Earticle

초록

영어

Wood plastic composites (WPCs) were prepared and used as pencil board. The primary raw materials were corn straw fiber powder, high-density polyethylene, and polystyrene. The tensile strength, surface roughness, hardness, and roll cutting performance of the prepared composites were tested and compared with those of linden wood, which is typically used for pencil board. A back propagation (BP) neural network was adopted to build the prediction model of process technical parameters of the composite. An improved particle swarm (modified particle swarm optimizer, MPSO) was adopted to optimize the BP neural network, and the advantages of the PSO algorithm’s global optimization ability and the BP neural network algorithm’s high processing speed were realized. The results show that the composite had the best performance, with a mass ratio of 3.8 (corn straw fiber powder): 47.6 (high-density polyethylene): 28.6 (polystyrene). The composites can be used as a substitute for linden wood in the production of pencil board.

목차

Abstract
 1. Introduction
 2. Pencil Composite Plate BP Neural Network Modeling and Algorithms
  2.1. BP Neural Network Model
  2.2. BP Neural Network Algorithm Analysis
 3. Improved Particle Swarm Optimization and MPSO-BP Neural Network Hybrid Algorithm
  3.1. Decreasing Nonlinear Inertia Weight Strategy
  3.2. Asynchronous Changes in the Value of the Acceleration Factor
 4. Experiment Research
  4.1. Design Variables
  4.2. Process Temperature
 5. Result and Discussion
 References

키워드

저자

• Bu Chiwu [ College of Light Industry, Harbin University of Commerce, Harbin 150028, P. R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605