Prediction of viscosity of ternary tin-based lead-free solder melt using BP neural network

Purpose Viscosity is an important basic physical property of liquid solders. However, because of the very complex nonlinear relationship between the viscosity of the liquid ternary Sn-based lead-free solder and its determinants, a theoretical model for the viscosity of the liquid Sn-based solder alloy has not been proposed. This paper aims to address the viscosity issues that must be considered when developing new lead-free solders. Design/methodology/approach A BP neural network model was established to predict the viscosity of the liquid alloy and the predicted values were compared with the corresponding experimental data in the literature data. At the same time, the BP neural network model is compared with the existing theoretical model. In addition, a mathematical model for estimating the melt viscosity of ternary tin-based lead-free solders was constructed using a polynomial fitting method. Findings A reasonable BP neural network model was established to predict the melt viscosity of ternary tin-based lead-free solders. The viscosity prediction of the BP neural network agrees well with the experimental results. Compared to the Seetharaman and the Moelwyn–Hughes models, the BP neural network model can predict the viscosity of liquid alloys without the need to calculate the relevant thermodynamic parameters. In addition, a simple equation for estimating the melt viscosity of a ternary tin-based lead-free solder has been proposed. Originality/value The study identified nine factors that affect the melt viscosity of ternary tin-based lead-free solders and used these factors as input parameters for BP neural network models. The BP neural network model is more convenient because it does not require the calculation of relevant thermodynamic parameters. In addition, a mathematical model for estimating the viscosity of a ternary Sn-based lead-free solder alloy has been proposed. The overall research shows that the BP neural network model can be well applied to the theoretical study of the viscosity of liquid solder alloys. Using a constructed BP neural network to predict the viscosity of a lead-free solder melt helps to study the liquid physical properties of lead-free solders that are widely used in electronic information.

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A novel heuristic approach for sustainable social and economic development based on green computing technology and big data

Journal of Enterprise Information Management ◽

10.1108/jeim-12-2020-0553 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Xiaoman Wu ◽

Jun Liu ◽

Yulian Peng

Keyword(s):

Neural Network ◽

Economic Development ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Green Computing ◽

Computing Technology ◽

Content Type ◽

Social And Economic Development ◽

Bp Neural Network Model

PurposeWithout damaging and consuming natural resources, green computing technology can meet the needs of society for a long time. This paper discusses how to realize the sustainable development of social economy through the innovation of green computing technology.Design/methodology/approachFor the green computing technology and sustainable social and economic development problems, it builds back propagation (BP) neural network model and analyzes the topological structure of the network model as well as the impact of the training errors allowed by the network on its performance.FindingsBy optimizing the number of input nodes, the number of hidden nodes and the target value, the genetic algorithm (GA) can get the optimal neural network model. The simulation experiment proves that the proposed model is effective.Originality/valueIt can not only reduce the possibility of falling into local optimum, but also optimize the initial weights and thresholds of BP neural network and further improve the stability and test effect of BP neural network model.

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Estimating nitrogen contents of apple leaves based on hyperspectral parameter models at different phenophases

JOURNAL OF ADVANCES IN AGRICULTURE ◽

10.24297/jaa.v6i2.5377 ◽

2016 ◽

Vol 6 (2) ◽

pp. 942-952

Author(s):

Xicun ZHU ◽

Zhuoyuan WANG ◽

Lulu GAO ◽

Gengxing ZHAO ◽

Ling WANG

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Shoot Growth ◽

Hyperspectral Data ◽

Estimation Model ◽

Apple Leaves ◽

Bp Neural Network Model ◽

Nitrogen Contents

The objective of the paper is to explore the best phenophase for estimating the nitrogen contents of apple leaves, to establish the best estimation model of the hyperspectral data at different phenophases. It is to improve the apple trees precise fertilization and production management. The experiments were done in 20 orchards in the field, measured hyperspectral data and nitrogen contents of apple leaves at three phenophases in two years, which were shoot growth phenophase, spring shoots pause growth phenophase, autumn shoots pause growth phenophase. The study analyzed the nitrogen contents of apple leaves with its original spectral and first derivative, screened sensitive wavelengths of each phenophase. The hyperspectral parameters were built with the sensitive wavelengths. Multiple stepwise regressions, partial least squares and BP neural network model were adopted in the study. The results showed that 551 nm, 716 nm, 530 nm, 703 nm; 543 nm, 705 nm, 699 nm, 756 nm and 545 nm, 702 nm, 695 nm, 746 nm were sensitive wavelengths of three phenophases. R551+R716, R551*R716, FDR530+FDR703, FDR530*FDR703; R543+R705, R543*R705, FDR699+FDR756, FDR699*FDR756and R545+R702, R545*R702, FDR695+FDR746, FDR695*FDR746 were the best hyperspectral parameters of each phenophase. Of all the estimation models, the estimated effect of shoot growth phenophase was better than other two phenophases, so shoot growth phenophase was the best phenophase to estimate the nitrogen contents of apple leaves based on hyperspectral models. In the three models, the 4-3-1 BP neural network model of shoot growth phenophase was the best estimation model. The R2 of estimated value and measured value was 0.6307, RE% was 23.37, RMSE was 0.6274.

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Prediction of Stock Market by BP Neural Network Model

Journal of Physics Conference Series ◽

10.1088/1742-6596/1744/4/042232 ◽

2021 ◽

Vol 1744 (4) ◽

pp. 042232

Author(s):

Benben Ruan

Keyword(s):

Neural Network ◽

Stock Market ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Bp Neural Network Model

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Regional logistics demand forecasting: a BP neural network approach

Complex & Intelligent Systems ◽

10.1007/s40747-021-00297-x ◽

2021 ◽

Author(s):

Lijuan Huang ◽

Guojie Xie ◽

Wende Zhao ◽

Yan Gu ◽

Yi Huang

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Demand Forecasting ◽

Indicator System ◽

Regional Logistics ◽

Logistics Enterprises ◽

Logistics Demand ◽

Bp Neural Network Model

AbstractWith the rapid development of e-commerce, the backlog of distribution orders, insufficient logistics capacity and other issues are becoming more and more serious. It is very significant for e-commerce platforms and logistics enterprises to clarify the demand of logistics. To meet this need, a forecasting indicator system of Guangdong logistics demand was constructed from the perspective of e-commerce. The GM (1, 1) model and Back Propagation (BP) neural network model were used to simulate and forecast the logistics demand of Guangdong province from 2000 to 2019. The results show that the Guangdong logistics demand forecasting indicator system has good applicability. Compared with the GM (1, 1) model, the BP neural network model has smaller prediction error and more stable prediction results. Based on the results of the study, it is the recommendation of the authors that e-commerce platforms and logistics enterprises should pay attention to the prediction of regional logistics demand, choose scientific forecasting methods, and encourage the implementation of new distribution modes.

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Threat Level Forecast for Ship's Oil Spill - Based on BP Neural Network Model

10.1109/cise.2009.5364349 ◽

2009 ◽

Cited By ~ 1

Author(s):

Wenxue Cai ◽

Yanwu Zheng ◽

Yongqiang Shi ◽

Huiling Zhong

Keyword(s):

Neural Network ◽

Network Model ◽

Oil Spill ◽

Neural Network Model ◽

Bp Neural Network ◽

Threat Level ◽

Bp Neural Network Model

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Research on GA-BP neural network model of surface roughness in air drum sanding process for poplar

European Journal of Wood and Wood Products ◽

10.1007/s00107-021-01686-2 ◽

2021 ◽

Author(s):

Xizhi Wu ◽

Han Niu ◽

Xian-Jun Li ◽

Yiqiang Wu

Keyword(s):

Neural Network ◽

Surface Roughness ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Bp Neural Network Model

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Misfire Diagnosis of Diesel Engine Based on Short-Time Vibration Characters

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.34-35.301 ◽

2010 ◽

Vol 34-35 ◽

pp. 301-305

Author(s):

Zhao Qian Zhu ◽

Jue Yang ◽

Xiao Ming Zhang ◽

Xiao Lei Li

Keyword(s):

Neural Network ◽

Diesel Engine ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Vibration Signal ◽

Vibration Monitoring ◽

Monitoring Test ◽

Bp Neural Network Model ◽

Short Time

This paper studied misfire diagnosis of diesel engine based on short-time vibration characters. Misfire of diesel engine was simulated by the vibration monitoring test. Cylinder vibration signal and top center signal were collected under different states. The short-time vibration signal of each cylinder was intercepted according to the diesel combustion sequence, effective value was calculated, and BP Neural Network model built with this character was used to diagnose diesel misfire. The result shows that this method can locate the misfire cylinder effectively, and it is meaningful for guiding the detection and repair of vehicles.

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Research on recognition method of cloud precipitation particle shape based on BP neural network

MATEC Web of Conferences ◽

10.1051/matecconf/202133606011 ◽

2021 ◽

Vol 336 ◽

pp. 06011

Author(s):

Haonan Dong ◽

Ruili Jiao ◽

Minsong Huang

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Particle Shape ◽

Northern China ◽

Recognition Method ◽

Cloud Particle ◽

Particle Images ◽

Bp Neural Network Model

In order to solve the problem that the shape of cloud particle images measured by airborne cloud imaging probe (CIP) cannot be automatically recognized, this paper proposes an automatic recognition method of cloud and precipitation particle shape based on BP neural network. This method mainly uses a set of geometric parameters which can better describe the shape characteristics of cloud precipitation particles. Based on the cloud precipitation particle images measured by CIP in the precipitation stratiform clouds in northern China, a particle shape data training set and a testing set were constructed to train and verify the effect of the selected BP neural network model. The selected BP neural network model can classify the cloud particle image into tiny, column, needle, dendrite, aggregate, graupel, sphere, hexagonal and irregular. Utilizing the field campaign data measured by CIP, the habit identified results by the improved Holroyd method and by the selected BP neural network model were compared, which shows that the accuracy of BP neural network method is better than that of improved Holroyd method.

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