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data-driven design of Ni-based turbine disc superalloys to improve yield strength

Journal of Materials Science & Technology 2023年第24期155卷 175-191页

作者： Bin Xu Haiqing Yin Xue Jiang Cong Zhang Ruijie Zhang Yongwei Wang Xuanhui Qu Zhenghua Deng Guoqiang Yang Dil Faraz Khan Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing 100083 China Collaborative Innovation Center of Steel Technology University of Science and Technology Beijing Beijing 100083 China Beijing Key Laboratory of Materials Genome Engineering University of Science and Technology Beijing Beijing 100083 China Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing 100083 China School of Mechanical Engineering Chongqing Three Gorges University Chongqing 404000 China School of Mechanical and Equipment Engineering Hebei University of Engineering Handan 056038 China Department of Physics University of Science and Technology Bannu Bannu 28100 Pakistan

Increasing the thrust-weight ratio of aeroengines requires development of high-strength and stable high-temperature materials. A data-driven design of Ni-based turbine disc superalloys is performed to improve the yield strength to reach the target. Through first-principles calculations determining the design superalloy system, the theoretical models and Calculation of Phase Diagram (CALPHAD) screening compositions, and machine learning extrapolating prediction, 14 compositions are selected from 2,865,039 composition combinations. Ni-17Cr-8Co-1Mo-1W-6Al-3Ti-1Nb-1Ta is selected to verify the design accuracy. Experimental tests prove that the designed alloy has trade-offs of microstructure with satisfying design targets, and then, the yield strength is higher in the designed alloy than in commercial superalloys, reaching 728 MPa at 850 ℃. A scheme for increasing the performance of the designed alloy is proposed by discussing the strengthening mechanisms, machine learning process, and alloying chemistry effect. The cross-scale data-driven design is regarded as an accurate and efficient way to design novel high-strength Ni-based turbine disc superalloys, whose significance is the obvious reduction of trial-and-error tests.

关键词： Ni-based superalloys data-driven design Machine learning CALPHAD First-principles calculation

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Reduction of data amount in data-driven design of linear quadratic regulators

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Control Theory and Technology 2024年第4期22卷 532-542页

作者： Shinsaku Izumi Xin Xin School of Systems Engineering Kochi University of TechnologyKamiKochi782-8502Japan School of Automation Southeast Universityand Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of EducationNanjing210096JiangsuChina

This paper discusses the data-driven design of linear quadratic regulators,i.e.,to obtain the regulators directly from experimental data without using the models of *** particular,we aim to improve an existing design method by reducing the amount of the required experimental *** the data amount leads to the cost reduction of experiments and computation for the data-driven *** present a simplified version of the existing method,where parameters yielding the gain of the regulator are estimated from only part of the data required in the existing *** then show that the data amount required in the presented method is less than half of that in the existing method under certain *** addition,assuming the presence of measurement noise,we analyze the relations between the expectations and variances of the estimated parameters and the *** a result,it is shown that using a larger amount of the experimental data might mitigate the effects of the noise on the estimated *** results are verified by numerical examples.

关键词： data-driven design Linear quadratic regulators Linear systems Riccati equation Stochastic properties

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Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel

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Science China(Technological Sciences) 2023年第7期66卷 2069-2079页

作者： SHANG ChunLei WANG ChuanJun WU HongHui LIU WenYue CHEN YiMian PAN GuangFei WANG ShuiZe WU GuiLin GAO JunHeng ZHAO HaiTao ZHANG ChaoLei MAO XinPing Beijing Advanced Innovation Center for Materials Genome Engineering Innovation Research Institute for Carbon NeutralityUniversity of Science and Technology BeijingBeijing 100083China State Key Laboratory of Metal Materials for Marine Equipment and Application Anshan 114009China Ansteel Beijing Research Institute Co.Ltd. Beijing 102200China

Pipeline transportation is one of the most economical ways to transport crude oil and natural gas over long *** toughness is one of the important qualities of pipeline steel to ensure safe transportation,wherein a key factor characterizing toughness is Charpy impact toughness(CIT).In this work,according to the production line data provided by a steel mill and the experimental data collected in literature,two machine learning model construction strategies were *** was based solely on the production line dataset,and the other was based on the production line dataset together with the literature *** these two strategies,the random forest model displayed the best prediction results,the accuracy of strategy I was 0.58,and the accuracy of strategy II was 0.90,wherein literature data effectively improved the CIT prediction ***,an optimized CIT model based on machine learning algorithms was *** proposed strategy of literature data-assisted production line data provides a new perspective for optimizing and predicting the performance of traditional structural materials.

关键词： data-driven design pipeline steel Charpy impact toughness machine learning

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data-driven Fault Detection for Vehicle Lateral Dynamics 36

Data-Driven Fault Detection for Vehicle Lateral Dynamics

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第36届中国控制会议

作者： WANG Yulei YUAN Jingxin CHEN Hong The State Key Laboratory of Automotive Simulation and Control Department of Control Science and EngineeringJilin University

ISBN: (纸本)9781538629185

In this paper,we investigate a data-driven fault detection（FD） problem and its application to vehicle lateral dynamics for the improvement of vehicle lateral safety,reliability and feasibility.A more practical situation in this work is the generalized（non-affine） models and real-time *** particular,a linear parameter-varying（LPV） model is established for vehicle lateral dynamics with varying longitudinal velocity,and the least square support vector machines（LS-SVM） aided datadriven approach is proposed to design the parity vector based residual generation and evaluation framework directly for fault *** proposed data-driven FD approach is illustrated via a benchmark simulation of vehicle model and environment from veDYNA.

关键词： Fault Detection Least Square Support Vector Machines Vehicle Lateral Dynamics data-driven design Parity Space

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Advances in machine learning-and artificial intelligence-assisted material design of steels

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International Journal of Minerals,Metallurgy and Materials 2023年第6期30卷 1003-1024页

作者： Guangfei Pan Feiyang Wang Chunlei Shang Honghui Wu Guilin Wu Junheng Gao Shuize Wang Zhijun Gao Xiaoye Zhou Xinping Mao Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China Yangjiang Branch Guangdong Laboratory for Materials Science and Technology(Yangjiang Advanced Alloys Laboratory)Yangjiang 529500China Guangdong Province Key Laboratory of Durability for Marine Civil Engineering School of Civil EngineeringShenzhen UniversityShenzhen 518060China

With the rapid development of artificial intelligence technology and increasing material data,machine learning-and artificial intelligence-assisted design of high-performance steel materials is becoming a mainstream paradigm in materials *** learning methods,based on an interdisciplinary discipline between computer science,statistics and material science,are good at discovering correlations between numerous data *** with the traditional physical modeling method in material science,the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel *** review starts with data preprocessing and the introduction of different machine learning models,including algorithm selection and model ***,some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition,structure,processing,and *** application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also ***,the applicability and limitations of machine learning in the material field are summarized,and future directions and prospects are discussed.

关键词： machine learning data-driven design new research paradigm high-performance steel

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Towards the automation of book typesetting

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Visual Informatics 2023年第2期7卷 1-12页

作者： Sérgio M.Rebelo Tiago Martins Diogo Ferreira Artur Rebelo University of Coimbra Centre for Informatics and Systems of the University of CoimbraDepartment of Informatics EngineeringCoimbraPortugal

This paper proposes a generative approach for the automatic typesetting of books in desktop *** presented system consists in a computer script that operates inside a widely used design software tool and implements a generative process based on several typographic rules,styles and principles which have been identified in the *** performance of the proposed system is tested through an experiment which included the evaluation of its outputs with *** results reveal the ability of the system to consistently create varied book designs from the same input content as well as visually coherent book designs with different contents while complying with fundamental typographic principles.

关键词： design tools data-driven design Generative design Graphic design Typography

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数据驱动设计模式的研究及应用

数据驱动设计模式的研究及应用

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作者：徐陋暨南大学

学位级别：硕士

当前程序设计有两种发展趋势：软件生产自动化(又名程序自动生成技术)和软件重用技术。这两个领域都有无数的专家将他们推陈出新，软件生产自动化领域有产生式编程、意图编程、特征驱动开发、模型驱动分析／设计(MDA／MDD)、UML2.0;而... 详细信息

当前程序设计有两种发展趋势：软件生产自动化(又名程序自动生成技术)和软件重用技术。这两个领域都有无数的专家将他们推陈出新，软件生产自动化领域有产生式编程、意图编程、特征驱动开发、模型驱动分析／设计(MDA／MDD)、UML2.0;而软件重用技术则有库函数，模板，面向对象、设计模式、构件、框架、构架等复用技术，其目的都是为了提高软件生产效率，达到软件大生产的工业化时代。本文在面向对象程序设计的基础上，结合程序设计发展趋势中软件生产自动化中的产生式编程、模型驱动分析／设计(MDA／MDD)，和软件重用中的设计模式的思想，提出一种数据驱动(data-driven)设计模式，它借助数据交换工具以数据为模型，在产生器作用下，自动生成功能代码所需要的目标类，可以降低程序和数据库的耦合度，再结合其它设计模式，可以从很大程度上实现软件重用，适合解决数据库应用系统中大量相似异构数据表操作可维护性低，可重用性低，开发周期过长的问题。在作者项目实例中将采用XML为数据交换工具应用数据驱动设计模式，并将其和原型(Prototype)设计模式结合使用，以达到最大软件重用。最后本文还讨论现存23种经典设计模式中和本模式相似的两个模式——适配器(Adapter)和桥接(Bridge)模式，并也将举出同一项目中两个小例子来说明这两个模式，用以讨论他们的相似和不同之处，分别指出他们各自的适用范围。

关键词：设计模式产生式编程 MDA/MDD data-driven design 软件重用 XML

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