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.

scholarly journals Effect of Flexible 3D Multipoint Stretch Bending Dies on the Shape Accuracy and the Optimal Design

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xiangfeng Lin ◽  
Yi Li ◽  
Zhongyi Cai ◽  
Jicai Liang ◽  
Ce Liang ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Surface Modification ◽  
Simulation Analysis ◽  
Shape Error ◽  
Shape Accuracy ◽  
Finite Element Software ◽  
Stretch Bending ◽  
Aluminum Profile ◽  
Die Surface ◽  
Bending Process

Aiming at the flexible 3D stretch bending with multipoint dies for the aluminum profile, the numerical simulation analysis of the bending process was carried out by ABAQUS finite element software. In the multipoint stretch bending (MPSB) process, the influence of the number of die units on the springback for the complex section profile was studied. The shape error between the forming parts and the target parts was reduced through the method of die surface modification. The results showed that the springback of the aluminum profile could reach a minimum when the number of die units was 25 under the precondition of saving cost and ensuring the quality of forming parts. In the numerical simulation, the maximum shape error of the forming parts reduced from 18.824 mm to 2.456 mm; in the test, the maximum shape error reduced from 27.26 mm to 6.03 mm through the method of die surface modification.

scholarly journals Research on Fault Diagnosis of Hydraulic System of Fast Erecting Device Based on Fuzzy Neural Network

2022 ◽  
Author(s):  
Yangbing Zheng ◽  
Xiao Xue ◽  
Jisong Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Hydraulic System ◽  
Fuzzy Neural Network ◽  
Simulation Analysis ◽  
Fuzzy Relation ◽  
Analysis Procedure ◽  
Fuzzy Neural ◽  
Simulation Results

In order to improve the fault diagnosis effectiveness of hydraulic system in erecting devices, the fuzzy neural neural network is applied to carry out fault diagnosis of hydraulic system. Firstly, the main faults of hydraulic system of erecting mechanism are summarized. The main faults of hydraulic system of erecting devices concludes abnormal noise, high temperature of hydraulic oil of hydraulic system, leakage of hydraulic system, low operating speed of hydraulic system, and the characteristics of different faults are analyzed. Secondly, basic theory of fuzzy neural network is studied, and the framework of fuzzy neural network is designed. The inputting layer, fuzzy layer, fuzzy relation layer, relationship layer after fuzzy operation and outputting layer of fuzzy neural network are designed, and the corresponding mathematical models are confirmed. The analysis procedure of fuzzy neural network is established. Thirdly, simulation analysis is carried out for a hydraulic system in erecting device, the BP neural network reaches convergence after 600 times iterations, and the fuzzy neural network reaches convergence after 400 times iterations, fuzzy neural network can obtain higher accuracy than BP neural network, and running time of fuzzy neural network is less than that of BP neural network, therefore, simulation results show that the fuzzy neural network can effectively improve the fault diagnosis efficiency and precision. Therefore, the fuzzy neural network is reliable for fault diagnosis of hydraulic system in erecting devices, which has higher fault diagnosis effect, which can provide the theory basis for healthy detection of hydraulic system in erecting devices.

Research on the PID Control Strategy in BP Neural Network of Terrain Detector-Milling Cutting Depth System

2014 ◽  
Vol 945-949 ◽  
pp. 1573-1578
Author(s):  
Xiao Feng ◽  
Hao Hu ◽  
Fan Rang Kong ◽  
Shi Qiu ◽  
Ye Sun
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Simulation Analysis ◽  
Dynamic Performance ◽  
Effective Control ◽  
Cutting Depth ◽  
Hydraulic Servo

Targeting at the nonlinear, time-varying characteristics of terrain detector-milling cutting depth electro-hydraulic servo system in soil milling collection machines, this paper proposed the PID control menthod in BP neural network of terrain detector - milling cutting depth system and designed PID controller in BP neural network and conducted simulation analysis by programming with Matlab. The results show that, when compared with conventional PID control, BP neural network compounded with PID control would enable the system better dynamic performance and follow-up characteristics, therefore, it is an effective control strategy.

Research of Construction Subcontracting Enterprise Competence Based on GA-BP Neural Network

2014 ◽  
Vol 1030-1032 ◽  
pp. 2664-2667
Author(s):  
Xi Kang Yan ◽  
Jing Yu Wang
Keyword(s):  
Neural Network ◽  
Quantitative Analysis ◽  
Bp Neural Network ◽  
Simulation Analysis ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Matlab Simulation ◽  
Five Dimensions ◽  
Enterprise Competence ◽  
Bp Neural Network Model

A new evaluation index system, which includes five dimensions is put forward to evaluate the competitiveness of construction subcontracting enterprise properly. Based on GA optimized BP neural network model,construction subcontracting enterprises’ competitiveness can be quantitative analysis systematically. Use of Matlab simulation analysis,research has shown that this system can well solve the problem of construction subcontracting enterprise competitiveness evaluation.

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 Effect of Forming Parameters on Profile Thinning of Flexible 3D Multi-Point Stretch Bending

Metals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1581
Author(s):  
Zhongyuan Shi ◽  
Yi Li ◽  
Jicai Liang ◽  
Ce Liang
Keyword(s):  
Numerical Simulation ◽  
Process Parameters ◽  
Orthogonal Experiment ◽  
Three Dimensional ◽  
Simulation Software ◽  
Experimental Result ◽  
Range Analysis ◽  
Stretch Bending ◽  
Rectangular Profile ◽  
Aluminum Profiles

The ABAQUS finite element simulation software is used to simulate the flexible multi-point three-dimensional stretch bending process of aluminum profiles. The effect of process parameters on the web thickness of rectangular profile in flexible multi-point three-dimensional stretch bending is studied by orthogonal experiment and range analysis. The process parameters used in the experiments include pre-stretching value, post-stretching value, the number of multi-point dies and friction coefficient. The optimal combination of process parameters is obtained by numerical simulation and experimental verification. When the aluminum profile is completed flexible multi-point stretch bending according to the best parameters, the thickness thinning of outer web and inner web is the smallest. The experimental result is closed to the numerical simulated results. The effectiveness of the numerical simulation is verified by the corresponding experimental methods.

Demand Prediction of the Rarely Used Spare Parts Based on the BP Neural Network

2014 ◽  
Vol 519-520 ◽  
pp. 1513-1519 ◽  
Author(s):  
Hong Long Mao ◽  
Jun Wei Gao ◽  
Xi Juan Chen ◽  
Jin Dong Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulation Analysis ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Spare Parts ◽  
Back Propagation Algorithm ◽  
Demand Prediction ◽  
Consumption Data ◽  
Bp Neural Network Model

For the rarely used spare parts, as the traditional predicting methods can't keep the high accurateness, the BP neural network is used to predict the rarely used spare parts demand. Firstly, the rarely used spare parts definition and its characteristics are given in this paper. Then the three layer BP neural network model is established, the back propagation algorithm is used as the learning algorithm. Finally, the rarely used spare parts-bus coupler consumption data is used for simulation analysis based on Guangzhou Subway line 3. The results show that the prediction is good.

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.

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