scholarly journals An integrated design method for remanufacturing process based on performance demand

2021 ◽  
Author(s):  
Chao Ke ◽  
Zhigang Jiang ◽  
Shuo Zhu ◽  
Yan Wang
Keyword(s):  
Design Method ◽  
Back Propagation ◽  
Integrated Design ◽  
Back Propagation Neural Network ◽  
Process Data ◽  
Process Scheme ◽  
Functional Development ◽  
Design For Remanufacturing ◽  
Performance Recovery ◽  
Theory Of Constraint

Abstract Design for remanufacturing process (DFRP) plays a key role in implementing remanufacturing because it directly affects the performance recovery of the End-of-Life (EoL) product. Since the used parts have various failure forms and defects, these make it hard to rapidly generate the remanufacturing process scheme for satisfying the performance demand of the used product. Moreover, remanufacturing process parameters are prone to conflicts during the process of implementing remanufacturing, this leads to the failure of the remanufacturing process. For accurately generating remanufacturing scheme and solving the conflicts, an integrated design method for remanufacturing process based on performance demand is proposed, which can reuse the historical remanufacturing process data for generating the remanufacturing process scheme. Firstly, for accurately describing the performance demand, the Kansei Engineering (KE) and Quality Functional Development (QFD) are applied to analyze the performance demand data and map the demand to the engineering features. Then, Back Propagation Neural Network (BPNN) is applied to inversely generate the remanufacturing process scheme rapidly for satisfying the performance demand by reusing the historical remanufacturing process data. Meanwhile, Theory of Constraint (TOC) and TRIZ are used to identify the conflicts of the remanufacturing process and resolve the conflicts for optimizing the remanufacturing process scheme. Finally, DFRP of the saddle guideway is taken as an example to demonstrate the effectiveness of the proposed method, the result shows the design method can quickly and efficiently generate the remanufacturing process for the EoL guide rail.

scholarly journals Structure Optimization of UHV RIP Oil-SF6 bushings Based on Improved Equal Margin Design Method and Back-Propagation Neural Network

2021 ◽  
Vol 261 ◽  
pp. 03040
Author(s):  
Zhang Shiling
Keyword(s):  
Field Distribution ◽  
Optimization Design ◽  
Design Method ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Analytic Formula ◽  
Optimized Design ◽  
Thermal Coupling ◽  
Coupling Calculation ◽  
Temperature Factors

Equal margin design method based on the classic analytic formula is widely used in development of extra-high voltage bushing products, and its effectiveness and practicality have been fully validated. However, model and temperature factors have significant impact on internal E-field distribution of UHVAC and UHVDC bushing condenser, which traditional analytic formula is difficult to evaluate quantitatively, so it’s necessary to improve traditional equal margin design method. Firstly, basic principles of equal margin design method and its software package were briefly described, and the laws of model and temperature factors influencing on condenser E-field were investigated on FEM (finite element method) computing platform. Based on these, mathematical model of improved equal margin design method for bushing condenser was established, and flow chart of optimization process combining FEM electro-thermal coupling calculation with genetic algorithm was presented. The improved method was applied to design of UHV RIP oil-gas prototype to realize uniform axial E-field distribution along bushing condenser and equal partial discharge margin between adjacent foils. Bushing condenser was fabricated according to above optimized design structure, and has passed all type tests. In the paper, the FEM electro-thermal coupling calculation method was applied to the inner insulation optimization design to make bushing condenser’s design more suitable. The paper can provide some theoretical guidelines for research and development of other bushings in UHV level.

scholarly journals Novel Four-Cell Lenticular Honeycomb Deployable Boom with Enhanced Stiffness

Materials ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 306
Author(s):  
Hui Yang ◽  
Shuoshuo Fan ◽  
Yan Wang ◽  
Chuang Shi
Keyword(s):  
Design Method ◽  
Fatigue Cracks ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Maximum Principal Stress ◽  
Dynamics Analysis ◽  
Solar Sails ◽  
Bending Load ◽  
Full Factorial ◽  
Optical Telescopes

Composite thin-walled booms can easily be folded and self-deployed by releasing stored strain energy. Thus, such booms can be used to deploy antennas, solar sails, and optical telescopes. In the present work, a new four-cell lenticular honeycomb deployable (FLHD) boom is proposed, and the relevant parameters are optimized. Coiling dynamics analysis of the FLHD boom under a pure bending load is performed using nonlinear explicit dynamics analysis, and the coiling simulation is divided into three consecutive steps, namely, the flattening step, the holding step, and the hub coiling step. An optimal design method for the coiling of the FLHD boom is developed based on a back propagation neural network (BPNN). A full factorial design of the experimental method is applied to create 36 sample points, and surrogate models of the coiling peak moment (Mpeak) and maximum principal stress (Smax) are established using the BPNN. Fatigue cracks caused by stress concentration are avoided by setting Smax to a specific constraint and the wrapping Mpeak and mass of the FLHD boom as objectives. Non-dominated sorting genetic algorithm-II is used for optimization via ISIGHT software.

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.

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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