面向磨削烧伤问题的间接监测技术研究
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摘要
过程监控系统是连接底层生产制造设备和上层生产决策管理的咽喉要道,是实现加工过程自动化不可或缺的一环,是保证产品制造质量的的重要课题。囿于现代机械加工设备和方法的复杂性与多样性,尽管在该研究领域涌现了许多有价值的探索,实际生产领域中的应用依然屈指可数。究其原因,关键在于缺乏对加工过程的清晰理解,缺乏对被监控对象发生机理的透彻认识,缺乏对监控方法的系统构建。因此,本文针对机械加工领域的典型应用—精密平面磨削加工,以间接监测技术为手段,以声发射传感器为主,以加速度传感器、电流传感器和电压传感器为辅,围绕磨削烧伤及其相关科学问题展开研究,旨在厘清精密磨削加工过程中加工参数与传感器信号特征之间的关系,准确提取磨削烧伤等问题发生时对应信号的不变性特征值,解决磨削加工中磨削烧伤及其相关问题的特征提取问题,提出一套切实可行的精密磨削加工过程间接监测方法。本文主要做了以下工作：
第一章：介绍了本课题的研究背景和意义、本研究领域内与本课题密切相关的国内外技术研究现状、本研究领域存在的科学问题以及本文的研究内容等。
第二章：初步建立了单颗磨粒作用工件时切削应变能与声发射有效值之间的数学模型；探讨了加工参数与声发射等信号之间的关系,研究了激光激发热损伤前后声发射信号的特征,以求在后续开展磨削过程监测研究时,能够消除磨削参数变化对信号特征的影响,从而在后续磨削烧伤研究中,去伪存真,准确提取磨削烧伤的信号特征。
第三章：针对具体工程应用,以精密平面磨削为对象,开展了磨削烧伤的不变性特征提取方法研究。将声发射传感器、电流传感器、电压传感器和加速度传感器应用到磨削加工过程的监测之中,提出了基于声发射“频谱矩心”的磨削烧伤不变性特征表征方法。
第四章：结合离散小波分析方法,提取出声发射信号5级分解系数的有效值作为磨削烧伤的特征值之一。同时,首次将希尔伯特—黄变换应用到声发射信号的磨削烧伤预测中,提出了以IMF平均能量值占比作为衡量IMF分量与磨削烧伤相关性大小的标准,提取了基于IMF边际谱的磨削烧伤不变性特征。
第五章：提出了以经验模态分解作为时域滤波器的信号去噪方法,获得了更加准确的砂轮—工件初始接触特征；提出了基于离散小波分解的砂轮磨损特征值提取方法。
第六章：开发了砂轮—工件初始接触检测系统和基于支持向量机的磨削加工智能监测系统,实现了砂轮—工件初始接触检测、磨削烧伤识别和砂轮磨损判断的自动化。
Process monitoring system is the bridge between equipments and management roles. It could bring considerable benefits such as automation and intelligence into modern manu-facturing process. However, due to the complexity and diversity of modern manufacturing equipments, applications of process monitoring system in real industries are rarely seen. The major reason of such phenomena is the lack of interpretation and understanding of manufacturing process, and the short of process monitoring methods. To alleviate such kind of problems, this thesis focuses on grinding burn and its interrelated scientific problems, to get to the bottom of the relationship between sensor features and grinding parameters, to extract signal features closely related with grinding burn and its interrelated problems, and to build a feasible indirect monitoring system for precision grinding. Several efforts have been achieved concentrating around these specific themes and list as follows:
In Chapterl, scientific backgrounds and state-of-art concerning this field are compre-hensively elaborated. The structure and contents of this thesis are depicted at the end of this chapter.
Chapter2constructs mathematical models for AE RMS and cutting strain energy under single-grit-workpiece condition. Relations between grinding parameters and AE sensor are also studied both in theory and experiment. In order to make a thorough inquiry into thermal damage of metallic materials, AE features in laser-induced burn are extracted by virtue of preliminary time domain and frequency domain methods.
In Chapter3, focus is shifted from laser-induced burn to practical grinding. Grinding burn features, i.e., spectral centroid of PSD and maximum power, are extracted, by means of AE sensor, current transducers, voltage transducers and accelerator.
In order to go deep into grinding burn feature extraction methods, Chapter4keeps on studying issues raised in Chapter3. Discrete Wavelet Tranform(DWT) and Hilbert-Huang Transform(HHT) are utilized in grinding burn feature extraction. Results indicate that RMS value of the first5details yielded from DWT and marginal spectra of HHT can successfully reflect the occurrence of grinding burn.
Chapter5focuses on interrelated problems of grinding burn, e.g., grinding wheel wear and wheel-workpiece initial contact detection. Wheel-workpiece contact detection method based on Empirical Mode Decomposition is proposed in this chapter. Solutions of wheel wear detection and prediction are also given in Chapters.
A grinding process monitoring system is constructed in Chapter6, based on Support Vector Machine(SVM) and features extracted in previous chapters.
Conclusions and prospects are briefly depicted at the end of this thesis.
第一章：介绍了本课题的研究背景和意义、本研究领域内与本课题密切相关的国内外技术研究现状、本研究领域存在的科学问题以及本文的研究内容等。
第二章：初步建立了单颗磨粒作用工件时切削应变能与声发射有效值之间的数学模型；探讨了加工参数与声发射等信号之间的关系,研究了激光激发热损伤前后声发射信号的特征,以求在后续开展磨削过程监测研究时,能够消除磨削参数变化对信号特征的影响,从而在后续磨削烧伤研究中,去伪存真,准确提取磨削烧伤的信号特征。
第三章：针对具体工程应用,以精密平面磨削为对象,开展了磨削烧伤的不变性特征提取方法研究。将声发射传感器、电流传感器、电压传感器和加速度传感器应用到磨削加工过程的监测之中,提出了基于声发射“频谱矩心”的磨削烧伤不变性特征表征方法。
第四章：结合离散小波分析方法,提取出声发射信号5级分解系数的有效值作为磨削烧伤的特征值之一。同时,首次将希尔伯特—黄变换应用到声发射信号的磨削烧伤预测中,提出了以IMF平均能量值占比作为衡量IMF分量与磨削烧伤相关性大小的标准,提取了基于IMF边际谱的磨削烧伤不变性特征。
第五章：提出了以经验模态分解作为时域滤波器的信号去噪方法,获得了更加准确的砂轮—工件初始接触特征；提出了基于离散小波分解的砂轮磨损特征值提取方法。
第六章：开发了砂轮—工件初始接触检测系统和基于支持向量机的磨削加工智能监测系统,实现了砂轮—工件初始接触检测、磨削烧伤识别和砂轮磨损判断的自动化。
Process monitoring system is the bridge between equipments and management roles. It could bring considerable benefits such as automation and intelligence into modern manu-facturing process. However, due to the complexity and diversity of modern manufacturing equipments, applications of process monitoring system in real industries are rarely seen. The major reason of such phenomena is the lack of interpretation and understanding of manufacturing process, and the short of process monitoring methods. To alleviate such kind of problems, this thesis focuses on grinding burn and its interrelated scientific problems, to get to the bottom of the relationship between sensor features and grinding parameters, to extract signal features closely related with grinding burn and its interrelated problems, and to build a feasible indirect monitoring system for precision grinding. Several efforts have been achieved concentrating around these specific themes and list as follows:
In Chapterl, scientific backgrounds and state-of-art concerning this field are compre-hensively elaborated. The structure and contents of this thesis are depicted at the end of this chapter.
Chapter2constructs mathematical models for AE RMS and cutting strain energy under single-grit-workpiece condition. Relations between grinding parameters and AE sensor are also studied both in theory and experiment. In order to make a thorough inquiry into thermal damage of metallic materials, AE features in laser-induced burn are extracted by virtue of preliminary time domain and frequency domain methods.
In Chapter3, focus is shifted from laser-induced burn to practical grinding. Grinding burn features, i.e., spectral centroid of PSD and maximum power, are extracted, by means of AE sensor, current transducers, voltage transducers and accelerator.
In order to go deep into grinding burn feature extraction methods, Chapter4keeps on studying issues raised in Chapter3. Discrete Wavelet Tranform(DWT) and Hilbert-Huang Transform(HHT) are utilized in grinding burn feature extraction. Results indicate that RMS value of the first5details yielded from DWT and marginal spectra of HHT can successfully reflect the occurrence of grinding burn.
Chapter5focuses on interrelated problems of grinding burn, e.g., grinding wheel wear and wheel-workpiece initial contact detection. Wheel-workpiece contact detection method based on Empirical Mode Decomposition is proposed in this chapter. Solutions of wheel wear detection and prediction are also given in Chapters.
A grinding process monitoring system is constructed in Chapter6, based on Support Vector Machine(SVM) and features extracted in previous chapters.
Conclusions and prospects are briefly depicted at the end of this thesis.
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