Towards data-efficient mechanical design of bicontinuous composites usinggenerative AI

The distribution of material phases is crucial to determine the composite’s mechanical property.While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases,this relationship is difficult to be revealed for complex irregular distributions,preventing design of such material structures to meet certain mechanical requirements.The noticeable developments of artificial intelligence(AI)algorithms in material design enables to detect the hidden structure-mechanics correlations which is essential for designing composite of complex structures.It is intriguing how these tools can assist composite design.Here,we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading.We find that generative AI,enabled through fine-tuned Low Rank Adaptation models,can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution.The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness,fracture and robustness of the material with one model,and such has to be done by several different experimental or simulation tests.This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions.

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.

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.

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.

Automatic well test interpretation based on convolutional neural network for a radial composite reservoir

An automatic well test interpretation method for radial composite reservoirs based on convolutional neural network(CNN) is proposed, and its effectiveness and accuracy are verified by actual field data. In this paper, based on the data transformed by logarithm function and the loss function of mean square error(MSE), the optimal CNN is obtained by reducing the loss function to optimize the network with "dropout" method to avoid over fitting. The trained optimal network can be directly used to interpret the buildup or drawdown pressure data of the well in the radial composite reservoir, that is, the log-log plot of the given measured pressure variation and its derivative data are input into the network, the outputs are corresponding reservoir parameters(mobility ratio, storativity ratio, dimensionless composite radius, and dimensionless group characterizing well storage and skin effects), which realizes the automatic initial fitting of well test interpretation parameters. The method is verified with field measured data of Daqing Oilfield. The research shows that the method has high interpretation accuracy, and it is superior to the analytical method and the least square method.

ChatGPT as an Educational Tool: Opportunities, Challenges, and Recommendations for Communication, Business Writing, and Composition Courses

This empirical study examines ChatGPT as an educational and learning tool.It investigates the opportunities and challenges that ChatGPT provides to the students and instructors of communication,business writing,and composition courses.It also strives to provide recommendations.After conducting 30 theory-based and application-based ChatGPT tests,it is found that ChatGPT has the potential of replacing search engines as it provides accurate and reliable input to students.For opportunities,the study found that ChatGPT provides a platform for students to seek answers to theory-based questions and generate ideas for application-based questions.It also provides a platform for instructors to integrate technology in classrooms and conduct workshops to discuss and evaluate generated responses.For challenges,the study found that ChatGPT,if unethically used by students,may lead to human unintelligence and unlearning.This may also present a challenge to instructors as the use of ChatGPT negatively affects their ability to differentiate between meticulous and automation-dependent students,on the one hand,and measure the achievement of learning outcomes,on the other hand.Based on the outcome of the analysis,this study recommends communication,business writing,and composition instructors to(1)refrain from making theory-based questions as take-home assessments,(2)provide communication and business writing students with detailed case-based and scenario-based assessment tasks that call for personalized answers utilizing critical,creative,and imaginative thinking incorporating lectures and textbook material,(3)enforce submitting all take-home assessments on plagiarism detection software,especially for composition courses,and(4)integrate ChatGPT generated responses in classes as examples to be discussed in workshops.Remarkably,this study found that ChatGPT skillfully paraphrases regenerated responses in a way that is not detected by similarity detection software.To maintain their effectiveness,similarity detection software providers need to upgrade their software to avoid such incidents from slipping unnoticed.

How Artificial Intelligence Supports Airbus Helicopters to Develop, Qualify and Certify Faster

Nowadays,artificial intelligence is increasingly used to develop and support progress in many fields and industries,such as finance,medical,transportation…,especially for complex problem resolution.The paper presents how Airbus Helicopters introduces artificial intelligence in material&process activities,aiming,amongst other things,to reduce the time to market and optimize qualification then certification costs/risks.The paper integrates the results of a proof of concept,achieved on flame resistance behavior of composite materials,related to interior compartment/cargo self-extinguishing requirements(EASA regulation for Rotorcraft CS27/29§853 and 855)and demonstrates how artificial intelligence supports engineering activities.The significant novelty introduced in this work is the use of advanced data-analysis software to support engineers and experts throughout development and qualification steps.Within this study,various artificial intelligence(AI)models have been trained using available experimental datasets from Airbus Helicopters and suppliers as described in Fig.1.Following that,the trained AI model has permitted to identify the most influencing parameters and allowed to focus interest on both critical and optimal setups to help materials experts to reach targets in terms of material performance.In addition,AI model also allows predicting the fire behavior of the material,for resin/fiber reinforcement/fire agent combinations that have not been tested experimentally.This point could be particularly useful for material development purpose.This work demonstrates that,thanks to artificial intelligence support,Airbus Helicopters has improved its understanding of complex phenomenalike flame resistance behavior.Main influencing parameters have been identified for the different tests configurations.And for each parameter,strong/weak ranges have been established.Doing tests in such critical conditions during materials screening phase should help to avoid failing tests in representative helicopter configurations and permit to speed up helicopter development and certification.The presented study also paves the way for material and processes optimizations for helicopter designs.

纳米复合智能防腐涂层在金属表面上的应用研究

智能防腐涂料能可有效地提高金属的使用寿命,因而具有自愈功能的智能防腐涂料越来越受到人们的重视。概述了pH响应、光刺激响应、离子响应等不同触发机制的纳米智能防腐机制研究,总结了不同金属表面纳米复合智能防腐涂层研究进展,最后提出金属表面智能自修复防腐涂层发展所面临的挑战和未来发展前景。

矿物组分识别与智能解释在不同岩性之间的信息共享与迁移学习

在地球科学领域,岩石微观观测数据的采集过程繁琐且效率低下,这不仅增加了研究成本,降低了可靠性,同时也限制了数据的开源共享。此外,由于岩性的多样性和观测手段的差异,单一数据集的规模通常较小,这对于依赖大规模数据集的深度学习框架而言是一大挑战。为此,本研究探索迁移学习如何促进不同岩性间的信息共享,并通过此机制提高矿物组分识别与智能解释任务的模型性能。通过采集不同区域、岩性、矿物组分和偏光模式下的铸体薄片样本,本文深入研究了深度学习模型在不同观测对象和手段下的迁移学习机制,并聚焦于探索地质信息的深层表征。研究成果不但揭示了迁移学习在促进地质学领域信息共享与模型性能提升中的关键作用,还为自动化和智能化地质认识融合奠定了基础。实验结果显示,通过迁移学习,本文模型在智能解释任务中的准确率显著提高,从53.3%提高至98.73%,而在矿物组分识别任务中,准确率也实现了近10%的提升。这些成果证明了迁移学习在地质学领域内解决实际问题和提高模型泛化能力、性能和稳定性方面的巨大潜力。

基于复合图双卷积神经网络的路面裂缝识别方法

为了建立一种基于深度学习卷积神经网络的路面检测模型,提高特殊路面裂缝,如白裂缝、浅裂缝、潮湿裂缝、修补开裂等的识别准确率,在单卷积神经网络结构(单网络)上,提出了基于复合图双卷积神经网络的路面裂缝识别方法。首先,该方法在输入灰度图基础上,考虑裂缝病害图像特征,增加对应二值图组成复合图通道;其次,在单网络结构基础上增加一个针对特殊裂缝识别的单网络,非特殊裂缝网络训练使用全部数据,特殊裂缝网络训练使用特殊裂缝数据,两个网络参数分别独立更新,从而形成复合图双网络结构;然后两个网络分别对同一测试数据进行判定,得出各自的概率矩阵,最后再根据概率单侧抑制的原理将两个单网络输出结果进行叠加,得出最终识别结果。组织了70万张检测车采集图片对复合图双网络方法进行训练和测试。结果表明,复合图双网络识别重叠度、精确度、召回率显著优于灰度图单网络,证明了提出的两处优化,即将单通道灰度图改造为双通道复合输入图和增加一个特殊裂缝识别网络,提升了非特殊裂缝与特殊裂缝区域识别能力。此外,复合图双网络的重叠度、精确度、召回率指标比其他深度学习路面裂缝识别算法方法高。

BPA可逆热敏变色复合材料制备方法及其应用研究进展

综述了BPA可逆热敏变色材料的制备及其应用,以及复配法、无机包覆法、微胶囊法、湿成膜法和近年来的2,6-单取代双酚A复合膜新方法,并分析了2,6-单取代法中膜结晶相变与二醛、酚醛树脂及胶种的关系。2,6-单取代双酚A线型或芳香酚醛树脂及其复合膜有机体系的设计与研究将进一步推动该类材料在具有可调的迟滞响应相变智能窗口,超低温变色敏感性、变色深度、稳定性,环境友好型合成树脂及塑料及实验教学等方面的应用。

人工智能视域下的数字化特定人物知识构成分析

针对人工智能模拟和还原特定人物的场景,文章从认识论和知识论的角度分析特定人物的个人知识构成。文章首先对人工智能视域下的知识进行界定,然后以南朝梁的人物萧绎作为研究标本,研究发现,其所具有知识的底层是人类共有的具身性知识,中层是历史性模因知识、历史性朴素理论知识,表层是个人习得知识与个人经验知识,另外还有历史性社会事理知识和个性事理知识作为连接事件与经验的推理知识。知识构成分析能够起到知识校正与锚定的作用,同时通过知识构建和逻辑推断才可能突破现有知识与有记录知识的局限。

“智能技术+工学专业+课程思政”融合教学探索——以复合材料工学课程为例

作为复合材料与工程本科专业的重要专业课程,复合材料工学是复合材料专业学生或其他相关专业学生从事复合材料学习、科学研究、生产和应用等必须掌握的主要课程。智能技术的发展是当今社会信息化和进步的要求,作为引领教育方向的思政教育,是为国家培养素质过硬人才的初心和使命。该文探索在复合材料工学课程中进行智能模块化切入的方法,把握要领将课程思政教育有机融入课程的途径,研究将工学、智能技术、课程思政教育及智慧教学合为一体的操作,对复合材料专业课程的课程思政教育提出拓展思考。

基于多种群协同优化的叠合板智能拆分设计

在装配式建筑中,叠合楼板的拆分主要是基于规则的半自动方式完成,其设计过程耗费大量时间和人力,难以获得最优解;同时,采用基于规则的方法存在构件规格多的问题,增加了构件加工的成本和时间。为了解决上述问题,本文提出基于多种群协同优化的叠合楼板智能拆分方法,以提高叠合楼板的标准化和模数化程度。将叠合板规格和数量、接缝宽度规格作为优化目标,每个叠合板位置和尺寸作为优化变量,考虑单构件的限重、限宽等加工和运输要求,建立多目标优化模型。采用变量关联性分析对叠合楼板所有布置区域进行分组划分,原高维优化问题转化为低维优化问题。通过上海某装配式建筑住宅项目的叠合楼板拆分实例应用,表明本文提出的叠合楼板智能拆分方法能够高效、快速地生成全部楼板的规格、数量和位置;相比基于规则的设计方法,本文提出的方法能够一定程度提高叠合板的标准化和模数化水平,且拆分结果满足设计规范和施工要求,从而验证了智能拆分方法的可行性和有效性,有利于装配式建筑的推广。

数字技术创新链与产业链的融合升级研究——以新一代人工智能为例

实现高水平科技自立自强要求全力打好关键核心技术攻坚战,创新链与产业链深度融合升级是实现关键核心技术创新的重要抓手。将多元主体协同纳入数字技术创新链与产业链的融合升级系统中,探究数字技术创新链与产业链融合机理;依据多因素复合系统指标体系,运用静态和动态复合系统协同度,以我国新一代人工智能产业为研究对象,确定数字技术创新链与产业链融合发展与升级路径。研究表明:人工智能创新链与产业链融合强度、深度不够,各环节运行发展呈现阶段性特征;知识创新环节发展速度与协同水平有待提高,创新网络环境系统在静态与动态模型测度中协同度数值最高,成为人工智能创新链与产业链融合发展的重要环节。进一步分析发现,我国数字技术创新链与产业链融合阶段可划分为多元共生、双向协同与有效融合三阶段,基于此,提出双链融合升级的三大路径。最后,从稳固各环节协同运行、协调创新资源配置和发挥企业创新主体功能三方面提出相应政策建议,以期为我国人工智能领域突破关键核心技术,实现双链融合发展提供相应的理论思考和政策启示。

车用燃料电池系统控制策略综述

针对车用燃料电池系统控制策略,从系统结构、控制对象、控制目标、控制方法等方面总结了其研究现状和发展趋势。在系统结构设计方面,需要研发高性能关键部件,简化控制系统结构,降低控制难度。在控制对象方面,针对强耦合物理量的解耦控制有待深入研究。在控制目标方面,多目标优化控制策略将是未来的研究重点。在控制方法方面,复合控制策略和基于学习的智能控制策略将是未来的研究方向。

磁性镓基液态金属复合材料的研究进展

镓基液态金属具有高导电性、高导热性、高流动性和优良的生物相容性,是一种具有很大发展和应用前景的功能材料。镓基液态金属与其他不同物理化学性质的材料组合而成的镓基液态金属复合材料为基础研究与应用研究提供了一个新的方向,为解决柔性电子、热调控和生物医学等各个领域的挑战性问题提供了新的思路并表现出重大潜力。磁性粒子的引入可以使镓基液态金属具有良好的磁性能。作为一种新兴的磁性功能材料或磁性智能材料,磁性镓基液态金属复合材料在柔性电子、微电机、靶向给药、热调控、屏蔽与吸波等领域已展现出初步的研究和应用价值。本文系统地总结和评述了磁性镓基液态金属复合材料的研究进展。从基础研究、磁力控制、磁热控温、屏蔽与吸波四个方面综述了磁性镓基液态金属复合材料的性能和应用,说明了这种复合策略的有效性。这可为磁性镓基液态金属复合材料的进一步研究与开发提供思路。

面向AI时代的纤维增强树脂基复合材料工艺仿真

纤维增强树脂基复合材料制造工艺是保证其产品结构效率和应用可靠性的关键,通过计算机进行工艺仿真是提高复合材料制造质量与降低制造成本的重要手段。传统工艺仿真依赖于制造过程中的物理化学机理,通过有限元/有限体积等数值计算方法,以及计算机图形学等辅助设计方法来求解相关机理模型的数学方程,目前已在增强体/预浸料的铺覆、树脂的渗透流动、热固性树脂的固化行为、热传导与热交换、非线性力学及残余应力与固化变形预测等方面得到广泛应用。近年来,人工智能(AI)的迅猛发展,其技术基础机器学习(ML)与人工神经网络(ANN)相结合,已用于增强体铺覆、液体成型工艺和热压罐工艺领域,主要目的是数据挖掘和建立降阶模型。前者可以建立工艺条件与制件固化质量、力学性能等之间的关系,后者则可以提高工艺仿真的计算效率。然而受限于纤维增强树脂基复合材料制造过程复杂、不可测、成本高的特点,在AI时代的起点,仅依赖实验获得的数据量难以满足ML的要求,同时数据驱动AI还面临模型代表性、普适性、可解释性不确定的问题。因此,基于物理化学机理的传统工艺仿真可为数据驱动ML仿真提供大量可靠数据,进而通过AI建立更多描述复合材料工艺的定量模型,扩展工艺仿真可计算的过程;同时,通过AI技术提高计算效率后,满足实时性要求的工艺仿真可进化为制造过程的数字孪生(DT),从而可为复合材料降低成本、提高全寿命周期管理的科学性提供新的技术支撑。

量子计算与音乐创作的交汇:《旋转——Q1合成器三重奏》(Spinnings—Q1 Synth Trio)

量子算法作曲伴随AI音乐时代的到来,俨然成为计算机音乐领域的热点话题。巴西室内乐和电子音乐作曲家爱德华多·雷克·米兰达(Eduardo Reck Miranda),在英国普利茅斯大学计算机音乐研究跨学科中心(ICCMR)主持与牛津大学合作的项目时,创作的交互式量子音乐作品《旋转——Q1合成器三重奏》被誉为量子算法作曲开篇之作。文章以该作品在伦敦歌德学院音乐首演现场实况版本作为研究对象,通过交互系统搭建、映射工具设计、量子算法编程三个维度解析量子算法作曲技术路径,旨在尝试揭示这一新兴音乐创作方式的原理、属性与规律,以此探索音乐艺术与科技融合创新的更多可能性。

基于PSO的飞行器姿态智能控制技术研究

以气动舵/推力矢量/直接力复合控制飞行器为对象,研究了多维异构复合姿态控制系统的控制律及分配律一体化智能设计问题。首先提出了基于PSO(Particle Swarm Optimization)算法的“智能控制律+智能分配律”飞行器姿态多维异构智能复合控制算法框架,并建立了控制系统数学模型,然后重点研究了控制律及分配律参数的智能优化算法实现方法,最后通过数学仿真验证了提出方法的有效性。仿真结果表明,所设计智能优化复合控制系统在标称模型以及模型摄动情况下均能够实现对姿态角指令的准确快速跟踪。