基于声发射技术的磨削状态识别方法
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摘 要
精密磨削加工是机械加工中重要方法之一，通过磨削状态识别可以有效提
高精密磨削的效率和质量。本文基于声发射技术对磨削量识别方法展开了深入
研究，对实现该方法的具体过程和关键技术进行了研究探讨。
根据采集磨削时产生的声发射信号，通过滤波的方式进行去除噪声，进一
步提取声发射信号的特征可以完成磨削状态的识别。
该方法的创新点在于以 840D 数控系统为平台，开发出 OEM 声发射信号采集
软件，通过自适应滤波对 AE 信号进行预处理，保证得到真实的磨削加工信号，
根据最大熵分布特征识别不同磨削量的 AE 信号。在此基础上可以实现智能磨削
并提高加工效率。
本文的主要研究工作围绕声发射信号采集系统、数字信号自适应滤波和提取
声发射信号的参数特征，并进行磨削量识别实验展开的，由以下几个方面组成：
1) 基于840D数控系统，开发研究了声发射信号数据采集系统，包括硬件系
统接线、参数配置和数字信号采集软件
2) 对影响磨削声发射信号噪声干扰进行了研究，并给出了具体的抑制方法，
通过对消滤波、自适应滤波器对声发射信号进行预处理提高滤噪水平，获得真实
反映磨削状态的声发射信号。
针对磨削量识别问题提出了最大熵特征算法，对磨削量重叠区域进行模糊划
分，建立了磨削量识别系统。并给出了减少误判的方法，提高了识别准确率。
通过实验完成了声发射信号采集并进行滤波去噪，然后提取最大熵特征，使
用磨削量识别算法验证了整个磨削量识别系统的有效性。针对信号的零漂给出了
解决办法，使用 AE 技术对磨削量状态进行监测，有效提高磨削质量。
分析总结了磨削状态识别方面存在的不足，并提出了进一步研究方向。
关键词：声发射 磨削状态识别 最大熵 自适应滤波 OEM 开发
ABSTRACT
Grinding status recognition is an efficient and advanced method used for
precision grinding which is one of the important grinding methods. It has been deeply
researched in this thesis, the process and key technologies have also been discussed
to enhance efficiency and quality in grinding.
Using the AE signals acquired during the grinding process, then eliminating the
noise with filtering method, and getting the character parameters, the recognition of
grinding status can be realized. The innovation of this thesis is that the data
acquisition software has been developed based on the numerical control system 840D.
The adaptive filtering is used to pre-process the AE signal and guarantee signal
reflecting the true grinding status and identify the different grinding status with the
maximum entropy method. The intelligence grinding will be carried out to improve
the efficiency.
The AE signal acquisition system, digital signals adaptive filtering and the
character parameters calculating have been researched in the paper, also including the
recognition method of grinding feed, the details as follow:
(1) Based on NC 840D, The digital signal acquisition system of is analyzed,
including hardware connection, parameter configuration and digital signal acquisition
software.
(2) The factors affecting the grinding noise of acoustic emission signals have been
studied, and give specific method for noise reduction. The noise cancellation and
adaptive filter have been adopted to pre-process the AE signal and improve the level of
filtering noise，The AE signals which are reflecting the true grinding status have been
obtained.
(3) The maximum entropy algorithm method is presented to identify the grinding
feed; the overlapping zone of grinding feed is divided with fuzzy method, also the
recognition system of grinding feed has been set up. And the method of reducing the
misjudgment has been given to improve the accuracy of recognition.
The acoustic emission signal acquisition and de-noising filter have been
completed through the experiments, and then the characteristics of maximum entropy
have been calculated, the efficiency of the entire grinding feed recognition system has
been verified with the algorithm. A solution has been given to solve the zero drift of
the signals, and the AE technology has been used to monitor the grinding status in
order to improve the grinding quality. Finally, the disadvantage of realizing the system
of recognition grinding feed is analyzed, and further research is presented.
Key Words: acoustic emission, grinding status recognition, maximum
entropy, adaptive filter, original equipment manufacture
目 录
中文摘要
ABSTRACT
摘 要...............................................................................................................................1
目 录.............................................................................................................................3
第一章 绪 论...................................................................................................................1
§1.1 课题来源及意义..............................................................................................1
§1.2 磨削状态监测方面的研究发展......................................................................2
§1.2.1 磨削状态监测技术的研究方法...............................................................2
§1.2.2 声发射技术在磨削状态监测方面的应用...............................................4
§1.2.3 声发射用于状态监测的关键技术...........................................................4
§1.3 本文研究内容及其结构..................................................................................5
§1.3.1 本文的研究内容.......................................................................................5
§1.3.2 本文的结构...............................................................................................6
第二章 磨削声发射信号采集系统.................................................................................7
§2.1 AE 信号采集原理 ............................................................................................ 7
§2.2 采集系统硬件组成...........................................................................................7
§2.2.1 硬件组成介绍...........................................................................................8
§2.2.2 硬件连线示意图.....................................................................................12
§2.2.3 840D 数控系统中 DMP 模块参数配置 ................................................. 13
§2.3 数据采集软件的开发....................................................................................17
§2.3.1 数控开发环境文件系统.........................................................................17
§2.3.2 AE 数字信号采集软件开发流程 ............................................................18
§2.3.3 OEM 软件开发界面................................................................................23
§2.4 本章小结........................................................................................................23
第三章 磨削 AE 信号自适应滤波............................................................................... 25
§3.1 磨削加工时的干扰噪声................................................................................26
§3.1.1 电磁干扰噪声.........................................................................................26
§3.1.2 机械噪声.................................................................................................27
§3.1.3 干扰噪声抑制方法.................................................................................27
§3.2 车间背景噪声对消滤波................................................................................27
§3.2.1 自适应对消滤波原理.............................................................................28
§3.2.2 对消滤波应用实例.................................................................................30
§3.3 磨削 AE 信号处理自适应滤波器设计........................................................ 30
§3.3.1 自适应滤波器结构.................................................................................30
§3.3.2 LMS 自适应滤波算法 .............................................................................31
§3.3.2 可变步长 LMS 自适应滤波算法 ...........................................................33
§3.4 滤波器的性能分析........................................................................................34
§3.5 本章小结........................................................................................................35
第四章 磨削状态最大熵识别算法...............................................................................36
§4.1 有效声发射信号的标准化处理....................................................................36
§4.2 磨削声发射信号的特征分析........................................................................37
§4.2.1 声发射信号特征参数分析.....................................................................38
§4.2.2 声发射信号参数分析方法比较.............................................................40
§4.2.3 声发射信号波形分析.............................................................................42
§4.3 磨削量识别的最大熵特征算法....................................................................43
§4.3.1 最大熵概率分布特征算法原理..............................................................43
§4.3.2 最大熵概率分布特征计算实例.............................................................44
§4.4 磨削量的模糊识别方法................................................................................46
§4.4.1 构造磨削量模糊划分的隶属度函数.....................................................47
§4.4.2 磨削量的模糊识别方法.........................................................................47
§4.4.3 模糊识别减少误判的方法.....................................................................49
§4.5 本章小结........................................................................................................50
第五章 磨削状态识别试验研究...................................................................................51
§5.1 实验目的和条件............................................................................................51
§5.1.1 实验目的.................................................................................................51
§5.1.2 实验条件.................................................................................................51
§5.2 磨削量识别方法验证....................................................................................52
§5.2.1 有效磨削 AE 信号零漂处理................................................................. 52
§5.2.2 背景噪声滤波效果比较.........................................................................53
§5.2.3 自适应滤波器滤波处理..........................................................................54
§5.2.4 磨削量最大熵识别算法验证.................................................................56
§5.3 本章小结........................................................................................................59
第六章 论文总结和展望...............................................................................................60
§6.1 全文总结........................................................................................................60
§6.2 研究展望........................................................................................................61
参考文献.........................................................................................................................63
在读期间公开发表的论文和承担科研项目及取得成果.............................................67
致 谢.............................................................................................................................68
摘要：
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摘要精密磨削加工是机械加工中重要方法之一，通过磨削状态识别可以有效提高精密磨削的效率和质量。本文基于声发射技术对磨削量识别方法展开了深入研究，对实现该方法的具体过程和关键技术进行了研究探讨。根据采集磨削时产生的声发射信号，通过滤波的方式进行去除噪声，进一步提取声发射信号的特征可以完成磨削状态的识别。该方法的创新点在于以840D数控系统为平台，开发出OEM声发射信号采集软件，通过自适应滤波对AE信号进行预处理，保证得到真实的磨削加工信号，根据最大熵分布特征识别不同磨削量的AE信号。在此基础上可以实现智能磨削并提高加工效率。本文的主要研究工作围绕声发射信号采集系统、数字信号自适应滤波和提取声发射信...
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作者：陈辉
分类：高等教育资料
价格：15积分
属性：72 页
大小：1.95MB
格式：PDF
时间：2024-11-19
