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.
Alexander NüßgenAlexander LerchRené DegenMarcus IrmerMartin de FriesFabian RichterCecilia BoströmMargot Ruschitzka
Lunar core samples are the key materials for accurately assessing and developing lunar resources.However,the difficulty of maintaining borehole stability in the lunar coring process limits the depth of lunar coring.Here,a strategy of using a reinforcement fluid that undergoes a phase transition spontaneously in a vacuum environment to reinforce the borehole is proposed.Based on this strategy,a reinforcement liquid suitable for a wide temperature range and a high vacuum environment was developed.A feasibility study on reinforcing the borehole with the reinforcement liquid was carried out,and it is found that the cohesion of the simulated lunar soil can be increased from 2 to 800 kPa after using the reinforcement liquid.Further,a series of coring experiments are conducted using a selfdeveloped high vacuum(vacuum degree of 5 Pa)and low-temperature(between-30 and 50℃)simulation platform.It is confirmed that the high-boiling-point reinforcement liquid pre-placed in the drill pipe can be released spontaneously during the drilling process and finally complete the reinforcement of the borehole.The reinforcement effect of the borehole is better when the solute concentration is between0.15 and 0.25 g/mL.
This paper explores how reinforcement learning(RL)can improve intelligent education systems.RL helps make learning personal,flexible,and efficient by choosing actions based on student needs and rewards like better scores or engagement.We study its use in custom learning paths,smart testing,and teacher support,showing how it beats old methods that don’t adapt.The paper also suggests future ideas—like better RL tools,teamwork learning,and mixing RL with big language models—while noting fairness challenges.Using pretend data with 1000 students,we test RL’s power to plan learning step by step.Results show RL can lift learning by 2025%in areas like tutoring and class focus.This work gives a clear plan for using RL to make education smarter and fairer,pointing to a bright future for adaptive learning.
As non-degradable traditional plastics contribute to environmental pollution,biodegradable polymers have been identified as a promising alternative.However,inherent drawbacks such as low toughness,poor tensile strength,and reduced thermal degradation temperatures limit the further development of biodegradable polymers.Nanocellulose has the potential to enhance the properties of biodegradable polymers without compromising their biodegradability.However,the abundant hydroxyl groups in nanocellulose’s molecular chains result in poor compatibility with hydrophobic polymers,requiring surface modification prior to their combination.This review first introduces several common biodegradable polymers and three types of nanocellulose,followed by a comprehensive analysis of the recent advancements in the chemical modification methods of nanocellulose over the last five years.These methods encompass esterification,oxidation,silylation,and graft modification.The focus of this discussion is primarily on the modification strategies,enhancement effects,and mechanisms.Furthermore,the degradability and applications of modified nanocellulose composites are summarized.Finally,the main challenges hindering the development of chemically modified nanocellulose-reinforced biodegradable polymers are proposed.It is hoped that this review will inspire future researchers to develop industrially valuable chemically modified nanocellulose-reinforced biodegradable polymers.
Shuya ZhangMingda CheRenliang HuangMei CuiWei QiRongxin Su
