Military missions in hostile environments are often costly and unpredictable,with squadrons sometimes facing isolation and resource scarcity.In such scenarios,critical components in vehicles,drones,and energy generato...Military missions in hostile environments are often costly and unpredictable,with squadrons sometimes facing isolation and resource scarcity.In such scenarios,critical components in vehicles,drones,and energy generators may require structural reinforcement or repair due to damage.This paper proposes a portable,on-site production method for molds under challenging conditions,where material supply is limited.The method utilizes large format additive manufacturing(LFAM)with recycled composite materials,sourced from end-of-life components and waste,as feedstock.The study investigates the microstructural effects of recycling through shredding techniques,using microscopic imaging.Three potential defense-sector applications are explored,specifically in the aerospace,automotive,and energy industries.Additionally,the influence of key printing parameters,particularly nonparallel plane deposition at a 45-degree angle,on the mechanical behavior of ABS reinforced with 20%glass fiber(GF)is examined.The results demonstrate the feasibility of this manufacturing approach,highlighting reductions in waste material and production times compared to traditional methods.Shorter layer times were found to reduce thermal gradients between layers,thereby improving layer adhesion.While 45-degree deposition enhanced Young's modulus,it slightly reduced interlayer adhesion quality.Furthermore,recycling-induced fiber length reduction led to material degradation,aligning with findings from previous studies.Challenges encountered during implementation included weak part adherence to the print bed and local excess material deposition.Overall,the proposed methodology offers a cost-effective alternative to traditional CNC machining for mold production,demonstrating its potential for on-demand manufacturing in resource-constrained environments.展开更多
The growing penetration of Electric Vehicles(EVs)in transportation brings challenges to power distribution systems due to uncertain usage patterns and increased peak loads.Effective EV fleet charging management strate...The growing penetration of Electric Vehicles(EVs)in transportation brings challenges to power distribution systems due to uncertain usage patterns and increased peak loads.Effective EV fleet charging management strategies are needed to minimize network impacts,such as peak charging power.While existing studies have addressed uncertainties in future arrivals,they often overlook the uncertainties in user-provided inputs of current ongoing charging EVs,such as estimated departure time and energy demand.This paper analyzes the impact of these uncertainties and evaluates three management strategies:a baseline Model Predictive Control(MPC),a data-hybrid MPC,and a fully data-driven Deep Reinforcement Learning(DRL)approach.For data-hybrid MPC,we adopted a diffusion model to handle user input uncertainties and a Gaussian Mixture Model for modeling arrival/departure scenarios.Additionally,the DRL method is based on a Partially Observable Markov Decision Process(POMDP)to manage uncertainty and employs a Convolutional Neural Network(CNN)for feature extraction.Robustness tests under different user uncertainty levels show that the data hybrid MPC performs better on the baseline MPC by 20%,while the DRL-based method achieves around 10%improvement.展开更多
基金Generalitat Valenciana(GVA)and Spanish Ministry of Science and Innovation(Grant Nos.TED2021-130879 B-C21,CIACIF/2021/286,PID2023-151110OB-I00,and CIPROM/2022/3)to provide funds for conducting experiments and software licensessupported by the National Research Foundation,Prime Minister's Office,Singapore under its Campus for Research Excellence and Technological Enterprise(CREATE)programme。
文摘Military missions in hostile environments are often costly and unpredictable,with squadrons sometimes facing isolation and resource scarcity.In such scenarios,critical components in vehicles,drones,and energy generators may require structural reinforcement or repair due to damage.This paper proposes a portable,on-site production method for molds under challenging conditions,where material supply is limited.The method utilizes large format additive manufacturing(LFAM)with recycled composite materials,sourced from end-of-life components and waste,as feedstock.The study investigates the microstructural effects of recycling through shredding techniques,using microscopic imaging.Three potential defense-sector applications are explored,specifically in the aerospace,automotive,and energy industries.Additionally,the influence of key printing parameters,particularly nonparallel plane deposition at a 45-degree angle,on the mechanical behavior of ABS reinforced with 20%glass fiber(GF)is examined.The results demonstrate the feasibility of this manufacturing approach,highlighting reductions in waste material and production times compared to traditional methods.Shorter layer times were found to reduce thermal gradients between layers,thereby improving layer adhesion.While 45-degree deposition enhanced Young's modulus,it slightly reduced interlayer adhesion quality.Furthermore,recycling-induced fiber length reduction led to material degradation,aligning with findings from previous studies.Challenges encountered during implementation included weak part adherence to the print bed and local excess material deposition.Overall,the proposed methodology offers a cost-effective alternative to traditional CNC machining for mold production,demonstrating its potential for on-demand manufacturing in resource-constrained environments.
基金supported by the National Research Foundation,Prime Minister’s Office,Singapore under its Campus for Research Excellence and Technological Enterprise(CREATE)programme.
文摘The growing penetration of Electric Vehicles(EVs)in transportation brings challenges to power distribution systems due to uncertain usage patterns and increased peak loads.Effective EV fleet charging management strategies are needed to minimize network impacts,such as peak charging power.While existing studies have addressed uncertainties in future arrivals,they often overlook the uncertainties in user-provided inputs of current ongoing charging EVs,such as estimated departure time and energy demand.This paper analyzes the impact of these uncertainties and evaluates three management strategies:a baseline Model Predictive Control(MPC),a data-hybrid MPC,and a fully data-driven Deep Reinforcement Learning(DRL)approach.For data-hybrid MPC,we adopted a diffusion model to handle user input uncertainties and a Gaussian Mixture Model for modeling arrival/departure scenarios.Additionally,the DRL method is based on a Partially Observable Markov Decision Process(POMDP)to manage uncertainty and employs a Convolutional Neural Network(CNN)for feature extraction.Robustness tests under different user uncertainty levels show that the data hybrid MPC performs better on the baseline MPC by 20%,while the DRL-based method achieves around 10%improvement.
