基于HMM-SVM的磁流变自抑振智能镗杆颤振在线预报理论和方法研究
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摘要
由于镗杆的长径比通常比较大、镗削系统结构刚度较低,镗削加工过程极易发生颤振,这给精密孔的加工带来了极大的困难,往往导致精密孔加工精度低、表面质量差、加工效率低等问题。为了对镗削颤振进行快速有效的抑制,确保精密孔的加工质量,本文结合浙江省自然科学基金项目(Y104462)和国家自然科学基金项目(50405036),利用理论推导、数值仿真分析和实验研究等手段,对磁流变自抑振智能镗杆镗削颤振的在线识别预报理论和方法展开了深入研究。
第1章,阐述了课题的研究背景与意义,详细介绍了金属切削颤振及其在线识别预报技术的研究现状和发展趋势,提出了本文的主要研究内容。
第2章,对金属切削颤振产生机理进行了深入的研究,建立了再生型颤振的系统动力学模型,并对机床金属切削系统的稳定性进行了分析,推导出了金属切削系统稳定性极限图；在此基础上,对变速切削法抑振机理进行了研究,揭示其颤振抑制的本质；研究了系统结构刚度和阻尼对系统稳定性的影响,分析了变刚度切削进行颤振抑制的可行性,最后设计了一种基于磁流变液材料的自抑振智能镗杆,并对其抑振原理进行了阐述。
第3章,提出了将EMD分解和HHT变换方法引入镗削颤振征兆特征提取过程中,并对其基本理论和实现过程进行深入的研究。首先,利用EMD对镗削振动信号进行分解,并通过将分解所得的IMF分量的能量变化情况与颤振的“能量频移”现象进行对比,验证了EMD分解在镗削颤振征兆特征提取是可行的。其次,再对IMF分量进行Hilbert变换,得到振动信号的时频图,最终快速有效的实现镗削颤振征兆特征的提取。
第4章,在研究多特征信息融合技术的基础上,提出将FastICA引入到镗削颤振识别预报中,并建立了基于IMF虚拟通道的FastICA镗削颤振征兆信号分离系统,将前一章中所提取的颤振征兆特征进一步融合,从而分离出更能反映颤振征兆的特征信号,经数值仿真分析表明,该方法能有效提高镗削颤振的征兆特征精准度,为后续的镗削状态识别系统打下基础。
第5章,在对HMM、SVM两个模式识别模型的基本算法、优缺点和适用范围进行深入分析的基础上,结合镗削颤振形成过程中过渡阶段较短且容易与相邻的正常镗削、颤振阶段混淆的特点,提出了一种基于HMM-SVM混合模型的镗削颤振识别系统。在该系统中,HMM作为第一层进行初步筛选,得到镗削过程最为接近的两种可能状态,然后再利用SVM分类器进一步分类识别,得到最终的识别结果,这样既发挥了HMM模型较强的时间序列建模能力,又充分利用了SVM较强的二类分类能力。
第6章,为了对本文提出的颤振预报理论和方法的正确性进行验证,建立了智能镗杆切削颤振识别预报系统的软硬件实验平台,并开展实验研究。首先,利用EMD对镗削振动信号进行分解,再利用FastICA分离出颤振爆发征兆信号,然后利用HHT进行Hilbert变换,得到镗削颤振征兆特征向量；再次,利用本文提出的HMM-SVM混合模型,进行镗削状态识别预报和颤振抑制实验,并通过实验方法对本文提出的镗削颤振在线预报控制方法的有效性进行验证。
第7章,对本文的主要研究工作进行总结与展望。
Due to the low structural stiffness of boring bar, chatter occurs frequently during precision hole boring process, it results in low quality of finished surface and even damages the cutting tool. To solve this problem, the theory and method of online chatter prediction for MR intelligent boring bar was studied systematically in this dissertatioa The research work is supported by the Natural Science Foundation of Zhejiang Province (Grant No. Y104462) and the National Natural Science Foundation of China (Grant No.50405036).
In Chapter1, the background and significance of the research were introduced, the research status and development trend of the technology of online chatter prediction were described in detail. Then, the main research contents of this dissertation were put forward.
In Chapter2, firstly, the generation mechanism of cutting chatter was studied, the stability of cutting machine tools was analyzed, and the Stability Lobe Diagram of cutting machine tools was derived. Secondly, in order to find out appropriate methods for the chatter suppression, the effect of structural stiffness and damping on system stability was analyzed. Then a magnetorheological fluid based intelligent boring bar was proposed.
In chapter3, firstly, the Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) based method for chatter feature extraction was proposed, and the basic theories of EMD and HHT were investigated. Then, the vibration signal of boring bar was decomposed by EMD and then transformed by HHT. Finally, the features of chatter symptom were extracted by analyzing the Hilbert spectrum of each Intrinsic Mode Function (IMF), which can provide sufficient guarantee for follow-up chatter recognition and prediction.
In chapter4, based on the study of feature fusion technology, FastICA theory was introduced into boring chatter recognition and prediction field, then the IMF virtual channels and FastICA based chatter symptom separation system was established. The signal-noised separation was accomplished by ICA, and the chatter symptom was gained. The simulation results showed that the EMD-ICA based vibration signal processing could separate the chatter symptom signal rapidly and effectively.
In chapter5, based on the study of Hidden Markov Model (HMM) and Support Vector Machine (SVM), an HMM-SVM based method for boring chatter recognition method was proposed, and the chatter identification system was established. In the system, HMM was used for the boring status preliminary selection to get two possible boring status, and then SVM was used for the deeper classification, and gain the final recognition results.
In chapter6, to verify the proposed theory and method, the experimental setup of MR fluid intelligent boring bar was built. Firstly, the boring vibration signal was descomposed by EMD. Then, IMFs were separated by FastICA and gained the chatter symptom signal. Finally, the boring statuses were classified by the HMM-SVM chatter recognition system. Series of experiments were carried out, and the results showed that the method could recognize boring chatter accurately and rapidly, and the intelligent boring bar could suppress chatter efficiently.
In Chapter7, the main research work of this dissertation was summarized and prospected.
第1章,阐述了课题的研究背景与意义,详细介绍了金属切削颤振及其在线识别预报技术的研究现状和发展趋势,提出了本文的主要研究内容。
第2章,对金属切削颤振产生机理进行了深入的研究,建立了再生型颤振的系统动力学模型,并对机床金属切削系统的稳定性进行了分析,推导出了金属切削系统稳定性极限图；在此基础上,对变速切削法抑振机理进行了研究,揭示其颤振抑制的本质；研究了系统结构刚度和阻尼对系统稳定性的影响,分析了变刚度切削进行颤振抑制的可行性,最后设计了一种基于磁流变液材料的自抑振智能镗杆,并对其抑振原理进行了阐述。
第3章,提出了将EMD分解和HHT变换方法引入镗削颤振征兆特征提取过程中,并对其基本理论和实现过程进行深入的研究。首先,利用EMD对镗削振动信号进行分解,并通过将分解所得的IMF分量的能量变化情况与颤振的“能量频移”现象进行对比,验证了EMD分解在镗削颤振征兆特征提取是可行的。其次,再对IMF分量进行Hilbert变换,得到振动信号的时频图,最终快速有效的实现镗削颤振征兆特征的提取。
第4章,在研究多特征信息融合技术的基础上,提出将FastICA引入到镗削颤振识别预报中,并建立了基于IMF虚拟通道的FastICA镗削颤振征兆信号分离系统,将前一章中所提取的颤振征兆特征进一步融合,从而分离出更能反映颤振征兆的特征信号,经数值仿真分析表明,该方法能有效提高镗削颤振的征兆特征精准度,为后续的镗削状态识别系统打下基础。
第5章,在对HMM、SVM两个模式识别模型的基本算法、优缺点和适用范围进行深入分析的基础上,结合镗削颤振形成过程中过渡阶段较短且容易与相邻的正常镗削、颤振阶段混淆的特点,提出了一种基于HMM-SVM混合模型的镗削颤振识别系统。在该系统中,HMM作为第一层进行初步筛选,得到镗削过程最为接近的两种可能状态,然后再利用SVM分类器进一步分类识别,得到最终的识别结果,这样既发挥了HMM模型较强的时间序列建模能力,又充分利用了SVM较强的二类分类能力。
第6章,为了对本文提出的颤振预报理论和方法的正确性进行验证,建立了智能镗杆切削颤振识别预报系统的软硬件实验平台,并开展实验研究。首先,利用EMD对镗削振动信号进行分解,再利用FastICA分离出颤振爆发征兆信号,然后利用HHT进行Hilbert变换,得到镗削颤振征兆特征向量；再次,利用本文提出的HMM-SVM混合模型,进行镗削状态识别预报和颤振抑制实验,并通过实验方法对本文提出的镗削颤振在线预报控制方法的有效性进行验证。
第7章,对本文的主要研究工作进行总结与展望。
Due to the low structural stiffness of boring bar, chatter occurs frequently during precision hole boring process, it results in low quality of finished surface and even damages the cutting tool. To solve this problem, the theory and method of online chatter prediction for MR intelligent boring bar was studied systematically in this dissertatioa The research work is supported by the Natural Science Foundation of Zhejiang Province (Grant No. Y104462) and the National Natural Science Foundation of China (Grant No.50405036).
In Chapter1, the background and significance of the research were introduced, the research status and development trend of the technology of online chatter prediction were described in detail. Then, the main research contents of this dissertation were put forward.
In Chapter2, firstly, the generation mechanism of cutting chatter was studied, the stability of cutting machine tools was analyzed, and the Stability Lobe Diagram of cutting machine tools was derived. Secondly, in order to find out appropriate methods for the chatter suppression, the effect of structural stiffness and damping on system stability was analyzed. Then a magnetorheological fluid based intelligent boring bar was proposed.
In chapter3, firstly, the Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) based method for chatter feature extraction was proposed, and the basic theories of EMD and HHT were investigated. Then, the vibration signal of boring bar was decomposed by EMD and then transformed by HHT. Finally, the features of chatter symptom were extracted by analyzing the Hilbert spectrum of each Intrinsic Mode Function (IMF), which can provide sufficient guarantee for follow-up chatter recognition and prediction.
In chapter4, based on the study of feature fusion technology, FastICA theory was introduced into boring chatter recognition and prediction field, then the IMF virtual channels and FastICA based chatter symptom separation system was established. The signal-noised separation was accomplished by ICA, and the chatter symptom was gained. The simulation results showed that the EMD-ICA based vibration signal processing could separate the chatter symptom signal rapidly and effectively.
In chapter5, based on the study of Hidden Markov Model (HMM) and Support Vector Machine (SVM), an HMM-SVM based method for boring chatter recognition method was proposed, and the chatter identification system was established. In the system, HMM was used for the boring status preliminary selection to get two possible boring status, and then SVM was used for the deeper classification, and gain the final recognition results.
In chapter6, to verify the proposed theory and method, the experimental setup of MR fluid intelligent boring bar was built. Firstly, the boring vibration signal was descomposed by EMD. Then, IMFs were separated by FastICA and gained the chatter symptom signal. Finally, the boring statuses were classified by the HMM-SVM chatter recognition system. Series of experiments were carried out, and the results showed that the method could recognize boring chatter accurately and rapidly, and the intelligent boring bar could suppress chatter efficiently.
In Chapter7, the main research work of this dissertation was summarized and prospected.
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