A BP-ANN Based Surrogate Modeling for Predicting Engineering Analysis of Forging Press

2014 ◽  
Vol 915-916 ◽  
pp. 987-991 ◽  
Author(s):  
Peng Fei Bao ◽  
Wei Dong Miao ◽  
Rong Xie ◽  
Yan Jun Shi
Keyword(s):  
Surrogate Model ◽  
Back Propagation ◽  
Surrogate Modeling ◽  
Element Model ◽  
Back Propagation Neural Network ◽  
Structure Design ◽  
Engineering Analysis ◽  
Design Variables ◽  
Business Software ◽  
Forging Press

Engineering analysis and simulation are time-consuming, and often trapped to computational burden, such as analyzing forging press. We herein employ surrogate modeling to reduce such computation cost while keeping high precision. This paper use a BP neural networks to building the surrogate model (BPNN-SM for short), and predicting the analysis results of mechanical structures with this model. The predicting process include confining design variables, sampling, building finite element model with business software ANSYS, constructing surrogate model to replace the original model and finally predicting data with the new model. In such process, we build a back-propagation neural network, and train it with sampling data from ANSYS results. We tested our methods with a mechanical structure design of hydraulic forging press. The experimental results verified the surrogate modeling.

scholarly journals Research on Vibration Reduction Method of Nonpneumatic Tire Spoke Based on the Mechanical Properties of Domestic cat’s Paw Pads

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Haichao Zhou ◽  
Huiyun Li ◽  
Ye Mei ◽  
Guolin Wang ◽  
Congzhen Liu ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
High Speed ◽  
Vibration Reduction ◽  
Element Model ◽  
Approximate Model ◽  
Structure Design ◽  
Peak Amplitude ◽  
Radial Excitation ◽  
Vibration Problem ◽  
Design Variables

Although there is no risk of puncture, the vibration problem caused by discontinuous structures limits nonpneumatic tire development (NPT). The vibration reduction of nonpneumatic tires is a solvable urgent problem. This current study analyzed the dynamic grounding characteristics and the vibration reduction mechanism of the cat’s paw pads and then applied the mechanical properties to the bionic design of nonpneumatic tire spokes to solve the vibration problem. Domestic cats’ paw pads’ dynamic grounding characteristics were determined using the pressure-sensitive walkway, high-speed camera, and VIC-2D. The results indicated that the mechanical characteristics of swing deformation of paw pads during the grounding process attenuated the grounding stress and buffered the energy storage to achieve the vibration reduction effect. According to the similarity transformation, a finite element model of NPT that could accurately reconstruct the structure and realistically reflect the load deformation was employed. The structure design of asymmetric arcs on the spokes’ side edges was proposed, and it can effectively reduce the radial excitation force of NPT. The three parameters, the asymmetric arc, the thickness, and the curvature of spokes, were used as design variables to maximize the vibration reduction. The orthogonal experimental, the Kriging approximate model, and the genetic algorithm were carefully selected for optimal solutions. Compared with the original tire, the results showed that peak amplitude 1, peak amplitude 2, and the root square of the optimized tire’s amplitudes were reduced by 76.07%, 52.88%, and 51.65%, respectively. These research results offer great potential guidance in the design of low-vibration NPT.

Loss prediction of axial compressors using genetic algorithm–back propagation neural network in throughflow method

2021 ◽  
pp. 095441002110414
Author(s):  
Jian Li ◽  
Jinfang Teng ◽  
Mingmin Zhu ◽  
Xiaoqing Qiang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Working Conditions ◽  
Surrogate Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Empirical Models ◽  
Empirical Correlations ◽  
Loss Prediction ◽  
Blade Element

While the consistent advance in computational power has enabled the Computational Fluid Dynamics (CFD) an effective tool for compressor performance characterization, the need for quick performance estimates at initial design phase of compressor still requires the use of low order models. Thus, the throughflow method remains the backbone of compressors design process. The accuracy of the throughflow calculation mainly depends on the adopted empirical correlations. However, the traditional empirical models are just accurate for the conventionally loaded compressor at normal working conditions. In this article, the mechanism of blade profile loss generation and the formation mode of existing empirical correlation are studied, and the reason why the traditional diffusion factor based empirical models are not applicable for modern high-loading compressors or conventional-loading blades at negative incidence is also discussed. Then, the Genetic Algorithm assisted Back Propagation Neural Network (GA-BPNN) is used to train the surrogate model for the design and off-design loss prediction along the blade span of the compressor. Based on the test data of four transonic compressor stages, a database containing 72 sets of blade element geometry and about 1400 sets of blade element performance data is established. Considering the different mechanisms of rotor and stator losses at different working conditions, the entire database and surrogate model are divided into four components according to the rotor and stator at positive incidence and negative incidence. Comparing the prediction results of the surrogate model with the traditional empirical correlations and experimental data, the results show that the GA-BPNN is an alternative solution for developing the empirical model.

scholarly journals COMPARISON OF HYBRID METHODS WITH DIFFERENT META MODEL USED IN BRIDGE MODEL-UPDATING

2017 ◽  
Vol 12 (3) ◽  
pp. 193-202 ◽  
Author(s):  
Zhiyuan Xia ◽  
Aiqun Li ◽  
Jianhui Li ◽  
Maojun Duan
Keyword(s):  
Neural Network ◽  
Model Updating ◽  
Hybrid Methods ◽  
Back Propagation ◽  
Element Model ◽  
Optimization Method ◽  
Back Propagation Neural Network ◽  
Meta Model ◽  
The One ◽  
Two Hybrid

Two hybrid model updating methods by integration of Gaussian mutation particle swarm optimization method, Latin Hypercube Sampling technique and meta models of Kriging and Back-Propagation Neural Network respectively were proposed, and the methods make the convergence speed of the model updating process faster and the Finite Element Model more adequate. Through the application of the hybrid methods to model updating process of a self-anchored suspension bridge in-service with extra-width, which showed great necessity considering the ambient vibration test results, the comparison of the two proposed methods was made. The results indicate that frequency differences between test and modified model were narrowed compared to results between test and original model after model updating using both methods as all the values are less than 6%, which is 25%−40% initially. Furthermore, the Model Assurance Criteria increase a little illustrating that more agreeable mode shapes are obtained as all of the Model Assurance Criteria are over 0.86. The particular advancements indicate that a relatively more adequate Finite Element Model is yielded with high efficiency without losing accuracy by both methods. However, the comparison among the two hybrid methods shows that the one with Back-Propagation Neural Network meta model is better than the one with Kriging meta model as the frequency differences of the former are mostly under 5%, but the latter ones are not. Furthermore, the former has higher efficiency than the other as the convergence speed of the former is faster. Thus, the hybrid method, within Gaussian mutation particle swarm optimization method and Back-Propagation Neural Network meta model, is more suitable for model updating of engineering applications with large-scale, multi-dimensional parameter structures involving implicit performance functions.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

2018 ◽  
pp. 143-149 ◽  
Author(s):  
Ruijie CHENG
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Energy Saving ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lighting Control ◽  
Lighting Energy ◽  
Neural Network Algorithm ◽  
Energy Saving Control ◽  
Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multi­feedback model for energy­saving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

A SIMPLIFIED ANALYTICAL PROCEDURE FOR SEISMIC ANALYSIS OF TIMBER LIGHT-FRAME SHEAR WALLS

2019 ◽  
Vol 3 (Special Issue on First SACEE'19) ◽  
pp. 173-180
Author(s):  
Giorgia Di Gangi ◽  
Giorgio Monti ◽  
Giuseppe Quaranta ◽  
Marco Vailati ◽  
Cristoforo Demartino
Keyword(s):  
Analytical Procedure ◽  
Seismic Analysis ◽  
Shear Walls ◽  
Element Model ◽  
Sensitivity Analyses ◽  
Width Ratio ◽  
Damping Factor ◽  
Equivalent Viscous Damping ◽  
Design Variables ◽  
Depth Analysis

The seismic performance of timber light-frame shear walls is investigated in this paper with a focus on energy dissipation and ductility ensured by sheathing-to-framing connections. An original parametric finite element model has been developed in order to perform sensitivity analyses. The model considers the design variables affecting the racking load-carrying capacity of the wall. These variables include aspect ratio (height-to-width ratio), fastener spacing, number of vertical studs and framing elements cross-section size. A failure criterion has been defined based on the observation of both the global behaviour of the wall and local behaviour of fasteners in order to identify the ultimate displacement of the wall. The equivalent viscous damping has been numerically assessed by estimating the damping factor which is in use in the capacity spectrum method. Finally, an in-depth analysis of the results obtained from the sensitivity analyses led to the development of a simplified analytical procedure which is able to predict the capacity curve of a timber light-frame shear wall.

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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