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Understanding microstructure-property relationships of HPDC Al-Si alloy based on machine learning and crystal plasticity simulation

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Advances in Manufacturing 2024年第3期12卷 497-511页

作者： qiang-qiang Zhai Zhao liu Ping Zhu State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai200240People’s Republic of China National Engineering Research Center of Automotive Power and Intelligent Control Shanghai Jiao Tong UniversityShanghai200240People’s Republic of China School of Design Shanghai Jiao Tong UniversityShanghai200240People’s Republic of China

Al-Si alloys manufactured via high-pressure die casting(HPDC)are suitable for a wide range of ***,the heterogeneous microstructure and unpredictable pore distribution of Al-Si high-pressure die castings result in significant variations in the mechanical properties,thus leading to a complicated microstructure-property relationship that is difficult to ***,a computational framework incorporating machine learning and crystal plasticity method is *** framework aims to provide a systematic and comprehensive understanding of this relationship and enable the rapid prediction of macroscopic mechanical properties based on the ***,we select eight variables that can effectively characterize the microstructural features and then obtain their statistical ***,based on 160 samples obtained via the Latin hypercube sampling method,representative volume elements are constructed,and the crystal plasticity fast Fourier transformation method is executed to obtain the macroscopic mechanical ***,the yield strength,elastic modulus,strength coefficient,and strain-hardening exponent are used to characterize the stress-strain curve,and Gaussian process regression models and microstructural variables are ***,sensitivity and univariate analyses based on these machine-learning models are performed to obtain insights into the microstructure-property relationships of the HPDC Al-Si *** results show that the Gaussian process regression models exhibit high accuracy(R2 greater than 0.84),thus confirming the viability of the proposed *** results of sensitivity analysis indicate that the pore size exerts the most significant effect on the mechanical ***,the proposed framework can not only be transferred to other alloys but also be employed for material design.

关键词： High-pressure die casting(HPDC) Machine learning Crystal plasticity Aluminum alloys

Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction

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Petroleum Science 2024年第4期21卷 2329-2344页

作者： jiu-qiang Yang Nian-Tian Lin Kai Zhang Yan Cui Chao Fu Dong Zhang College of Earth Sciences and Engineering Shandong University of Science and Technology Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and Technology Transportation Institute of Inner Mongolia University Key Laboratory of Gas Hydrate Qingdao Institute of Marine Geology Ministry of Natural Resources

Conventional machine learning(CML) methods have been successfully applied for gas reservoir prediction. Their prediction accuracy largely depends on the quality of the sample data; therefore, feature optimization of the input samples is particularly important. Commonly used feature optimization methods increase the interpretability of gas reservoirs; however, their steps are cumbersome, and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently. In contrast to CML methods, deep learning(DL) methods can directly extract the important features of targets from raw data. Therefore, this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM). This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes, considerably simplifying the feature optimization. A CNN was used for feature optimization to highlight sensitive gas reservoir information. APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results. The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction, giving full play to the advantages of DL and CML methods. The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model. In the feature optimization process of multicomponent seismic attribute data, CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods. In the prediction process, the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy. The constructed CNN-APSO-LSSVM mode

关键词： Multicomponent seismic data Deep learning Adaptive particle swarm optimization Convolutional neural network Least squares support vector machine Feature optimization Gas-bearing distribution prediction

Non-contact and full-field online monitoring of curing temperature during the in-situ heating process based on deep learning

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Advances in Manufacturing 2024年第1期12卷 167-176页

作者： qiang-qiang liu Shu-Ting liu Ying-Guang Li Xu liu Xiao-Zhong Hao College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and AstronauticsNanjing210016People’s Republic of China School of Mechanical and Power Engineering Nanjing Tech UniversityNanjing211816People’s Republic of China

Online monitoring of the curing temperature field is essential to improving the quality and efficiency of the manufacturing process of composite *** embedded sensor-based technologies have difficulty monitoring the full temperature field or have to introduce heterogeneous items that could have an undesired impact on the *** this paper,a non-contact,full-field monitoring method based on deep learning that predicts the internal temperature field of composite parts in real time using surface temperature measurements of auxiliary materials is *** the proposed method,an average temperature monitoring accuracy of 97%is achieved in various heating *** addition,this method also demonstrates satisfying feasibility when a stronger thermal barrier covers the *** method was experimentally validated during the self-resistance electric heating process,in which the monitoring accuracy reached 93.1%.This method can potentially be applied to automated manufacturing and process control in the composites industry.

关键词： Online monitoring Curing temperature field Deep learning(DL) In-situ heating

Spontaneous Infection and Periodic Evolving of Domain in a Diffusive SIS Epidemic Model

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Acta Mathematicae Applicatae Sinica 2024年第1期40卷 164-191页

作者： qiang Wen Guo-qiang Ren Bin liu School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan430074China

In this paper, we consider a susceptible-infective-susceptible(SIS) reaction-diffusion epidemic model with spontaneous infection and logistic source in a periodically evolving domain. Using the iterative technique,the uniform boundedness of solution is established. In addition, the spatial-temporal risk index R0(ρ) depending on the domain evolution rate ρ(t) as well as its analytical properties are discussed. The monotonicity of R0(ρ)with respect to the diffusion coefficients of the infected dI, the spontaneous infection rate η(ρ(t)y) and interval length L is investigated under appropriate conditions. Further, the existence and asymptotic behavior of periodic endemic equilibria are explored by upper and lower solution method. Finally, some numerical simulations are presented to illustrate our analytical results. Our results provide valuable information for disease control and prevention.

关键词： diffusive SIS model spontaneous infection periodically evolving domain periodic endemic equilibrium

Remote sensing image encryption algorithm utilizing 2D Logistic memristive hyperchaotic map and SHA-512

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Science China(Technological Sciences) 2024年第5期67卷 1553-1566页

作者： LAI qiang liu Yuan YANG Liang School of Electrical and Automation Engineering East China Jiaotong UniversityNanchang330013China

The two-dimensional Logistic memristive hyperchaotic map(2D-LMHM)and the secure hash SHA-512 are the foundations of the unique remote sensing image encryption algorithm(RS-IEA)suggested in this *** proposed map is formed from the improved Logistic map and the memristor,which has wide phase space and hyperchaotic range and is exceptionally excellent to be utilized in specific *** proposed image algorithm uses the permutation-assignment-diffusion *** generates two position matrices in a progressive manner to achieve an efficient random exchange of pixel positions,assignment is carried through on the image pixels of the permutated image to entirely remove the original image information,strengthening the relationship between permutation and diffusion,and loop diffusion in two different directions can use subtle changes of pixels to affect the whole *** random key and plain-image SHA-512 hash values are used to produce an additional key,which is then utilized to figure out the permutation parameters and the initial value of a chaotic *** experimental results with the average NPCR=99.6094%(NPCR:number of pixels change rate),average UACI=33.4638%(UACI:unified average changing intensity),100%pass rate of the targets in the test set,the average correlation coefficient is 0.00075,and the local information entropy is 7.9025,which shows that the algorithm is able to defend against a variety of illegal attacks and provide more trustworthy protection than some of the existing state-of-the-art algorithms.

关键词： chaos memristive hyperchaotic map remote sensing SHA-512 image encryption RS-IEA

Automatic modulation recognition of radio fuzes using a DR2D-based adaptive denoising method and textural feature extraction

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Defence Technology（防务技术） 2024年第4期34卷 328-338页

作者： Yangtian liu Xiaopeng Yan qiang liu Tai An Jian Dai Science and Technology on Electromechanical Dynamic Control Laboratory School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China China Research and Development Academy of Machinery Equipment Beijing 100089China

The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference ***,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio *** solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is ***,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)***,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for *** DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical *** trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.

关键词： Automatic modulation recognition Adaptive denoising Data rearrangement and the 2D FFT(DR2D) Radio fuze

Statistical analysis and interpretation on lightning impulse breakdown voltages of ester liquids under varying field uniformity

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High Voltage 2024年第1期9卷 22-34页

作者： Haichuan Yu qiang liu Zhongdong Wang Department of Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Engineering University of ExeterExeterUK

Ester liquids(ELs),as the alternative transformer liquids to the conventional mineral oils(MOs),have been increasingly used in power transformers with high voltage *** various properties,the lightning impulse breakdown voltages(LIBDVs)have been studied elaborately in the past two decades under different electric field *** electric field uniformities do have an effect on the breakdown characteristics,for example,breakdown mechanisms,polarity effects,and differences of LIBDV between ELs and MOs,which is quantitatively investigated based on the LIBDVs collected from the *** breakdown mechanism transition phenomenon between streamer initiation dominated breakdown and streamer propagation dominated breakdown has been reviewed ***,the transition field factor boundaries to observe the polarity effect on LIBDVs are taken at around 20 for ELs and 10 for ***,the transition field factor boundaries to observe the LIBDV distinctions between ELs and MOs are about 109 and 14 under positive and negative lightning impulse,*** systematic analysis and interpretation on the transition field factor boundaries among different phenomena can provide insights on insulation design of ELs filled power transformers.

关键词： field lightning breakdown

Multicolored Bipartite Ramsey Numbers of Large Cycles

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Acta Mathematicae Applicatae Sinica 2024年第2期40卷 347-357页

作者： Shao-qiang liu Yue-jian PENG College of Mathematics and Statistics Hanshan Normal UniversityChaozhou 521041China School of Mathematics Hunan UniversityChangsha 410082China

For an integer r≥2 and bipartite graphs Hi,where 1≤i≤r,the bipartite Ramsey number br(H1,H2,…,Hr)is the minimum integer N such that any r-edge coloring of the complete bipartite graph KN;N contains a monochromatic subgraph isomorphic to Hi in color i for some 1≤i≤*** show that if r≥3;α1,α2>0,αj+2≥[(j+2)!-1]Σi=1^(j+1)α1 for j=1,2…r-2,then br(C2[α1n],C2[α2n],…,C2[αrn]=(Σ=j=1^(r)a j+o(1))n.

关键词： ramsey number bipartite ramsey number regularity lemma

Efficiently simulating the work distribution of multiple identical bosons with boson sampling

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Frontiers of physics 2024年第3期19卷 197-206页

作者： Wen-qiang liu Zhang-qi Yin Center for Quantum Technology Research and Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements(MOE) School of PhysicsBeijing Institute of TechnologyBeijing 100081China

Boson sampling has been theoretically proposed and experimentally demonstrated to show quantum computational ***,it still lacks the deep understanding of the practical applications of boson *** we propose that boson sampling can be used to efficiently simulate the work distribution of multiple identical *** link the work distribution to boson sampling and numerically calculate the transition amplitude matrix between the single-boson eigenstates in a one-dimensional quantum piston system,and then map the matrix to a linear optical network of boson *** work distribution can be efficiently simulated by the output probabilities of boson sampling using the method of the grouped probability *** scheme requires at most a polynomial number of the samples and the optical *** work opens up a new path towards the calculation of complex quantum work distribution using only photons and linear optics.

关键词： quantum simulation quantum work distribution boson sampling linear optic