speed of opening roller, the speed of rotor and twist factor, according to the results of yam quality test by Uster.展开更多
In wire arc additive manufacturing(WAAM),a trade-off exists among deposition efficiency,microstructure,and mechanical properties.Addressing this challenge,this work proposes an innovative multi-objective optimization ...In wire arc additive manufacturing(WAAM),a trade-off exists among deposition efficiency,microstructure,and mechanical properties.Addressing this challenge,this work proposes an innovative multi-objective optimization framework tailored for WAAM of AZ31 magnesium alloy components,which integrates deposition efficiency and microstructure as coupled objectives and is resolved through the NSGA-Ⅱ algorithm.The proposed framework employs quadratic regression to correlate process parameters with deposition efficiency through geometric morphology mediation,while addressing uncertainties in WAAM by integrating theoretical insights with data-driven stacked ensemble learning for grain size prediction,establishing the hybrid physics-informed data method for WAAM microstructure prediction.The optimized process achieved a deposition rate of 6257 mm3/min,with effective width and average layer height maintained at 10.1 mm and 4.13 mm,respectively.Microstructural optimization produced a fine,uniform,fully equiaxed grain structure with an average grain size of 38μm.These findings underscore the significant industrial potential of intelligent optimization strategies in WAAM for manufacturing lightweight,high-performance components in aerospace and transportation sectors.展开更多
Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects s...Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.展开更多
The integration of artificial intelligence into the development and production of mechatronic products offers a substantial opportunity to enhance efficiency, adaptability, and system performance. This paper examines ...The integration of artificial intelligence into the development and production of mechatronic products offers a substantial opportunity to enhance efficiency, adaptability, and system performance. This paper examines the utilization of reinforcement learning as a control strategy, with a particular focus on its deployment in pivotal stages of the product development lifecycle, specifically between system architecture and system integration and verification. A controller based on reinforcement learning was developed and evaluated in comparison to traditional proportional-integral controllers in dynamic and fault-prone environments. The results illustrate the superior adaptability, stability, and optimization potential of the reinforcement learning approach, particularly in addressing dynamic disturbances and ensuring robust performance. The study illustrates how reinforcement learning can facilitate the transition from conceptual design to implementation by automating optimization processes, enabling interface automation, and enhancing system-level testing. Based on the aforementioned findings, this paper presents future directions for research, which include the integration of domain-specific knowledge into the reinforcement learning process and the validation of this process in real-world environments. The results underscore the potential of artificial intelligence-driven methodologies to revolutionize the design and deployment of intelligent mechatronic systems.展开更多
1.Colors of chemical reaction engineering models Kinetic models of chemical reactions are a crucial asset for understanding and optimizing chemical processes[1].These models are critical for reactor design,process opt...1.Colors of chemical reaction engineering models Kinetic models of chemical reactions are a crucial asset for understanding and optimizing chemical processes[1].These models are critical for reactor design,process optimization,catalyst design,scale-up,and process control,making them indispensable in the chemical industry.Kinetic models predict the change in temperature and concentration of the relevant species,given an actual concentration and temperature.Reaction predictions are made by integrating the kinetic model with a reactor model,which accounts for external constraints,such as flow,inlet concentration。展开更多
Fiber quality measurement in spinning preparation is crucial for optimizing waste and meeting yarn quality specifications.The brand-new Uster AFIS 6–the next-generation laboratory instrument from Uster Technologies–...Fiber quality measurement in spinning preparation is crucial for optimizing waste and meeting yarn quality specifications.The brand-new Uster AFIS 6–the next-generation laboratory instrument from Uster Technologies–uniquely tests man-made fiber properties in addition to cotton.It provides critical data to optimize fiber process control for cotton,man-made fibers,and blended yarns.展开更多
文摘The optimization system, which was the subject of our study, is an autonomous chain for the automatic management of cyanide consumption. It is in the phase of industrial automation which made it possible to use the machines in order to reduce the workload of the worker while keeping a high productivity and a quality in great demand. Furthermore, the use of cyanide in leaching tanks is a necessity in the gold recovery process. This consumption of cyanide must be optimal in these tanks in order to have a good recovery while controlling the concentration of cyanide. Cyanide is one of the most expensive products for mining companies. On a completely different note, we see huge variations during the addition of cyanide. Following a recommendation from the metallurgical and operations teams, the control team carried out an analysis of the problem while proposing a solution to reduce the variability around plus or minus 10% of the addition setpoint through automation. It should be noted that this automatic optimization by monitoring the concentration of cyanide, made use of industrial automation which is a technique which ensures the operation of the ore processing chain without human intervention. In other words, it made it possible to substitute a machine for man. So, this leads us to conduct a study on concentration levels in the real world. The results show that the analysis of the modeling of the cyanide consumption optimization system is an appropriate solution to eradicate failures in the mineral processing chain. The trend curves demonstrate this resolution perfectly.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11105062 and 11265014the Fundamental Research Funds for the Central Universities under Grant Nos LZUJBKY-2011-57 and LZUJBKY-2015-119
文摘Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this work, we calculate the excitation probability of a simple model of a bistable biological unit in response to pulsatile inputs, and its spontaneous excitation rate due to noise perturbation. Then we analytically calculate the mutual information, energy cost, and energy efficiency of an array of these bistable units. We find that the optimal number of units could maximize this array＇s energy efficiency in encoding pulse inputs, which depends on the fixed energy cost. We conclude that demand for energy efficiency in biological systems may strongly influence the size of these systems under the pressure of natural selection.
基金financially supported by the National Key R&D Program of China(No.2021YFB3702301)the National Natural Science Foundation of China(No.52101068]+2 种基金the China Postdoctoral Science Foundation[No.2022T150342]the Postdoctoral International Exchange Program[No.YJ20210129]the Shuimu Tsinghua Scholar Program(No.2020SM100)
文摘Notable advancements have been made in the additive manufacturing(AM)of aerospace materials,driven by the needs for integrated components with intricate geometries and small-lot production of high-value components.Nickel-based superalloys,pivotal materials for high-temperature bearing components in aeroengines,present significant challenges in the fabrication of complex parts due to their great hardness.Huge attention and rapid progress have been garnered in AM processing of nicklebased superalloys,largely owing to its distinct benefits in the freedom of fabrication and reduced manufacturing lifecycle.Despite extensive research into AM in nickel-based superalloys,the corresponding results and conclusions are scattered attributed to the variety of nickel-based superalloys and complex AM processing parameters.Therefore,there is still a pressing need for a comprehensive and deep understanding of the relationship between the AM processing and microstructures and mechanical performance of nickel-based superalloys.This review introduces the processing characteristics of four primary AM technologies utilized for superalloys and summarizes the microstructures and mechanical properties prior to and post-heat treatments.Additionally,this review presents innovative superalloys specifically accommodated to AM processing and offers insights into the material development and performance improvement,aiming to provide a valuable assessment on AM processing of nickel-based superalloys and an effective guidance for the future research.
基金Support by National Natural Science Foundation of China(22127802,22573091)the HY Action(62402010305)。
文摘Biomass-based hydrocarbon fuels,as one of the alternatives to traditional fossil fuels,have attracted considerable attention in the energy field due to their renewability and environmental benefits.This article provides a systematic review of recent research progress in the chemical synthesis of biomass-based hydrocarbon fuels.It outlines the conversion pathways using feedstocks such as lipids,terpenoids,cellulose/hemicellulose,and lignin.Depending on the feedstock,various products with distinct structural characteristics can be prepared through reactions such as cyclization,condensation,and catalytic hydrogenation.Throughout the synthesis process,three key factors play a critical role:efficient catalyst development,production process optimization,and computational-chemistry-based molecular design.Finally,the article discusses future perspectives for biomass-based hydrocarbon fuel synthesis research.
基金National MCF Energy R&D Program(2024YFE03260300)。
文摘Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durability,and corrosion resistance.These metals have body-centered cubic crystal structure,characterized by limited slip systems and impeded dislocation motion,resulting in significant low-temperature brittleness,which poses challenges for the conventional processing.Additive manufacturing technique provides an innovative approach,enabling the production of intricate parts without molds,which significantly improves the efficiency of material usage.This review provides a comprehensive overview of the advancements in additive manufacturing techniques for the production of refractory metals,such as W,Ta,Mo,and Nb,particularly the laser powder bed fusion.In this review,the influence mechanisms of key process parameters(laser power,scan strategy,and powder characteristics)on the evolution of material microstructure,the formation of metallurgical defects,and mechanical properties were discussed.Generally,optimizing powder characteristics,such as sphericity,implementing substrate preheating,and formulating alloying strategies can significantly improve the densification and crack resistance of manufactured parts.Meanwhile,strictly controlling the oxygen impurity content and optimizing the energy density input are also the key factors to achieve the simultaneous improvement in strength and ductility of refractory metals.Although additive manufacturing technique provides an innovative solution for processing refractory metals,critical issues,such as residual stress control,microstructure and performance anisotropy,and process stability,still need to be addressed.This review not only provides a theoretical basis for the additive manufacturing of high-performance refractory metals,but also proposes forward-looking directions for their industrial application.
文摘Selective Laser Melting(SLM),an advanced metal additive manufacturing technology,offers high precision and personalized customization advantages.However,selecting reasonable SLM parameters is challenging due to complex relationships.This study proposes a method for identifying the optimal process window by combining the simulation model with an optimization algorithm.JAYA is guided by the principle of preferential behavior towards best solutions and avoidance of worst ones,but it is prone to premature convergence thus leading to insufficient global search.To overcome limitations,this research proposes a Differential Evolution-framed JAYA algorithm(DEJAYA).DEJAYA incorporates four key enhancements to improve the flexibility of the original algorithm,which include DE framework design,horizontal crossover operator,longitudinal crossover operator,and global greedy strategy.The effectiveness of DEJAYA is rigorously evaluated by a suite of 23 distinct benchmark functions.Furthermore,the numerical simulation establishes AlSi10Mg single-track formation models,and DEJAYA successfully identified the optimal process window for this problem.Experimental results validate that DEJAYA effectively guides SLM parameter selection for AlSi10Mg.
基金Yunnan Fundamental Research Project,China(No.202201BE070001-056)。
文摘The volatilization characteristics and kinetic mechanisms of arsenic were investigated in the temperature range of 623−773 K and pressure ranges of 10−10000 Pa.The experimental results reveal that the evaporation rate increases with increasing temperature and decreasing pressure.Surface reaction control dominates at low pressures(<100 Pa),whereas diffusion control dominates at high pressures(>5000 Pa).The evaporation behavior is successfully described by an Arrhenius-type model for temperature dependence and Logistic model for pressure dependence.Key kinetic parameters,including the critical pressure,maximum evaporation rate and evaporation coefficient,were calculated.The evaporation coefficient varies between 0.010 and 0.223,and the critical pressures vary between 281 and 478 Pa with temperature.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFB3710805).
文摘Ultra-high-strength aluminumalloy profile is an ideal choice for aerospace structuralmaterials due to its excellent specific strength and corrosion resistance.However,issues such as uneven metal flow,stress concentration,and forming defects are prone to occur during their extrusion.This study focuses on an Al-Zn-Mg-Cu ultra-high-strength aluminum alloy profile with a double-U,multi-cavity thin-walled structure.Firstly,hot compression experiments were conducted at temperatures of 350○C,400○C,and 450○C,with strain rates of 0.01 and 1.0 s^(−1),to investigate the plastic deformation behavior of the material.Subsequently,a 3D coupled thermo-mechanical extrusion simulation model was established using Deform-3D to systematically analyze the influence of die structure and process parameters on metal flow velocity,effective stress/strain,and temperature distribution.The simulation revealed significant velocity differences,stress concentration,and uneven temperature distribution.Key parameters,including mesh density,extrusion ratio,die fillet,and bearing length,were optimized through full-factorial experiments.This optimization,combined with a stepped flow-guiding die design,effectively improved the metal flow pattern during extrusion.Trial production based on both the initial and optimized parameters were carried out.A comparative analysis demonstrates that the optimized scheme results in a final profile whose cross-section matches the target design closely,with complete filling of complex features and no obvious forming defects.This research provides a valuable reference for the extrusion process optimization and die design of complex-section profiles made from ultra-high-strength aluminum alloys.
基金financially supported by the National Key Research and Development Program of China (No. 2023YFB3812601)the National Natural Science Foundation of China (No. 51925401)the Young Elite Scientists Sponsorship Program by CAST, China (No. 2022QNRC001)。
文摘Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.
基金funded by the American University of Sharjah.United Arab Emirates award number EN 9502-FRG19-M-E75。
文摘Variable stiffness composites present a promising solution for mitigating impact loads via varying the fiber volume fraction layer-wise,thereby adjusting the panel's stiffness.Since each layer of the composite may be affected by a different failure mode,the optimal fiber volume fraction to suppress damage initiation and evolution is different across the layers.This research examines how re-allocating the fibers layer-wise enhances the composites'impact resistance.In this study,constant stiffness panels with the same fiber volume fraction throughout the layers are compared to variable stiffness ones by varying volume fraction layer-wise.A method is established that utilizes numerical analysis coupled with optimization techniques to determine the optimal fiber volume fraction in both scenarios.Three different reinforcement fibers(Kevlar,carbon,and glass)embedded in epoxy resin were studied.Panels were manufactured and tested under various loading conditions to validate results.Kevlar reinforcement revealed the highest tensile toughness,followed by carbon and then glass fibers.Varying reinforcement volume fraction significantly influences failure modes.Higher fractions lead to matrix cracking and debonding,while lower fractions result in more fiber breakage.The optimal volume fraction for maximizing fiber breakage energy is around 45%,whereas it is about 90%for matrix cracking and debonding.A drop tower test was used to examine the composite structure's behavior under lowvelocity impact,confirming the superiority of Kevlar-reinforced composites with variable stiffness.Conversely,glass-reinforced composites with constant stiffness revealed the lowest performance with the highest deflection.Across all reinforcement materials,the variable stiffness structure consistently outperformed its constant stiffness counterpart.
基金financially supported by the Key Project of Research and Development in Yunnan Province(Nos.202103AN080001-002 and 202202AG050007-4)Key Project of Yunnan Fundamental Research(No.202101AS070017)the Kunming University of Science and Technology Analysis and Testing Fund(No.2022M20202230013).
文摘Understanding and clarifying the evolution of microstructure and performance of Al-Zr-Sc alloy wires during processing paths is a crucial issue in developing heat-resistant conductors with high strength and high electrical conductivity(EC).In this study,the microstructure evolution and corresponding performance changes of Al-0.2Zr-0.06Sc alloy wires produced by three processing paths are investigated.Results indicate that ageing treatment+hot extrusion+cold drawing processing path can produce the highest strength Al-Zr-Sc wires attributed to favorable interactions among precipitation strengthening of Al_(3)(Zr,Sc)phases,grain boundary strengthening and dislocation strengthening.High EC is attained by the hot extrusion+ageing treatment+cold drawing processing path,which reveals the importance of dynamic precipitation of Al_(3)Sc phases during hot extrusion and further precipitation of solute atoms during ageing treatment for improving the EC.The processing path using hot extrusion+cold drawing+ageing treatment achieves the highest EC of the Al-Zr-Sc wire,but the strength decreases significantly due to the loss of dislocation strengthening.Additionally,the pinning effect of Al_(3)Sc and Al_(3)(Zr,Sc)ensures good heat resistance of Al-Zr-Sc wires.These results provide guidance for the process design of Al-Zr-Sc wires with variable combinations of strength and EC.
基金the National Natural Science Foundation of China(Grant No.51464035).
文摘The isothermal compression test for Ti-6Al-7Nb alloy was conducted by using Gleeble-3800 thermal simulator.The hot deformation behavior of Ti-6Al-7Nb alloy was investigated in the deformation temperature ranges of 940-1030℃and the strain rate ranges of 0.001-10 s^(-1).Meanwhile,the activation energy of thermal deformation was computed.The results show that the flow stress of Ti-6Al-7Nb alloy increases with increasing the strain rate and decreasing the deformation temperature.The activation energy of thermal deformation for Ti-6Al-7Nb alloy is much greater than that for self-diffusion ofα-Ti andβ-Ti.Considering the influence of strain on flow stress,the strain-compensated Arrhenius constitutive model of Ti-6Al-7Nb alloy was established.The error analysis shows that the model has higher accuracy,and the correlation coefficient r and average absolute relative error are 0.9879 and 4.11%,respectively.The processing map(PM)of Ti-6Al-7Nb alloy was constructed by the dynamic materials model and Prasad instability criterion.According to PM and microstructural observation,it is found that the main form of instability zone is local flow,and the deformation mechanisms of the stable zone are mainly superplasticity and dynamic recrystallization.The optimal processing parameters of Ti-6Al-7Nb alloy are determined as follows:960-995℃/0.01-0.18 s^(-1)and 1000-1030℃/0.001-0.01 s^(-1).
基金Supported by the National High Technology Research and Development Program of China(2014AA041803)the National Natural Science Foundation of China(61320106009)
文摘Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process.Besides,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the MVs.In this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch process.The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data.Unlike the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties.The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.
基金the National Natural Science Foundation of China(Grant No.594305050).
文摘On the principle of non-incremental algorithm, some basic ideas of process optimal control iterative algorithm, based on the Optimal Control Variational Principle in Mechanics, is proposed in this paper. Then the essential governing equations are presented. This work provides a new method to achieve the numerical solutions of the mechanic of finite deformation.
文摘This paper studies rotor spinning blended yam produced of spun silk and cashmere. Nine samples were spun, from three different opening rollers and different navels. According to the Uster test results of yam quality properties, optimum selection is done by using analysis method of combining fuzzy decision-making and fuzzy pattern classification. Experimental plan is designed based on universal rotated experimental design, and the method of confined optimization is used to optimize the speed of opening roller, the speed of rotor and twist factor, according to the results of yam quality test by Uster.
基金supported by the National Natural Science Foundation of China(Nos.52475317 and 52305331).
文摘In wire arc additive manufacturing(WAAM),a trade-off exists among deposition efficiency,microstructure,and mechanical properties.Addressing this challenge,this work proposes an innovative multi-objective optimization framework tailored for WAAM of AZ31 magnesium alloy components,which integrates deposition efficiency and microstructure as coupled objectives and is resolved through the NSGA-Ⅱ algorithm.The proposed framework employs quadratic regression to correlate process parameters with deposition efficiency through geometric morphology mediation,while addressing uncertainties in WAAM by integrating theoretical insights with data-driven stacked ensemble learning for grain size prediction,establishing the hybrid physics-informed data method for WAAM microstructure prediction.The optimized process achieved a deposition rate of 6257 mm3/min,with effective width and average layer height maintained at 10.1 mm and 4.13 mm,respectively.Microstructural optimization produced a fine,uniform,fully equiaxed grain structure with an average grain size of 38μm.These findings underscore the significant industrial potential of intelligent optimization strategies in WAAM for manufacturing lightweight,high-performance components in aerospace and transportation sectors.
基金funded by the National Natural Science Foundation of China Youth Fund(Grant No.62304022)Science and Technology on Electromechanical Dynamic Control Laboratory(China,Grant No.6142601012304)the 2022e2024 China Association for Science and Technology Innovation Integration Association Youth Talent Support Project(Grant No.2022QNRC001).
文摘Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.
文摘The integration of artificial intelligence into the development and production of mechatronic products offers a substantial opportunity to enhance efficiency, adaptability, and system performance. This paper examines the utilization of reinforcement learning as a control strategy, with a particular focus on its deployment in pivotal stages of the product development lifecycle, specifically between system architecture and system integration and verification. A controller based on reinforcement learning was developed and evaluated in comparison to traditional proportional-integral controllers in dynamic and fault-prone environments. The results illustrate the superior adaptability, stability, and optimization potential of the reinforcement learning approach, particularly in addressing dynamic disturbances and ensuring robust performance. The study illustrates how reinforcement learning can facilitate the transition from conceptual design to implementation by automating optimization processes, enabling interface automation, and enhancing system-level testing. Based on the aforementioned findings, this paper presents future directions for research, which include the integration of domain-specific knowledge into the reinforcement learning process and the validation of this process in real-world environments. The results underscore the potential of artificial intelligence-driven methodologies to revolutionize the design and deployment of intelligent mechatronic systems.
基金Yannick Ureel and Maarten Dobbelaere acknowledge financial support from the Fund for Scientific Research Flanders(FWO Flanders)respectively through doctoral fellowship grants(1185822N and 1S45522N)The authors acknowledge funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme/ERC(818607).
文摘1.Colors of chemical reaction engineering models Kinetic models of chemical reactions are a crucial asset for understanding and optimizing chemical processes[1].These models are critical for reactor design,process optimization,catalyst design,scale-up,and process control,making them indispensable in the chemical industry.Kinetic models predict the change in temperature and concentration of the relevant species,given an actual concentration and temperature.Reaction predictions are made by integrating the kinetic model with a reactor model,which accounts for external constraints,such as flow,inlet concentration。
文摘Fiber quality measurement in spinning preparation is crucial for optimizing waste and meeting yarn quality specifications.The brand-new Uster AFIS 6–the next-generation laboratory instrument from Uster Technologies–uniquely tests man-made fiber properties in addition to cotton.It provides critical data to optimize fiber process control for cotton,man-made fibers,and blended yarns.
