【摘要】：排屑鉆與普通鉆削加工相比,可提高鉆削加工質量,顯著改善孔加工表面粗糙度質量。但排屑鉆加工過程和普通鉆削一樣都處于半封閉或者封閉環(huán)境,孔加工表面粗糙度也難以檢測和分析。本文擬結合鉆削工藝參數和監(jiān)測信號特征,開展排屑鉆孔加工表面粗糙度預測研究。所開展的主要工作包括監(jiān)測平臺搭建、信號消噪處理、工藝參數和監(jiān)測信號特征對粗糙度的影響規(guī)律、預測模型的建立與驗證等方面。(1)排屑鉆監(jiān)控平臺搭建與數據采集。搭建排屑鉆監(jiān)控平臺,采集排屑鉆加工過程中的振動信號、聲發(fā)射信號,以及所加工孔壁粗糙度值,并采用最小二乘擬合的方法對信號進行趨勢項處理。(2)信號消噪處理。針對在鉆削加工噪聲背景下振動信號特征識別和提取困難的問題,提出了一種小波包分頻譜減去噪方法。根據鉆削信號在時頻域特點,首先將鉆削前機床空轉信號視為監(jiān)測信號的“加性噪聲”;然后,采用小波包分解將“加性噪聲”和監(jiān)測信號進行分頻處理,確定各頻帶幀數;最后,對各個子頻帶內“加性噪聲”的相應頻帶進行譜減處理,再重構鉆削振動信號。(3)工藝參數和監(jiān)測信號特征對粗糙度的影響規(guī)律。依據所采集的實驗數據,首先分析了不同工藝參數對監(jiān)測信號特征以及孔壁粗糙度的影響規(guī)律。然后通過方差分析的方法研究了不同工藝參數對監(jiān)測信號特征和表面粗糙度影響的顯著性。最后,分析了監(jiān)測信號特征與孔壁表面粗糙度的對應關系。(4)粗糙度預測模型建立與驗證。首先確定了神經網絡的輸入層與輸出層節(jié)點數,然后針對BP神經網絡隱含層節(jié)點數無法確定的問題,采用動態(tài)調節(jié)隱含層節(jié)點數的方法,對比不同結構預測值的準確度,確定最優(yōu)網絡結構。最后,通過對試驗樣本進行仿真分析,對粗糙度預測模型的有效性進行驗證。理論分析和實驗結果表明:采用本文所建立的粗糙度預測模型,能夠有效預測排屑鉆表面粗糙度。同時該方法可有效克服傳統(tǒng)粗糙度檢測采用人工抽檢所導致的漏檢、檢測效率不高等缺點,為實現排屑鉆粗糙度預測提供了新的方法和理論基礎。
[Abstract]:Compared with common drilling, chip removal drill can improve the quality of drilling and improve the surface roughness of hole machining. However, the process of chip removal drilling is in a semi-closed or closed environment, and the surface roughness of hole machining is also difficult to detect and analyze. In this paper, combining with the parameters of drilling process and the characteristics of monitoring signals, the prediction of surface roughness of chip removal drilling is carried out. The main work includes the construction of monitoring platform, signal de-noising processing, the influence of process parameters and monitoring signal characteristics on roughness, the establishment and verification of prediction model and so on. (1) the construction of monitoring platform for chip removal drill and data acquisition. The monitoring platform of chip removal drill is set up to collect vibration signal, acoustic emission signal and the roughness value of the hole wall in the process of chip removal drilling. The least square fitting method is used to process the trend term of the signal. (2) the signal is de-noised. In order to solve the problem of difficult recognition and extraction of vibration signals in the background of drilling noise, a wavelet packet spectrum division subtract method is proposed. According to the characteristics of drilling signal in time and frequency domain, the machine tool idle signal before drilling is regarded as the "additive noise" of the monitoring signal, and then the "additive noise" and the monitoring signal are processed by using wavelet packet decomposition to determine the frame number of each frequency band. Finally, the corresponding frequency band of "additive noise" in each sub-band is subtracted and then the drilling vibration signal is reconstructed. (3) the influence of technological parameters and monitoring signal characteristics on roughness. Based on the experimental data collected, the influence of different process parameters on the characteristics of the monitoring signal and the roughness of the hole wall was analyzed. Then, the effects of different process parameters on the characteristics of monitoring signals and surface roughness were studied by ANOVA. Finally, the relationship between the characteristics of the monitoring signal and the surface roughness of the hole wall is analyzed. (4) the prediction model of roughness is established and verified. Firstly, the number of nodes in the input layer and the output layer of the neural network is determined. Then, aiming at the problem that the number of hidden layer nodes in the BP neural network cannot be determined, the method of dynamically adjusting the number of nodes in the hidden layer is adopted to compare the accuracy of the predicted values of different structures. The optimal network structure is determined. Finally, the validity of the roughness prediction model is verified by the simulation analysis of the test samples. The theoretical analysis and experimental results show that the roughness prediction model established in this paper can effectively predict the surface roughness of chip removal drills. At the same time, this method can effectively overcome the shortcomings of traditional roughness detection caused by manual sampling, such as low detection efficiency and so on. It provides a new method and theoretical basis for the prediction of chip removal drill roughness.
【學位授予單位】：湘潭大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TG52

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