Emerging Trends in Damage Tolerance Assessment:A Review of Smart Materials and Self-Repairable Structures

The discipline of damage tolerance assessment has experienced significant advancements due to the emergence of smart materials and self-repairable structures.This review offers a comprehensive look into both traditional and innovative methodologies employed in damage tolerance assessment.After a detailed exploration of damage tolerance concepts and their historical progression,the review juxtaposes the proven techniques of damage assessment with the cutting-edge innovations brought about by smart materials and self-repairable structures.The subsequent sections delve into the synergistic integration of smart materials with self-repairable structures,marking a pivotal stride in damage tolerance by establishing an autonomous system for immediate damage identification and self-repair.This holistic approach broadens the applicability of these technologies across diverse sectors yet brings forth unique challenges demanding further innovation and research.Additionally,the review examines future prospects that combine advanced manufacturing processes with data-centric methodologies,amplifying the capabilities of these‘intelligent’structures.The review culminates by highlighting the transformative potential of this union between smart materials and self-repairable structures,promoting a sustainable and efficient engineering paradigm.

An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

人工智能背景下Materials Project数据库在计算材料学课程教学中的应用

该文探讨了在人工智能背景下,Materials Project数据库在计算材料学课程教学中的应用和影响。Materials Project数据库是一个集成了AI和大数据技术的开放获取的材料库,能为学生提供海量的材料晶体结构和物性数据,使教学内容更为丰富,让学生能通过亲自操作获取和分析数据,深入理解微观结构与物性之间的关系。这一新兴的教学模式不仅提升了学生的科研能力和创新思维能力,还有助于培养具备计算材料专业知识和多学科交叉的复合型人才。总体来说,人工智能时代下,大数据的引入为计算材料学课程带来新的活力,并对未来教育改革和实践产生了积极影响。

Intelligent Molding Proceeding of Composites and Intelligent Manufacturing Systems for Composite Materials

The technology of Intelligent cure operation is set forth according to developing tendency of smart material and structure. Intelligent-system-based tool was developed in order to operate the autoclave cure of a fiber reinforced thermosetting matrix composite laminate in an optimal manner. The objective function is comforts for minimizing the total cure time, uniforming the temperature distribution, controling exothermal and minimizing the process-induced residual stresses in the laminate. Data is analyzed on-line to determine the trends in real-time. The results from application of this overall strategy for the curing of composites are presented.

Research progress of cholesteric liquid crystals with broadband reflection characteristics in application of intelligent optical modulation materials

Cholesteric liquid crystals（CLCs） have recently sparked an enormous amount of interest in the development of soft matter materials due to their unique ability to self-organize into a helical supra-molecular architecture and their excellent selective reflection of light based on the Bragg relationship.Nowadays,by the virtue of building the self-organized nanostructures with pitch gradient or non-uniform pitch distribution,extensive work has already been performed to obtain CLC films with a broad reflection band.Based on authors＇ many years＇ research experience,this critical review systematically summarizes the physical and optical background of the CLCs with broadband reflection characteristics,methods to obtain broadband reflection of CLCs,as well as the application in the field of intelligent optical modulation materials.Combined with the research status and the advantages in the field,the important basic and applied scientific problems in the research direction are also introduced.

Design and manufacture of intelligent Cu-based wet friction materials

The friction sheets working process was analyzed. It is found that its characteristic is microregion instantaneous high temperature and the current cooling method, making the sheets cooled by the lubricating oil flowing through the friction surface, is not very efficient. Then, intelligent materials concept was introduced, the component and microstructure of intelligent Cu-based friction materials were designed, and the intelligent Cu-based wet friction materials as well as sheets were manufactured. And the intelligent friction materials working principle, i.e. the materials cooling the friction microregion in real time or the friction sheets cutting the peak value of microregion instantaneous high temperature during friction process, was given depending on the characteristics of the materials’ and friction sheets’ working process. Finally, it is indicated that the intelligent friction sheets excell the currently used friction sheets in properties, including anti-heating property, anti-wearing property as well as friction characteristic.

Materials Science-Technology and Intelligent Engineering

This paper analyses the peculiar acting mechanism of artificial neural network (ANN) tech, and explores the great immediate significence for the intelligent sci-tech (IST) to research and develop the nano-tech.

Editorial:Mechanics of intelligent materials and structures

Recently, intelligent or smart materials and structures have been received more and more attention due to their distinguished multi-field coupling properties and wide applications in aerospace, automobiles, civil structures, medical devices, information storage, energy harvesting and so on. It is of academic challenge to fully understand the complex multi-field coupling behaviors of various smart materials and structures, and of engineering sig- nificance to enhance the performance and reliability of these materials and structures in industrial applications. The papers in the special topic of Mechanics of Intelligent Materials and Structures focus on the understanding of the electromechanical, magneto-elastic, and magneto-rheological coupling behav- iors and properties of smart materials and structures for applications in vibration control, resonators, and various functional devices.

Damage Detection of Composite Material Intelligent Structure with a New Photoelectric System

A kind of photoelectric system that is suitable to measuring and to testing the damage of the composite material intelligent structure was presented. It can measure the degree of damage of the composite intelligent structure and it also can tell us the damage position in the structure. This system consists of two parts ： software and hardware. Experiments of the damage detection and the analysis of the composite material structure with the photoelectric system were performed, and a series of damage detection experiments was conducted. The results prove that the performance of the system is well and the effects of the measure and test are evident. Through all the experiments, the damage detection technology and test system are approved to be real-time, effective and reliable in the damage detection of the composite intelligent structure.

2D multifunctional devices:from material preparation to device fabrication and neuromorphic applications

Neuromorphic computing systems,which mimic the operation of neurons and synapses in the human brain,are seen as an appealing next-generation computing method due to their strong and efficient computing abilities.Two-dimensional (2D) materials with dangling bond-free surfaces and atomic-level thicknesses have emerged as promising candidates for neuromorphic computing hardware.As a result,2D neuromorphic devices may provide an ideal platform for developing multifunctional neuromorphic applications.Here,we review the recent neuromorphic devices based on 2D material and their multifunctional applications.The synthesis and next micro–nano fabrication methods of 2D materials and their heterostructures are first introduced.The recent advances of neuromorphic 2D devices are discussed in detail using different operating principles.More importantly,we present a review of emerging multifunctional neuromorphic applications,including neuromorphic visual,auditory,tactile,and nociceptive systems based on 2D devices.In the end,we discuss the problems and methods for 2D neuromorphic device developments in the future.This paper will give insights into designing 2D neuromorphic devices and applying them to the future neuromorphic systems.

MatChat: A large language model and application service platform for materials science

The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence(GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data.This specialized AI model, named Mat Chat, focuses on predicting inorganic material synthesis pathways. Mat Chat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although Mat Chat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. Mat Chat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.

Smart epidermal electrophysiological electrodes:Materials,structures,and algorithms

Epidermal electrophysiological monitoring has garnered significant attention for its potential in medical diagnosis and healthcare,particularly in continuous signal recording.However,simultaneously satisfying skin compliance,mechanical properties,environmental adaptation,and biocompatibility to avoid signal attenuation and motion artifacts is challenging,and accurate physiological feature extraction necessitates effective signal-processing algorithms.This review presents the latest advancements in smart electrodes for epidermal electrophysiological monitoring,focusing on materials,structures,and algorithms.First,smart materials incorporating self-adhesion,self-healing,and self-sensing functions offer promising solutions for long-term monitoring.Second,smart meso-structures,together with micro/nanostructures endowed the electrodes with self-adaption and multifunctionality.Third,intelligent algorithms give smart electrodes a“soul,”facilitating faster and more-accurate identification of required information via automatic processing of collected electrical signals.Finally,the existing challenges and future opportunities for developing smart electrodes are discussed.Recognized as a crucial direction for next-generation epidermal electrodes,intelligence holds the potential for extensive,effective,and transformative applications in the future.

In situ transmission electron microscopy and artificial intelligence enabled data analytics for energy materials

Energy materials are vital to energy conversion and storage devices that make renewable resources viable for electrification technologies. In situ transmission electron microscopy(TEM) is a powerful approach to characterize the dynamic evolution of material structure, morphology, and chemistry at the atomic scale in real time and in operando. In this review, recent advancements of in situ TEM techniques for studying energy materials, including catalysts, batteries, photovoltaics, and thermoelectrics, are systematically discussed and summarized. The topics include a broad range of material transformations that are in situ stimulated by heating, biasing, lighting, electron-beam illuminating, and cryocooling under vacuum, liquid, or gas environments within TEM, as well as the mechanistic understanding of the associated solid-solid, solid-liquid, and solid-gas reactions elucidated by in situ TEM examination and operando measurements. Special focus is also put on the emerging progress of artificial intelligence enabled microscopy data analytics, including machine learning enhanced tools for retrieving useful information from massive TEM imaging, diffraction, and spectroscopy datasets, highlighting its merits and potential for automated in situ TEM experimentation and analysis. Finally, the pressing challenges and future perspectives on in situ TEM study for energy-related materials are discussed.

ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples, the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

Systems Theory-Driven Framework for AI Integration into the Holistic Material Basis Research of Traditional Chinese Medicine

This paper introduces a systems theory-driven framework to integration artificial intelligence(AI)into traditional Chinese medicine(TCM)research,enhancing the understanding of TCM’s holistic material basis while adhering to evidence-based principles.Utilizing the System Function Decoding Model(SFDM),the research progresses through define,quantify,infer,and validate phases to systematically explore TCM’s material basis.It employs a dual analytical approach that combines top-down,systems theory-guided perspectives with bottom-up,elements-structure-function methodologies,provides comprehensive insights into TCM’s holistic material basis.Moreover,the research examines AI’s role in quantitative assessment and predictive analysis of TCM’s material components,proposing two specific AIdriven technical applications.This interdisciplinary effort underscores AI’s potential to enhance our understanding of TCM’s holistic material basis and establishes a foundation for future research at the intersection of traditional wisdom and modern technology.

Study of Detecting Impact Damage for Composite Material Based on Intelligent Sensor

A system of impact damage detection for composite material structures by using an intelligent sensor embedded in composite material is described. In the course of signal processing, wavelet transform has the exceptional property of temporal frequency localization, whereas Kohonen artificial neural networks have excellent characteristics of self-learning and fault-tolerance. By combining the merits of abstracting time-frequency domain eigenvalues and improving the ratio of signal to noise in this system, impact damage in composite material can be properly recognized.

Research status and development directions of intelligent drilling fluid technologies

By reviewing the current status of drilling fluid technologies with primary intelligence features at home and abroad,the development background and intelligent response mechanisms of drilling fluid technologies such as variable density,salt response,reversible emulsification,constant rheology,shape memory loss prevention and plugging,intelligent reservoir protection and in-situ rheology control are elaborated,current issues and future challenges are analyzed,and it is pointed out that intelligent material science,nanoscience and artificial intelligence theory are important methods for future research of intelligent drilling fluid technology of horizontal wells with more advanced intelligent features of"self-identification,self-tuning and self-adaptation".Based on the aforementioned outline and integrated with the demands from the drilling fluid technology and intelligent drilling fluid theory,three development suggestions are put forward:(1)research and develop intelligent drilling fluids responding to variable formation pressure,variable formation lithology and fluid,variable reservoir characteristics,high temperature formation and complex ground environmental protection needs;(2)establish an expert system for intelligent drilling fluid design and management;and(3)establish a real-time intelligent check and maintenance processing network.

Biodegradable materials for bone defect repair

Compared with non-degradable materials,biodegradable biomaterials play an increasingly important role in the repairing of severe bone defects,and have attracted extensive attention from researchers.In the treatment of bone defects,scaffolds made of biodegradable materials can provide a crawling bridge for new bone tissue in the gap and a platform for cells and growth factors to play a physiological role,which will eventually be degraded and absorbed in the body and be replaced by the new bone tissue.Traditional biodegradable materials include polymers,ceramics and metals,which have been used in bone defect repairing for many years.Although these materials have more or fewer shortcomings,they are still the cornerstone of our development of a new generation of degradable materials.With the rapid development of modern science and technology,in the 21 st century,more and more kinds of new biodegradable materials emerge in endlessly,such as new intelligent micro-nano materials and cell-based products.At the same time,there are many new fabrication technologies of improving biodegradable materials,such as modular fabrication,3 D and 4 D printing,interface reinforcement and nanotechnology.This review will introduce various kinds of biodegradable materials commonly used in bone defect repairing,especially the newly emerging materials and their fabrication technology in recent years,and look forward to the future research direction,hoping to provide researchers in the field with some inspiration and reference.

Conceptual Issues in the Qualification of Intelligent Buildings

The reality of global warming must have been settled by now while the incidence of same has in very recent times adopted unprecedented dimensions. The global community continues to look for ways to combat the impact of climate change and technology is looked upon to deliver the innovations that would ensure a better tomorrow today. Rapid advancement of Information Technology (IT), is now transforming the way we create and interact with the built environment with the notion of Intelligent Buildings (IBs) underscoring its main features. However, these IBs utilize systems that require energy, and fossil fuels are currently the world’s primary energy sources;they can also irreparably harm the environment, exacerbating climate change. What then is the true essence of IBs? This paper, through review of existing literature, attempts to explore some issues associated with the conceptualization of IBs, highlighting how they are similar with other notional options that deliver the same benefits but without the needed IT systems or the energy required in running them. It also discusses the need to focus on less energy demanding and management approaches at design or occupancy of buildings as a way to reduce the demand and thus consumption of fossil fuels across the world.