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

Abstract

Roughness 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.

A Dynamic Econometric Analysis of Urbanization and Ecological Environment in Silk Road Economic Belt

“…To better represent the degree of disorder among the urbanization subsystem and the ecology subsystem in Table 1, the coupling degree model of (2) is introduced [18]as follows:…”

Investigation on Milling Quality of Stone–Plastic Composite Using Response Surface Methodology

“…They showed that the response surface model can be used for single-and multi-objective optimization. Meanwhile, a simulation model for the surface roughness was established by Wu et al 12 back-propagation (BP) neural network based on the genetic algorithm (GA-BP neural network), for medium-density fiberboard ground by metal-matrix composites. That work indicated that a GA-BP neural network can be used to predict the changes in surface roughness under different grinding conditions.…”

Prediction of Postoperative Survival Level of Esophageal Cancer Patients Based on Kaplan-Meier(K-M) Survival Analysis and Gray Wolf Optimization (GWO)-BP Model

“…BP neural network model [17,18], genetic algorithm model [19,20], support vector machine model [21], decision tree method [22]and time series method [23] were commonly used prediction methods at present. However, BP neural network had some defects such as local optimization, irrelevant to physical meaning, strong dependence on training data and slow convergence speed, which hindered its application in practical engineering [17,19]. Strong macro search and global optimization capabilities were the characteristics of genetic algorithm (GA) [20].…”

“…The proposed GPU-based training of BP neural network was tested on a breast cancer data, which shown a significant enhancement in training speed [16]. BP neural network model [17,18], genetic algorithm model [19,20], support vector machine model [21], decision tree method [22]and time series method [23] were commonly used prediction methods at present. However, BP neural network had some defects such as local optimization, irrelevant to physical meaning, strong dependence on training data and slow convergence speed, which hindered its application in practical engineering [17,19].…”