基于改进鱼群算法的铝热连轧摩擦系数模型研究

doi:10.3969/j.issn.1000-1158.2016.01.13

Abstract
Figure/Table
References (14)
Related Citation (15)

Download: PDF (306 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract In the level 2 process automation of hot rolling,there is a problem of low forecast accuracy of friction coefficient in the deformation zone. A quantitative relationship between the oil film thickness, the rolling temperature and the coefficient are analyzed. The model of friction coefficient which is more suitable for the scene of the rolling is established through the improved artificial fish swarm algorithm. The above model is applied to the aluminum strip machine and the results show that the new model improved the soft measurement precision of the friction coefficient, the stability of rolling and played a reference role in the other model base on the model of the friction coefficient.

Key words： metrology friction coefficient improved fish swarm algorithm oil film thickness aluminum hot rolling

Received: 26 February 2014 Published: 10 December 2015

PACS:

TB93

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	SUN Hao
	YANG Jing-ming
	HU Zi-yu
	CHE Hai-jun
	HAN Le

Cite this article:

SUN Hao,YANG Jing-ming,HU Zi-yu, et al. Study on Aluminum Hot Rolling Model of Friction Coefficient Based on Improved Fish Swarm Algorithm[J]. Acta Metrologica Sinica, 2016, 37(1): 53-55.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2016.01.13 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2016/V37/I1/53

［1］黄凤良. 软测量思想与软测量技术［J］. 计量学报, 2004, 25(3): 284-288.
［2］杨景明,马凤艳,车海军,等.基于最小二乘支持向量机的反向建模的铝合金变形抗力模型［J］.计量学报,2013,34（6）:532-536.
［3］赵志伟,杨景明,车海军,等.基于人工蜂群算法与反向传播神经网络的铝热连轧轧制力预测［J］.计量学报,2014,35（2）:157-160.
［4］王宗宽, 周亚军. 铝板带轧制过程中的工艺润滑及影响因素［J］. 轻合金加工技术, 2008, 35(11): 29-32.
［5］李小玉. 轧制工艺润滑［M］. 北京:冶金工业出版社, 1981.
［6］白振华, 周庆田, 窦爱民, 等. 冷连轧高速轧制过程中摩擦因数机理模型的研究［J］. 钢铁, 2007, 42(5): 47-50.
［7］冷热轧板带轧机的模型与控制［M］. 北京:冶金工业出版社, 2010.
［8］孙建林. 轧制工艺润滑原理, 技术与应用［M］. 北京:冶金工业出版社, 2004.
［9］王联国, 洪毅, 赵付青, 等. 一种改进的人工鱼群算法［J］. 计算机工程, 2008, 34(19): 192-194.
［10］范玉军, 王冬冬, 孙明明. 改进的人工鱼群算法［J］. 重庆师范大学学报(自然科学版), 2007, 24(3): 23-26.
［11］李晓磊. 一种新型的智能优化方法——人工鱼群算法［D］. 杭州: 浙江大学, 2003.
［12］Shen W, Guo X, Wu C, et al. Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm［J］. Knowledge-Based Systems, 2011, 24(3): 378-385.
［13］Wang C R, Zhou C L, Ma J W. An improved artificial fish-swarm algorithm and its application in feed-forward neural networks［C］//Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on, 2005, 5: 2890-2894.
［14］Tsai H C, Lin Y H.Modification of the fish swarm algorithm with particle swarm optimization formulation and communication behavior［J］.Applied Soft Computing, 2011, 11(8): 5367-5374.