改进差分进化算法在大型工件平面度评定中的应用

厉玉康; 黄海鸿

doi:10.3788/OPE.20192712.2659

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改进差分进化算法在大型工件平面度评定中的应用

微纳技术与精密机械 | 更新时间：2020-07-09

- 改进差分进化算法在大型工件平面度评定中的应用
- Application of improved differential evolution algorithm in flatness evaluation of large work-piece
- 光学精密工程 2019年27卷第12期页码：2659-2667
- 作者机构：
  
  1.机械工业绿色设计与制造重点实验室，安徽合肥 230009
  2.合肥工业大学机械工程学院，安徽合肥 230009
- 作者简介：
  
  [ "厉玉康(1995-)，男，安徽天长人，硕士研究生，2017年于辽宁工程技术大学获得学士学位，研究方向为机电装备成套技术与系统。E-mail：863735238@qq.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(51675155);国家自然科学基金资助项目(51635010);国家自然科学基金资助项目(51722502)
- DOI：10.3788/OPE.20192712.2659
  中图分类号： TP241
- 收稿日期：2019-04-18，
  
  录用日期：2019-6-26，
  
  纸质出版日期：2019-12-25
- 稿件说明：
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厉玉康, 黄海鸿. 改进差分进化算法在大型工件平面度评定中的应用[J]. 光学精密工程, 2019,27(12):2659-2667.

Yu-kang LI, Hai-hong HUANG. Application of improved differential evolution algorithm in flatness evaluation of large work-piece[J]. Optics and precision engineering, 2019, 27(12): 2659-2667.
厉玉康, 黄海鸿. 改进差分进化算法在大型工件平面度评定中的应用[J]. 光学精密工程, 2019,27(12):2659-2667. DOI： 10.3788/OPE.20192712.2659.

Yu-kang LI, Hai-hong HUANG. Application of improved differential evolution algorithm in flatness evaluation of large work-piece[J]. Optics and precision engineering, 2019, 27(12): 2659-2667. DOI： 10.3788/OPE.20192712.2659.

摘要

生产线上检测大型复杂工件平面度误差时，存在检测面积较大、数据量较多的问题，为了提高检测效率及精度，采用优化算法提高其平面度误差评定速度。提出将差分进化(DE)算法应用在其平面度误差的评定中，并提出将粒子群(PSO)算法的优化方法融入差分进化算法的框架，改进变异操作以提高标准DE算法的收敛速度。介绍了大型工件平面度误差评定采用最小区域法的数学模型，阐述了改进的DE算法的原理和实现步骤，最后以叉车外壁板为例，通过对外壁板平面度误差的评定以验证算法的收敛速度与精度。结果表明，改进的DE算法在大型工件平面度误差评定中收敛结果稳定，误差接近于0；精度较遗传算法提高36.83%；收敛速度较遗传算法提高58.33%，较标准的DE算法提高28.57%。可以很好地应用在大型工件平面度误差检测中，提高检测效率。

Abstract

Several problems are encountered when measuring the flatness error of large and complex workpieces in a production line

such as abroad area of the detection surface and a vast amount of data. To improve the efficiency and accuracy of flatness error detection

an optimization algorithm was adopted to increase the speed of flatness error evaluation. The Differential Evolution (DE)algorithm was implemented for solving these problems

and the optimization method of Particle Swarm Optimization(PSO) algorithm was integrated into the DE algorithm framework to increase the convergence speed by improving the mutation operation. This study proposed a mathematical model using the minimum zone method for the flatness error evaluation of large workpieces and expounded the principle and implementation steps of the improved DE algorithm. Finally

using the outer panel of a forklift truck as an example

the convergence speed and accuracy of the algorithm were verified by evaluating the flatness error of the outer panel. The results demonstrate that the convergence result of the improved DE algorithm is stable for evaluating the flatness error of large workpieces

and the error is close to zero. The accuracy of the proposed algorithm is 36.83% higher than that of the genetic algorithm

and the convergence speed is 58.33% and 28.57% higher than those of the genetic algorithm and standard DE algorithm

respectively. The proposed algorithm can be satisfactorily applied to the flatness error detection of large workpieces to improve the detection efficiency.

关键词

Keywords

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