Process Optimization Studies Based on BP Neural Network in Electroless Plating of Ni-Fe-Co-P on Carbon Fiber

2012 ◽  
Vol 616-618 ◽  
pp. 1978-1983
Author(s):  
Ying Jia ◽  
Hu Xu ◽  
Cheng Feng ◽  
Xing Yun Wang
Keyword(s):  
Neural Network ◽  
Carbon Fiber ◽  
Electroless Plating ◽  
Bp Neural Network ◽  
Ph Value ◽  
Orthogonal Experiment ◽  
Predictive Ability ◽  
Sodium Hypophosphite ◽  
Experimental Program ◽  
Method Of Calculation

It presents a method to predict and optimize the electroless plating process, and to compare the predictive ability of the network to the experimental results. It combined with the neural network and orthogonal experiment and used a small step searching method to optimize the chemical process of plated Ni - Co-Fe-P on carbon fiber within the scope of the process parameters, got more optimized process recipe: the temperature is 88°C, ratio of the main salt concentration is 0.46, the concentration of sodium citrate is 46 g/L, and the pH value is 9.03, the concentration of sodium hypophosphite is 24 g/L. Through validating with experiments, the error between them is 2.39%, the linear correlation between the method of calculation and experimental program of the target is very good, and the correlation coefficient R =0.99943 which indicated that the training results are reliable and the BP neural network optimizing the process recipe is indeed feasible.

Based on BP Neural Network Discrete Data Forecast

2011 ◽  
Vol 50-51 ◽  
pp. 977-981 ◽  
Author(s):  
Jing Wang ◽  
Guo Li Wang ◽  
Jian Hui Wu ◽  
Yu Su
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Methods ◽  
Bp Neural Network ◽  
Model Fitting ◽  
Predictive Ability ◽  
Discrete Data ◽  
Engineering System ◽  
Forecast Performance ◽  
Artificial Neural

Artificial neural network is based on human brain structure and operational mechanism based on knowledge and understanding of its structure and behavior of simulated an engineering system. BP artificial neural network is an important component of neural networks, as it can on the linear or nonlinear multivariable without preconditions in the case of statistical analysis, with the traditional statistical methods, analysis of the variables need to be consistent with certain conditions compared to its own advantage. The BP neural network does not need the precise mathematical model, does not have any supposition request to the material itself. Its processing non-linear problem's ability is stronger than traditional statistical methods. This article uses two groups of data to establish the BP neural network model separately, and carries on the comparison to the model fitting ability and the forecast performance, discovered BP neural network when data distribution relative centralism fits ability, forecasts the stable property. But the predictive ability is unable in the discrete data application to achieve anticipated ideally.

scholarly journals Springback Study on Profile Flexible 3D Stretch-Bending Process Using the Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yi Li ◽  
Ce Liang ◽  
Xiangfeng Lin ◽  
Jicai Liang ◽  
Zhongyi Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Material Properties ◽  
Simulation Analysis ◽  
Orthogonal Experiment ◽  
Range Analysis ◽  
Finite Element Software ◽  
Stretch Bending ◽  
Aluminum Profile ◽  
Bending Process

The springback is one of the main defects in the flexible 3D stretch-bending process. In this paper, according to the orthogonal design of experiments, the numerical simulation analysis of the springback for the 3D stretch-bending aluminum profile is carried out by the ABAQUS finite element software. And to investigate the effect of material properties on the springback, the range analysis of the orthogonal experiment is performed. The results show that these material properties of the aluminum profile (elastic modulus E, yield strength σy, and tangent modulus E1) might have the biggest influence on the springback of the aluminum profile, and the optimized forming parameters are founded as follows: the horizontal bending degree is 14°, the vertical bending degree is 14°, the number of multipoint stretch-bending dies is 10, the friction coefficient is 0.15, and aluminum alloy grade is 6063. Moreover, the model of the BP neural network for the prediction of the springback is established and trained based on the orthogonal experiment, and the results with the BP neural network model are in good agreement with experimental results. So it is obvious that the BP neural network could predict effectively the springback of 3D multipoint stretch-bending parts.

Use BP Neural Network Set up the Corrosion Prediction Model of Low Temperature Parts of Atmospheric Pressure Device

2012 ◽  
Vol 152-154 ◽  
pp. 1138-1142 ◽  
Author(s):  
Yu Guang Fan ◽  
Zai Dong Piao ◽  
Bing Chen ◽  
Hong Xian Lin ◽  
Yang Yang
Keyword(s):  
Neural Network ◽  
Low Temperature ◽  
Prediction Model ◽  
Corrosion Protection ◽  
Bp Neural Network ◽  
Atmospheric Pressure ◽  
Ph Value ◽  
Set Up

In research of the low temperature parts of atmospheric pressure device, by using BP neural network, the connection of PH value, Cl-, H2S and Fe+2 was setup which can predict Fe+2 content accurately, and obtain the requirement accuracy, hence more accurate corrosion can be predicted and providing more suggests for corrosion protection.

scholarly journals Preparation of electrospun nanofiber membrane for air filtration and process optimization based on BP neural network

2021 ◽  
Author(s):  
Le Kang ◽  
Yuankun Liu ◽  
Liping Wang ◽  
Xiaoping Gao
Keyword(s):  
Neural Network ◽  
Quality Factor ◽  
Flow Rate ◽  
Bp Neural Network ◽  
Polyvinylidene Fluoride ◽  
Orthogonal Experiment ◽  
Solution Concentration ◽  
Filtration Efficiency ◽  
Air Filtration ◽  
Filtration Resistance

Abstract The filtration layer in a medical protective mask can effectively prevent aerosol particles that might carry viruses from air. A nanofiber/microfiber composite membrane (NMCM) was successfully fabricated by electrospinning polyvinylidene fluoride (PVDF) nanofibers collected on the electrified and melt-blown polypropylene (PP) nonwovens, aiming to improve the filtration efficiency and reduce the resistance of respiration of mask. A four-factor and three-level orthogonal experiment was designed to study the effect of electrospinning parameters such as spinning solution concentration, voltage, tip-collect distance (TCD), and flow rate of solution on the filtration efficiency, resistance of respiration as well as quality factor of NMC developed to predict the resistance of respiration. Experimental results demonstrated that the filtration efficiency of NMCM≥95% in comparison to that of electrified and melt-blown PP nonwovens 79.38%, which increases by 19.68%. Additionally, the average resistance of respiration is 94.78 Pa, which meets the protection requirements. Multivariate analysis of variance indicated that the resistance of respiration of the NMCM has significantly dependent on the concentration, voltage, TCD, and flow rate of the spinning solution and the quality factor of the NMCM has dependent on the resistance of respiration. The air permeability ranges from 166.23 to 314.35mm/s, which is inversely proportional to the filtration resistance. As far as the filtration resistance is concerned, the optimal spinning parameters were obtained as follows. The concentration of spinning solution is 15%, the voltage is 27 kV, the TCD is 22 cm, and the flow rate is 2.5 mL/h. The relative error of the BP neural network varies from 0.49505% to 1.49217%, i.e. the error value varies from 0.17 to1.33 Pa. The predicted resistance of respiration corresponding to the optimal process is 68.1374 Pa.

scholarly journals Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network

Coatings ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1402
Author(s):  
Yutao Li ◽  
Kaiming Wang ◽  
Hanguang Fu ◽  
Xiaohui Zhi ◽  
Xingye Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Dilution Rate ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Orthogonal Experiment ◽  
Scanning Speed ◽  
Good Prediction ◽  
Optimal Process ◽  
Orthogonal Experiments ◽  
Fcc Phase

The dilution rate has a significant impact on the composition and microstructure of the coatings, and the dilution rate and process parameters have a complex coupling relationship. In this study, three process parameters, namely laser power, powder feeding rate, and scanning speed, were selected as variables to design the orthogonal experiment. The dilution rate and hardness data were obtained from AlCoCrFeNi coatings based on orthogonal experiments. Then, a BP neural network was used to establish a prediction model of the process parameters on the dilution rate. The established BP neural network exhibited good prediction of the dilution rate of AlCoCrFeNi coatings, and the average relative error between the predicted value and the experimental value was only 5.89%. Subsequently, the AlCoCrFeNi coating was fabricated with the optimal process parameters. The results show that the coating was well-formed without defects, such as cracks and pores. The microhardness of the AlCoCrFeNi coating prepared with the optimal process parameters was 521.6 HV0.3. The elements were uniformly distributed in the microstructure, and the grain size was about 20–60 μm. The microstructure of the AlCoCrFeNi coating was only composed of the BCC phase without the existence of the FCC phase and intermetallic compounds.

The Preparation and Growth Mechanism of Ni-Coated TiH2 Composite Powder by Electroless Plating

2013 ◽  
Vol 743-744 ◽  
pp. 353-359 ◽  
Author(s):  
Jia Qian Hou ◽  
Lai Rong Xiao ◽  
Lei Guo ◽  
Song Song Zhou ◽  
Ruo Fan Wang
Keyword(s):  
Growth Mechanism ◽  
Electroless Plating ◽  
Composite Powder ◽  
Ostwald Ripening ◽  
Ph Value ◽  
Orthogonal Experiment ◽  
Layer Growth ◽  
X Ray Diffraction ◽  
Powder Morphology ◽  
Technical Parameters

The Ni-coated TiH2composite powder was prepared by electroless plating and the concentration of reducing agent, reaction temperature, reaction time, PH value and so on were optimized by orthogonal experiment design. The Ni/TiH2composite powder morphology and composition was analyzed by scanning electron microscopy (SEM), Energy Dispersive spectroscopy (EDS), X-ray diffraction (XRD); the plated Ni layer growth mechanism was explored preliminary. The Optimization technical parameters: the plating temperature was 85, the pH value was 10 and the hydrazine concentration was 100ml/L. Complete coating and uniform thickness of the Ni layer with average coating thickness about 2.0μm was successfully prepared with the optimization technical parameters. The growth mechanism of the coating followed the Ostwald ripening mechanism. Compared the TiH2uncoated with Ni layers particles, the TiH2composite powder could efficiently delay the starting time of gas release by approximately 80s.

BP Neural Network Predicting the Rate of Photo-Catalytic Degradation

2012 ◽  
Vol 554-556 ◽  
pp. 1624-1627
Author(s):  
De Qing Chu ◽  
Bao Guang Mao ◽  
Li Min Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Orthogonal Experiment ◽  
Sol Gel ◽  
Catalytic Degradation ◽  
Hydrothermal Technique ◽  
Predictable Process ◽  
Photo Catalyst ◽  
Four Levels ◽  
Bpnn Model

The composite photo-catalyst (PW12-TiO2) was prepared by a sol-gel and hydrothermal technique. According to the experimental data of the four factors and four levels orthogonal experiment, the BPNN model was built to predict the rate of photo-catalytic degradation and the predictable process was achieved through the software of MATLAB 7.1, The results showed that the BPNN can be used to predicting the rate of photo-catalytic degradation.

scholarly journals Technical and tactical diagnosis model of table tennis matches based on BP neural network

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Wenwen Huang ◽  
Miaomiao Lu ◽  
Yuxuan Zeng ◽  
Mengyue Hu ◽  
Yi Xiao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Back Propagation ◽  
Predictive Ability ◽  
Diagnostic Model ◽  
Table Tennis ◽  
Diagnosis Model

Abstract Background The technical and tactical diagnosis of table tennis is extremely important in the preparation for competition which is complicated by an apparent nonlinear relationship between athletes’ performance and their sports quality. The neural network model provides a high nonlinear dynamic processing ability and fitting accuracy that may assist in the diagnosis of table tennis players’ technical and tactical skill. The main purpose of this study was to establish a technical and tactical diagnosis model of table tennis matches based on a neural network to analyze the influence of athletes’ techniques and tactics on the competition results. Methods A three-layer Back Propagation (BP) neural network model for table tennis match diagnosis were established. A Double Three-Phase evaluation method produced 30 indices that were closely related to winning table tennis matches. A data sample of 100 table tennis matches was used to establish the diagnostic model (n = 70) and evaluate the predictive ability of the model (n = 30). Results The technical and tactical diagnosis model of table tennis matches based on BP neural network had a high-level of prediction accuracy (up to 99.997%) and highly efficient in fitting (R2 = 0.99). Specifically, the technical and tactical diagnosis results indicated that the scoring rate of the fourth stroke of Harimoto had the greatest influence on the winning probability. Conclusion The technical and tactical diagnosis model of table tennis matches based on BP neural network was highly accurate and efficiently fit. It appears that the use of the model can calculate athletes’ technical and tactical indices and their influence on the probability of winning table tennis matches. This, in turn, can provide a valuable tool for formulating player’s targeted training plans.

Study on Shrinkage of Concrete Based on BP Neural Network

2010 ◽  
Vol 163-167 ◽  
pp. 3249-3257
Author(s):  
Tie Cheng Wang ◽  
Quan Min Peng ◽  
Wen Liang Liu ◽  
Li Feng Feng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Orthogonal Experiment ◽  
Simulation Method ◽  
Effect Analysis ◽  
Concrete Shrinkage ◽  
The Neural Network ◽  
Mix Proportion ◽  
Longitudinal Length ◽  
Optimal Mix

In order to solve the problem of concrete cracking in wellhead groove with large longitudinal length, shrinkage tests of concrete containing fly ash and slag in natural environment were conducted. According to the experimental study, the shrinkage model of concrete based on BP neural network is established and the results from model agree well with the measured results. Then the effect analysis of single factor on shrinkage using the neural network model is carried out. Simultaneously, the optimal mix proportion of concrete is successfully predicted by orthogonal experiment of numerical simulation method, so that it provides reference to the control of concrete shrinkage for the engineering structure.

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