Compositional optimization of glass forming alloys based on critical dimension by using artificial neural network

An artificial neural network （ANN） model was developed for simulating and predicting critical dimension dc of glass forming alloys. A group of Zr-Al-Ni-Cu and Cu-Zr-Ti-Ni bulk metallic glasses were designed based on the dc and their de values were predicted by the ANN model. Zr-Al-Ni-Cu and Cu-Zr-Ti-Ni bulk metallic glasses were prepared by injecting into copper mold. The amorphous structures and the determination of the dc of as-cast alloys were ascertained using X-ray diffraction. The results show that the predicted de values of glass forming alloys are in agreement with the corresponding experimental values. Thus the developed ANN model is reliable and adequate for designing the composition and predicting the de of glass forming alloy.

Study of microstructure evolution of magnesium alloy cylindrical part with longitudinal inner ribs during hot flow forming by coupling ANN-modified CA and FEA

Hot flow forming(HFF)is a promising forming technology to manufacture thin-walled cylindrical part with longitudinal inner ribs(CPLIRs)made of magnesium(Mg)alloys,which has wide applications in the aerospace field.However,due to the thermo-mechanical coupling effect and the existence of stiffened structure,complex microstructure evolution and uneven microstructure occur easily at the cylindrical wall(CW)and inner rib(IR)of Mg alloy thin-walled CPLIRs during the HFF.In this paper,a modified cellular automaton(CA)model of Mg alloy considering the effects of deformation conditions on material parameters was developed using the artificial neural network(ANN)method.It is found that the ANN-modified CA model exhibits better predictability for the microstructure of hot deformation than the conventional CA model.Furthermore,the microstructure evolution of ZK61 alloy CPLIRs during the HFF was analyzed by coupling the modified CA model and finite element analysis(FEA).The results show that compared with the microstructure at the same layer of the IR,more refined grains and less sufficient DRX resulted from larger strain and strain rate occur at that of the CW;various differences of strain and strain rate in the wall-thickness exist between the CW and IR,which leads to the inhomogeneity of microstructure rising firstly and declining from the inside layer to outside layer;the obtained Hall-Petch relationship between the measured microhardness and predicted grain sizes at the CW and the IR indicates the reliability of the coupled FEA-CA simulation results.

High current pulse forming network switched by static induction thyristor

A high-current pulse forming network (PFN) has been developed for applications to artificial solar-wind generation. It is switched by staticinduction thyristor (SIThy) and is capable of generating pulsed current of ~9.7 kA for a time duration of ~1 ms. The SIThy switch module ismade that it can be controlled by an optical signal and it can be operated at elevated electrical potential. The experiments reported in this paperused two switch modules connected in series for maximum operating voltage of 3.5 kV. The experimental results have demonstrated a pulsedhigh-current generator switched by semiconductor devices, as well as the control and operation of SIThy for pulsed power application.

Selection of Optimal Beam Forming Algorithm for Mobile Ad Hoc Networks

Ad hoc networks have drawn considerable attentions of researchers for the last few years. Various applications of ad hoc networks have been reported in the literatures including disaster management, battle field, environmental management, healthcare, and smart grid. Ad hoc networks have some limitations namely short operating life, unreliability, scalability, delay, high interference, and scarce resources. In order to overcome these limitations, numerous researches have been carried out. Smart antenna integration is one of them. It has been shown in the literatures that smart antenna can improve network’s capacity, increase network lifetime, reduce delay, and improve scalability by directing antenna radiation pattern in a desired direction. But, producing a desired antenna radiation pattern is not a simple task for resource constraint ad hoc networks. A careful selection of beam forming algorithm is required. In this paper we show that smart antenna system, consisting of circular microstrip antennas and arranged in a linear arrangement, is the most suitable choice for ad hoc network. We compare a number of smart antenna algorithms in this paper under different noisy conditions. We show that the Least Square Constant Modulus (LSCM) and Least Constant Modulus (LCM) algorithms outperform other algorithms in terms of directivity and minimized side lobes.

Springback prediction for incremental sheet forming based on FEM-PSONN technology

In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.

Springback and tensile strength of 2A97 aluminum alloy during age forming

The analysis of variance(ANOVA), multiple quadratic regression and radial basis function artificial neural network(RBFANN) methods were used to study the springback and tensile strength in age forming of 2A97 aluminum alloy based on orthogonal array. The ANOVA analysis indicates that the springback reaches the minimum value when age forming is performed at 210 °C for 20 h using a single-curvature die with a radius of 400 mm, and the tensile strength reaches the maximum value when age forming is performed at 180 °C for 15 h using a single-curvature die with a radius of 1000 mm. The orders of the importance for the three factors of pre-deformation radius, aging temperature and aging time on the springback and tensile strength were determined. By analyzing the predicted results of the multiple quadratic regression and RBFANN methods, the prediction accuracy of the RBFANN model is higher than that of the regression model.

PHYSICAL SIMULATION AND PROPERTY PREDICTION IN HEAT FORMING PROCESS OF 1Cr18Ni9Ti

Regarding heat forming process of 1Cr18Ni9Ti as typical forming process, this paper presents the study of the effect of various parameters on flow stress, grain size and hardness of formed specimen by means of Gleeble-1500 Thermo-simulation machine and metalloscope. On the basis of technical experi- ment this paper, data are proceeded by applying multilayer feedforward back-propagation neural network, a prediction model of technological parameters together with microstructure and property in the heat forming process is established, thus forging property prediction in the heat forming process is realized.

Optimization of press bend forming path of aircraft integral panel

In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response surface,and the genetic algorithm.First,a multi-step press bend forming FEM equivalent model was established,with which the FEM experiments designed with Taguchi method were performed.Then,the BP neural network response surface was developed with the sample data from the FEM experiments.Furthermore,genetic algorithm was applied with the neural network response surface as the objective function. Finally,verification was carried out on a simple curvature grid-type stiffened panel.The forming error of the panel formed with the optimal path is only 0.098 39 and the calculating efficiency has been improved by 77%.Therefore,this novel optimization method is quite efficient and indispensable for the press bend forming path designing.

Projected change in precipitation forms in the Chinese Tianshan Mountains based on the Back Propagation Neural Network Model

In the context of global warming,precipitation forms are likely to transform from snowfall to rainfall with a more pronounced trend.The change in precipitation forms will inevitably affect the processes of regional runoff generation and confluence as well as the annual distribution of runoff.Most researchers used precipitation data from the CMIP5 model directly to study future precipitation trends without distinguishing between snowfall and rainfall.CMIP5 models have been proven to have better performance in simulating temperature but poorer performance in simulating precipitation.To overcome the above limitations,this paper used a Back Propagation Neural Network(BNN)to predict the rainfall-to-precipitation ratio(RPR)in months experiencing freezing-thawing transitions(FTTs).We utilized the meteorological(air pressure,air temperature,evaporation,relative humidity,wind speed,sunshine hours,surface temperature),topographic(altitude,slope,aspect)and geographic(longitude,latitude)data from 28 meteorological stations in the Chinese Tianshan Mountains region(CTMR)from 1961 to 2018 to calculate the RPR and constructed an index system of impact factors.Based on the BNN,decision-making trial and evaluation laboratory method(BP-DEMATEL),the key factors driving the transformation of the RPR in the CTMR were identified.We found that temperature was the only key factor affecting the transformation of the RPR in the BP-DEMATEL model.Considering the relationship between temperature and the RPR,the future temperature under different representative concentration pathways(RCPs)(RCP2.6/RCP4.5/RCP8.5)provided by 21 CMIP5 models and the meteorological factors from meteorological stations were input into the BNN model to acquire the future RPR from 2011 to 2100.The results showed that under the three scenarios,the RPR in the number of months experiencing FTTs during 2011-2100 will be higher than that in the historical period(1981-2010)in the CTMR.Furthermore,in terms of spatial variation,the RPR values on the south slope will be larger than those on the north slope under the three emission scenarios.Moreover,the RPR values exhibited different variation characteristics under different emission scenarios.Under the low-emission scenario(RCP2.6),as time passed,the RPR values changed slightly at more stations.Under the mediumemission scenario(RCP4.5),the RPR increased in the whole CTMR and stabilized on the north slope by the end of this century.Under the high-emission scenario(RCP8.5),the RPR values increased significantly through the 21 st century in the whole CTMR.This study may help to provide a scientific management basis for agricultural production and hydrology.

The dynamic relaxation form finding method aided with advanced recurrent neural network

How to establish a self‐equilibrium configuration is vital for further kinematics and dynamics analyses of tensegrity mechanism.In this study,for investigating tensegrity form‐finding problems,a concise and efficient dynamic relaxation‐noise tolerant zeroing neural network(DR‐NTZNN)form‐finding algorithm is established through analysing the physical properties of tensegrity structures.In addition,the non‐linear constrained opti-misation problem which transformed from the form‐finding problem is solved by a sequential quadratic programming algorithm.Moreover,the noise may produce in the form‐finding process that includes the round‐off errors which are brought by the approximate matrix and restart point calculating course,disturbance caused by external force and manufacturing error when constructing a tensegrity structure.Hence,for the purpose of suppressing the noise,a noise tolerant zeroing neural network is presented to solve the search direction,which can endow the anti‐noise capability to the form‐finding model and enhance the calculation capability.Besides,the dynamic relaxation method is contributed to seek the nodal coordinates rapidly when the search direction is acquired.The numerical results show the form‐finding model has a huge capability for high‐dimensional free form cable‐strut mechanisms with complicated topology.Eventually,comparing with other existing form‐finding methods,the contrast simulations reveal the excellent anti‐noise performance and calculation capacity of DR‐NTZNN form‐finding algorithm.

ADAPTIVE PINNING SYNCHRONIZATION OF COUPLED NEURAL NETWORKS WITH MIXED DELAYS AND VECTOR-FORM STOCHASTIC PERTURBATIONS

In this article, we consider the global chaotic synchronization of general cou- pled neural networks, in which subsystems have both discrete and distributed delays. Stochastic perturbations between subsystems are also considered. On the basis of two sim- ple adaptive pinning feedback control schemes, Lyapunov functional method, and stochas- tic analysis approach, several sufficient conditions are developed to guarantee global syn- chronization of the coupled neural networks with two kinds of delay couplings, even if only partial states of the nodes are coupled. The outer-coupling matrices may be symmetric or asymmetric. Unlike existing results that an isolate node is introduced as the pinning target, we pin to help the network realizing synchronization without introducing any iso- late node when the network is not synchronized. As a by product, sufficient conditions under which the network realizes synchronization without control are derived. Numerical simulations confirm the effectiveness of the obtained results.

Structural form selection of the high-rise buildingwith the improved BP neural network

As civil engineering technology development,the structural form selection is more and more critical in design of high-rise buildings.However,structural form selection involves expertise knowledge and changes with the environment which makes the task arduous.An approach utilizing improved back propagation(BP)neural network optimized by the Levenberg-Marquardt(L-M)algorithm is proposed to extract the main controlling factors of structural form selection.Then,an intelligent expert system with artificial neural network is constructed to design high-rise buildings structure effectively.The experiment tests the model in 15 well-known architecture samples and get the prediction accuracy of 93.33%.The results show that the method is feasible and can help designers select the appropriate structural form.

短视频过度使用及注意力不集中与初中生拒学行为的关系

目的:探讨初中生短视频过度使用、注意力不集中与拒学行为的显要表现及内在关联。方法:选取1 106名在校初中生进行调查,采用短视频APP过度使用量表、注意力缺陷多动障碍评定量表的注意力不集中维度、拒绝上学行为问卷评估短视频过度使用程度、注意力不集中、拒学行为。采用R语言网络分析工具包评估单个网络的核心节点和共存网络的桥梁节点,比较不同性别和年级的网络结构差异。结果:短视频过度使用、注意力不集中、拒学行为网络的核心节点分别为“使用短视频会增强与他人的联系”(预期影响值=1.03)、“容易分心”(预期影响值=1.99)、“学校疏离”(预期影响值=0.83)。共存网络的桥梁节点为“违抗行为”(桥预期影响值=0.69)、“难以从事持续性的脑力活动”(桥预期影响值=0.47)。网络结构差异分析发现,初一、初二、初三学生共存网络的核心节点分别为“因使用短视频而停止做其他事”“违抗行为”“学习能力”。结论:初中生短视频过度使用、注意力不集中、拒学行为的显要表现分别为使用短视频会增强与他人的联系、容易分心、学校疏离;三者共存时最重要的影响因素为违抗行为、难以从事持续性的脑力活动。

二次B—样条三角面片的Polarform算法

近年来三角B样条曲面（B-patch）作为非张量积曲面在几何造型的拟会和设计中得到了广泛的应用。它以类deCasteljau算法和拓朴三角形形式而使该曲面既有样条函数的整体光滑性，又适于描述非拓扑四边形的情形，特别是适用于有限元网格划分。本文讨论了多元B样条和多元对称仿射变换及其与B—patch的关系和性质，给出在三角划分域上的二次三角B样条曲面的Polarform算法和定义。可方便地应用于曲面设计与拟合的诸多场合。

基于Polar Form算法的三角B样条曲面和三角Bezer曲面间的转化

本文探讨了近来流行的PolarForm方法，该方法在几何造型、CAD、计算机图形学等方面有重要的应用，首先给出了一般三角域的二次B样条曲面片的PolarForm算法，然后讨论了三角域上的B─面片和Bezier面片间的相互转换算法。

Seismic reliability analysis of urban water distribution network

An approach to analyze the seismic reliability of water distribution networks by combining a hydraulic analysis with a first-order reliability method （FORM）, is proposed in this paper. The hydraulic analysis method for normal conditions is modified to accommodate the special conditions necessary to perform a seismic hydraulic analysis. In order to calculate the leakage area and leaking flow of the pipelines in the hydraulic analysis method, a new leakage model established from the seismic response analysis of buried pipelines is presented. To validate the proposed approach, a network with 17 nodes and 24 pipelines is investigated in detail. The approach is also applied to an actual project consisting of 463 nodes and 767 pipelines. The results show that the proposed approach achieves satisfactory results in analyzing the seismic reliability of large-scale water distribution networks.

Deep Learning in Sheet Metal Bending With a Novel Theory-Guided Deep Neural Network

Sheet metal forming technologies have been intensively studied for decades to meet the increasing demand for lightweight metal components.To surmount the springback occurring in sheet metal forming processes,numerous studies have been performed to develop compensation methods.However,for most existing methods,the development cycle is still considerably time-consumptive and demands high computational or capital cost.In this paper,a novel theory-guided regularization method for training of deep neural networks(DNNs),implanted in a learning system,is introduced to learn the intrinsic relationship between the workpiece shape after springback and the required process parameter,e.g.,loading stroke,in sheet metal bending processes.By directly bridging the workpiece shape to the process parameter,issues concerning springback in the process design would be circumvented.The novel regularization method utilizes the well-recognized theories in material mechanics,Swift’s law,by penalizing divergence from this law throughout the network training process.The regularization is implemented by a multi-task learning network architecture,with the learning of extra tasks regularized during training.The stress-strain curve describing the material properties and the prior knowledge used to guide learning are stored in the database and the knowledge base,respectively.One can obtain the predicted loading stroke for a new workpiece shape by importing the target geometry through the user interface.In this research,the neural models were found to outperform a traditional machine learning model,support vector regression model,in experiments with different amount of training data.Through a series of studies with varying conditions of training data structure and amount,workpiece material and applied bending processes,the theory-guided DNN has been shown to achieve superior generalization and learning consistency than the data-driven DNNs,especially when only scarce and scattered experiment data are available for training which is often the case in practice.The theory-guided DNN could also be applicable to other sheet metal forming processes.It provides an alternative method for compensating springback with significantly shorter development cycle and less capital cost and computational requirement than traditional compensation methods in sheet metal forming industry.

基于SQL*Forms的图书馆管理系统建设

论述了构建图书馆局域网和利用SQL Forms开发图书馆管理系统的关键技术 。

On Study of Solutions of Kac-van Moerbeke Lattice and Self-dual Network Equations

The closed form of solutions of Kac-van Moerbeke lattice and self-dual network equations are considered by proposing transformations based on Riccati equation, using symbolic computation. In contrast to the numerical computation of travelling wave solutions for differential difference equations, our method obtains exact solutions which have physical relevance.