河南工程学院论文格式.doc

河南工程学院毕业设计(论文)
题目
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摘要
本文在对摩擦焊接头的形成过程的分析和实验研究的基础上,用BP网络建立了摩擦焊主要工艺参数(摩擦时间、摩擦压力、顶锻压力)和接头性能(接头强度)的预测系统。在神经网络的训练过程中,在快速BP算法的基础上做了一些有效的改进,如:引入权值系数β、选取相应的误差函数系数α等。实验结果表明,该系统对摩擦时间、摩擦压力、顶锻压力和接头强度的预测值和实际值吻合良好,符合工程应用的要求。
采用先进的超声C扫描成像系统对摩擦焊试件1的接头进行超声波无损检测,记录下其相应的超声扫描信号。在对扫描信号的处理中,使用目前无损检测领域较为前沿的信号处理方法——小波包分析法,并通过分析不同区域的小波包分解系数灰度图把信号分为三类:无缺陷信号、未焊合缺陷信号、弱结合缺陷信号。该套分析方法又成功地将另外两个试件的超声波检测信号同样地分为三类。
利用小波包法和幅频特性曲线法分别提取信号特征,并把其作为信号缺陷识别神经网络模型的输入,通过训练,建立了基于BP网络的摩擦焊接头超声波扫描信号的模式识别系统。该系统能够很好地区别各类信号,实现信号的自动识别分类。
关键词:
摩擦焊接头神经网络 BP算法超声波检测信号特征

Abstract
The friction welding-a modern connecting method is used in the various fields more and more widely and so the study of this field is now ing more and more imperative.
In this paper, on the base of analyzing and experimental studying on the process in the forming process of the friction welded joints, work is used to construct the work prediction system of the major technical parameters (friction-time ,friction force and so on) and the capability of the friction welding joints. During the training of the work, the influence, which is caused by the work parameters, on the error function is discussed emphatically and the traditional BP algorithm is improved by importing the weight coefficient βand error function coefficient α. The forecast values can meet the actual values well..
In addition, using ultrasonic testing machine test the friction welding joint and the modern analyzing method –wavelet packet analyze is used to class the reflected echo scan signal into three sorts: good welding, un-welding and weak defect. After classing, the selected characters of signal according two analyze methods –wavelet packet analyze and amplitude-frequency analyze are used to construct the classing-prediction work.
At last, in order to convenient for practice application, visual C++ is applied to integrate these applicati