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Arrhenius constitutive equation and artificial neural network model of flow stress in hot deformation of offshore steel with high strength and toughness

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Materials and Processing Report 2023年第1期38卷

作者： Li Fangpo Li Ning Ren Xiaojian Qiao Song Lu Caihong Wang Jianjun Xu Yang Wang Bin a CNPC Tubular Goods Research Institute Xi’an China b Institute for Hygiene of Ordance Industry Xi’an China c State Key Laboratory of Rolling and Automation Northeastern University Shenyang China d Shandong Iron and Steel Group Rizhao Co. Ltd Rizhao China

ABSTRACTIn this study, the thermal deformation behaviour of a high strength offshore steel at different temperatures and rates was investigated through thermal compression *** Arrhenius constitutive model and a back propagation artificial neural network (BP-ANN) were established to address more complex deformation characteristics. The performances of both models were was evaluated using standard statistical parameters such as the correlation coefficient (R) and average absolute relative error (AARE). The results showed that both models can accurately predict the rheological stresses generated during *** BP-ANN outperforms the Arrhenius equation model with correlation coefficients of fit greater than 99.9% and less than 0.8% relative error. At a strain rate of 0.01 s−1and 10 s−1, the accuracy of the ANN decreases slightly due to the fact that it exceeds the strain rate range of the training set, as compared to the Arrhenius constitutive equations as these are more accurately predicted.

关键词： High strength offshore steel compression experiment Arrhenius constitutive equation back-propagation artificial neural network flow stress

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Comprehensive prediction method for die-roll height of fine-blanking components

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第9-12期98卷 2819-2829页

作者： Zhuang, Xincun Zhang, Wen Wu, Yanlin Zhao, Zhen Shanghai Jiao Tong Univ Inst Forming Technol & Equipment Sch Mat Sci & Engn Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Natl Engn Res Ctr Die & Mold CAD Shanghai 200030 Peoples R China

This study proposes an economic and effective approach for predicting the die-roll height of fine-blanking components by fully considering the effect of three types of parameters: geometric features of components, process parameters of fine-blanking, and material parameters of fine-blanked sheets. Two prediction submodels that involved geometric features and process parameters individually were established based on a back-propagation artificial neural network (BP ANN), merged into one model, and finally generalised to different materials by correction according to experimental results. During modelling, simulations rather than experiments were used to obtain data for supporting BP ANN considering the demand for a single controlling variable and for saving economic and time costs. Analyses showed that geometric features had a more distinct influence on die-roll height;furthermore, the effect of process parameters is complicated and can be described more suitably by BP ANN. The prediction results of the combined model were favourable compared with the experimental results. Furthermore, the experimental curve used for correction during generalisation of the model showed that a material with higher yield strength generally had smaller die-roll.

关键词： Die-roll Fine-blanking Geometric feature back-propagation artificial neural network Correction curve

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artificial neural network-Based Three-dimensiona Continuous Response Relationship Construction of 3Cr20Ni10W2 Heat-Resisting Alloy and Its Application in Finite Element Simulation

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HIGH TEMPERATURE MATERIALS AND PROCESSES 2018年第5期37卷 411-424页

作者： Li, Le Wang, Li-yong Beijing Informat Sci & Technol Univ Minist Educ Key Lab Modern Measurement & Control Technol Beijing 100192 Peoples R China Beijing Informat Sci & Technol Univ Collaborat Innovat Ctr Elect Vehicles Beijing Beijing 100192 Peoples R China

The application of accurate constitutive relationship in finite element simulation would significantly contribute to accurate simulation results, which plays a critical role in process design and optimization. In this investigation, the true stress-strain data of 3Cr20Ni10W2 heat-resisting alloy were obtained from a series of isothermal compression tests conducted in a wide temperature range of 1203-1403K and strain rate range of 0.01-10s(-1) on a Gleeble 1500 testing machine. Then the constitutive relationship was modeled by an optimally constructed and well-trained back-propagation artificial neural network (BP-ANN). The evaluation of the BP-ANN model revealed that it has admirable performance in characterizing and predicting the flow behaviors of 3Cr20Ni10W2 heat-resisting alloy. Meanwhile, a comparison between improved Arrhenius-type constitutive equation and BP-ANN model shows that the latter has higher accuracy. Consequently, the developed BP-ANN model was used to predict abundant stress-strain data beyond the limited experimental conditions and construct the three-dimensional continuous response relationship for temperature, strain rate, strain, and stress. Finally, the three-dimensional continuous response relationship was applied to the numerical simulation of isothermal compression tests. The results show that such constitutive relationship can significantly promote the accuracy improvement of numerical simulation for hot forming processes.

关键词： constitutive relationship heat-resisting alloy back-propagation artificial neural network accuracy improvement numerical simulation

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Performance evaluation of fuzzy-logic and BP-ANN methods for WEDM of aeronautics super alloy

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METHODSX 2018年 5卷 890-908页

作者： Nain, Somvir Singh Sihag, Parveen Luthra, Sunil CMR Coll Engn & Technol Dept Mech Engn Ctr Excellence Mat & Mfg Hyderabad 501401 Telangana India Natl Inst Technol Dept Civil Engn Kurukshetra 136119 Haryana India State Engn Coll Dept Mech Engn Nilokheri Haryana India

The main purpose of this research is to check the relative importance of methods fuzzy-logic and back- propagation neural network to evaluate the performance of wire electric discharge machine (WEDM) of aeronautics super alloy. It has been confirmed that BP-ANN method reveals significant result over the fuzzy logic method for the evaluation of surface roughness and waviness of the WEDM of aeronautic super alloy. On the basis of Taguchi analysis, it has been established that the variable pulse-on, interaction amid the pulse-on and pulse-off time, wire tension and spark-gap voltage have a superlative influence on the surface roughness. The waviness is influenced prominently by pulse-on time, pulse-off time and spark-gap voltage. The thickness of recast layer is minimized up to 9.434 mu m. (C) 2018 Published by Elsevier B.V.

关键词： Wire electric discharge machining Fuzzy-logic back-propagation artificial neural network SEM Taguchi

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Simulation of Micro-grid Energy Storage Inverter with neural network Control 29

Simulation of Micro-grid Energy Storage Inverter with Neural...

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第29届中国控制与决策会议

作者： Huaqing Zeng Changliang Liu Automation Department North China Electric Power University

ISBN: (纸本)9781509046584

The study for inverter control was researched under the simulation system of single deviation-grid micro-grid energy storage inverter, the inverter output voltage and current waveforms under various conditions were obtained. The inverse model of the micro-grid energy storage inverter was identified based on the algorithm of back-propagation artificial neural network, and then the neural network internal model control scheme based on the design of inverse system was adopted to improve the power quality of the inverter output. The control scheme of the micro-grid energy storage inverter was realized by the Matlab/simulink platform, a simulation study was carried out. Results show that the neural network internal model control scheme based on the design of the inverse system has a better effect on controlling inverter compared to the conventional PI controller, and can be applied in the control system design of the micro-grid energy storage inverter.

关键词： Micro-grid energy storage inverter Output voltage and current Inverse model back-propagation artificial neural network

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A novel non-reference image quality assessment algorithm 2

A novel non-reference image quality assessment algorithm

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2017 IEEE 2nd Information Technology, networking, Electronic and Automation Control Conference (ITNEC 2017)

作者： Yong Ma Yingyun Yang Yao Lyu Jiawei He Information Engineering School Communication University of China

ISBN: (纸本)9781509064151;9781509064144

Due to the poor performance of many nonreference evaluation algorithms for the TID2013 image library, it is a great challenge to evaluate non-reference image quality for such library. In this paper, a novel non-reference image quality assessment(NR-IQA) algorithm based on wavelet energy, structure and texture features and color signature is proposed. The wavelet energy is calculated by combining visual saliency map and four level wavelet transform. The textural and structural map are obtained by Rudin-Osher-Fatemi algorithm while the features of these are extracted by local binary pattern(LBP) method. Two types of characteristic parameters of distorted image are analyzed, among which one is acquired in gray-scale image and the other is obtained in two color channel(Cb and Cr). Finally, back propagation artificial neural network and Radial Basis Function neural network are used to learn the mapping between feature space and subjective opinion scores. Experimental results on two benchmark image quality databases show that the proposed method has highly competitive performance in the state-of-the-art NR-IQA theories, especially in TID2013 IQA database.

关键词： Non-reference image quality assessment wavelet energy texture features structure features back-propagation artificial neural network radial basis function neural network

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Rapid Soil Classification with Laser Induced Breakdown Spectroscopy

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SPECTROSCOPY AND SPECTRAL ANALYSIS 2017年第1期37卷 241-246页

作者： De-shuo, Meng Nan-jing, Zhao Ming-jun, Ma Yan-hong, Gu Yang, Yu Li, Fang Yuan-yuan, Wang Yao, Jia Wen-qing, Liu Jian-guo, Liu Chinese Acad Sci Anhui Inst Opt & Fine Mech Hefei 230031 Peoples R China

Soil classification is an important research content in soil science field. It is the basis of soil survey and resource evaluation which is important to agricultural production. There are many soil classification standards all over the world. China has two kinds of classifications including soil genetic classification and soil system classification. There are great differences between different types of soil elements, so it is feasible for soil classification to use laser induced breakdown spectroscopy. Laser induced breakdown spectroscopy (LIBS) is a new element analysis technology which uses a laser pulse with high energy density to ablate samples. LIBS has been used in many fields including environmental protection and industrial production control. It can directly reflect the difference of element content in different soils. The experimental setup including an Nd: YAG laser, a spectrometer, a computer and a rotating platform. In the experiment 7 kinds of soil (red soil, brick red soil, lateritic red soil, paddy soil, cinnamon, alluvial soil and alpine meadow soil) including 25 samples were used. All soil samples were grinded and sieved before the experiment. Under the same experimental condition, the temperatures of the plasma created by the laser pulses on the surface of the different soil samples have great differences. The lateritic red soil had the highest temperature, and the alpine meadow soil had the lowest. But it was not enough to form the basis for classification. Therefore six constant elements including Si, Fe, Al, Mg, Ca and Ti were selected and their spectral line intensity were treated as classification index. Principal component analysis (PCA) was used to simplify the classification process. The PCA method could simplify the 6 indexes to few independent indexes which could also reflect the spectral information of the 6 elements. The original spectral data was processed by Matlab. The process consisted of spectral background removal, characteristi

关键词： Laser induced breakdown spectroscopy Soil classification Principal component analysis back-propagation artificial neural network

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Vis/NIR Chemical Imaging Technique for Predicting Sodium Humate Contents in Aquaculture Environment

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WATER AIR AND SOIL POLLUTION 2017年第5期228卷 177-177页

作者： Qu, Jia-Huan Sun, Da-Wen Pu, Hongbin South China Univ Technol Sch Food Sci & Engn Guangzhou 510641 Guangdong Peoples R China South China Univ Technol Guangzhou Higher Educ Mega Ctr Acad Contemporary Food Engn Guangzhou 510006 Guangdong Peoples R China Guangzhou Higher Educ Mega Ctr Engn & Technol Res Ctr Guangdong Prov Intelligent Guangzhou 510006 Guangdong Peoples R China Natl Univ Ireland Univ Coll Dublin Agr & Food Sci Ctr FRCFT Dublin 4 Ireland

This study aimed to develop a visible and near-infrared (Vis/NIR) chemical imaging (400-1000 nm) technique to provide rapid prediction of the contents of sodium humate dissolved in aquaculture environment. Gray reference image with 5% reflectance value was first used to correct the obtained raw images in order to promote the reflectance values as compared to that with 99% reflectance for further spectral analysis. Successive projection algorithm (SPA) was introduced to extract four optimal wavelengths, which were then used for the establishment of back-propagation artificial neural network (BP-ANN) models. The results revealed that the BP-ANN model based on the selected four optimal wavelengths better performed (R-C(2) = 0.986, R-CV(2) = 0.985, R-P(2) = 0. 9 9 3, RMSEC = 0. 329 m g /L, RMSECV = 0.433, RMSEP = 0.734 mg/L) than that based on the whole 381 wavelengths (R-C(2) = 0.978, R-CV(2) = 0.996, R-P(2) = 0.977, RMSEC = 0.388 mg/L, RMSECV = 0.625, RMSEP = 0.734 mg/L). Finally, a series of chemical images were developed to clearly display the concentration distribution of the sodium humate dissolved in water, demonstrating that Vis/NIR chemical imaging technique was feasible to quantify the contents of sodium humate in the aquatic environment and could be further used for real-time monitoring the quality of aquaculture water.

关键词： Imaging spectroscopy Vis/NIR Humic Gray reference image Successive projections algorithm back-propagation artificial neural network

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Modeling chlorophyll-a concentrations using an artificial neural network for precisely eco-restoring lake basin

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ECOLOGICAL ENGINEERING 2016年第0期95卷 422-429页

作者： Lu, Fang Chen, Zhi Liu, Wenquan Shao, Hongbo Concordia Univ Dept Bldg Civil & Environm Engn Montreal PQ H3G 1M8 Canada SOA Inst Oceanog 1 Key Lab Marine Sedimentol & Environm Geol Qingdao 266061 Peoples R China Jiangsu Acad Agr Sci Inst Agrobiotechnol Nanjing 210014 Jiangsu Peoples R China

A back-propagation artificial neural network (BPANN) model was developed in this study for the prediction of chlorophyll-a concentration in Lake Champlain. 21 years of monitoring data (1992-2012) of water quality parameters was used to train, validate and test the BPANN models. The optimal input parameters of the model were selected on the basis of the performance of models built with different combinations of input variables. To verify the model performances, the trained models were applied to field monitoring data from Lake Champlain. Prediction accuracy was measured by using the coefficient of determination (R-2) and RMSE-observations standard deviation ratio (RSR). The R-2 values of the best-performed model in the training set, validation set, testing set, and all-year data were 0.82, 0.93, 0.81, and 0.87, respectively. The corresponding RSR values of the three data sets and all-year were 0.62, 0.38, 0.53, and 0.48, respectively. Results indicated that the developed BPANN model can predict chlorophyll-a concentrations in Lake Champlain with high accuracy and provide a quick assessment of chlorophyll-a variation for lake management and eco-restoration. (C) 2016 Elsevier B.V. All rights reserved.

关键词： back-propagation artificial neural network Chlorophyll-a Prediction Lake water quality Eco-restoring

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Construction of Processing Maps based on Expanded Data by BP-ANN and Identification of Optimal Deforming Parameters for Ti-6Al-4V Alloy

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2016年第2期17卷 171-180页

作者： Quan, Guo-zheng Wen, Hai-rong Jia-Pan Zou, Zhen-yu Chongqing Univ Sch Mat Sci & Engn State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

The intrinsic relationships between deforming parameters and microstructural mechanisms for Ti-6Al-4V alloy were analyzed by processing maps. A series of thermal compression tests were carried out in the temperatures range of 1023 similar to 1323 K (across beta-transus) and strain rates range of 0.01 similar to 10 s(-1) on a Gleeble-3500 thermo-mechanical simulator. Based on the stress-strain data collected from compression tests, a back-propagation artificial neural network (BP-ANN) model was developed, which presents reliable performance in tracking and predicting strain-stress data. By utilizing this model, the volume of stress-strain data was expanded. According to the intensive stress-strain data, the apparent activation energy was calculated to be 564.05 kJ mol(-1) and 300.20 kJ mol(-1) for alpha+beta-phase field and single beta-phase field, respectively. Moreover, the processing maps were constructed at finer intervals of temperature, from which, the stable regions with higher power dissipation efficiency (eta > 0.3) and unstable regions with negative instability parameter (xi < 0) were clarified clearly. By combining processing map with microstructure observations, two main stable softening mechanisms, i.e., globularization and dynamic recovery (DRV) were identified, and globularization-predominant (0.3 < eta < 0.55) parameter domain ((epsilon) over dot < 0.1 s(-1)) in alpha+beta-phase field and DRV-predominant (0.25 < (epsilon) over dot < 0.41) parameter domain (0.032 s(-1) < < 1 s(-1)) in beta-phase field were recommended.

关键词： back-propagation artificial neural network Kinetic analysis Processing maps Ti-6Al-4V alloy

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