scholarly journals Prediction of Surface Roughness for HSM Based on BP Neural Network

2017 ◽  
Author(s):  
Chen Ying ◽  
Sun Yanhong ◽  
Yang Zhengwen ◽  
Wu Guangdong
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Bp Neural Network

A study on the GA-BP neural network model for surface roughness of basswood-veneered medium-density fiberboard

Holzforschung ◽  
2020 ◽  
Vol 74 (10) ◽  
pp. 979-988
Author(s):  
Xizhi Wu ◽  
Han Niu ◽  
Xian-Jun Li ◽  
Yiqiang Wu
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Bp Neural Network ◽  
Medium Density Fiberboard ◽  
Research Work ◽  
Feed Speed ◽  
Medium Density ◽  
Belt Speed ◽  
Interface Bonding Strength ◽  
Grain Roughness

AbstractRoughness is an important property of wood surface and has a significant influence on the interface bonding strength and surface coating quality. However, there are no theoretical models for basswood-veneered medium-density fiberboard (MDF) by fine sanding from existing research work. In this paper, the basswood-veneered MDF was fine sanded with an air drum. Orthogonal experiment was implemented to study the effects of abrasive granularity, feed rate, belt speed, air drum deformation and air drum pressure on the surface roughness of basswood-veneered MDF. The simulation models of the parallel-grain roughness and the vertical-grain roughness of the sanded surface were conducted based on the BP (error back propagation) neural network, which was optimized by a genetic algorithm (GA) (GA-BP neural network), and these models were verified by extensive experimental data. The results showed that the influence of sanding parameters on parallel-grain roughness was similar to that on vertical-grain roughness. The order of influence was that: abrasive granularity > belt speed > feed speed > air drum deformation and air drum pressure. Based on the work, the parallel-grain roughness and vertical-grain roughness of basswood-veneered MDF could be well predicted by the GA-BP neural network. The average relative errors on parallel-grain roughness and vertical-grain roughness were 3.4% and 1.9%, respectively.

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

Sign in / Sign up

Export Citation Format

Share Document