Analysis of Acoustic Emission Signal to Characterization the Damage Mechanism During Drilling of Al-5%SiC Metal Matrix Composite
Analysis of Acoustic Emission Signal to Characterization the Damage Mechanism During Drilling of Al-5%SiC Metal Matrix Composite
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DOI：
10.1007/s12633-020-00426-0
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发表时间：
2020-03
期刊：
影响因子：
3.4
通讯作者：
K. Thirukkumaran;C. Mukhopadhyay
中科院分区：
文献类型：
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作者：
K. Thirukkumaran;C. Mukhopadhyay
刀具磨损导致加工过程中表面质量差、工件尺寸误差以及意外的刀具突然失效。因此，检测刀具磨损对提高工件质量和延长刀具寿命至关重要。为此，采用声发射技术研究了不同几何形状刀具在Al-5%SiC金属基复合材料（MMC）钻削过程中的磨损特性。分别在600-1200 rpm和0.07-0.17 mm/rev范围内的不同切削速度和进给速度下进行了干式钻孔实验。钻削试验采用不同刀尖角度（90°、118°和135°）的高强度钢（HSS）刀具。对采集到的声发射信号进行了时域、频域和时频域分析。声发射计数，能量，峰值振幅，均方根电压（AERMS）与切削参数（主轴转速和进给速度）。对声发射信号进行快速傅立叶变换（FFT）和连续小波变换（CWT），可以识别出声发射信号的主要峰值频率和时频谱。研究了不同几何形状刀具磨损与声发射参数（小波系数）的关系。小波包变换（WPT）被用来提取信号中存在的各种AE源的特征。WPT结果可以区分不同的频率成分和相关的损伤机制参与钻井过程。还使用扫描电子显微镜（SEM）表征了切削工具和钻孔工件中的损伤。
Tool wear drives poor surface quality, dimensional error in the workpiece, and unexpected sudden tool failure during the machining process. Thus, detection of tool wear is essential to increase the workpiece quality and extend tool life. In this view, acoustic emission technique (AET) was employed to investigate the tool wear characteristics for different tool geometries during drilling of Al-5%SiC metal matrix composite (MMC). The dry drilling experiments were performed for different cutting speeds and feed rates ranging from 600-1200 rpm and 0.07–0.17 mm/rev, respectively. The high strength steel (HSS) tool with different point angles (90°, 118°, and 135°) was used for the drilling tests. The captured acoustic emission (AE) signals were analyzed in time domain, frequency domain, and time-frequency domain. AE count, energy, peak amplitude, and root mean square voltage (AERMS) were correlated with cutting parameters (spindle speed and feed rate). The Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) of AE signals could identify the predominant peak frequency and time-frequency spectrum. The relationship between tool wear and AE parameter (wavelet coefficient) for different tool geometries was studied. Wavelet packet transform (WPT) was utilized to extract various AE source features present in the signals. The WPT results could distinguish different frequency components and the related damage mechanisms involved in the drilling process. The damage in the cutting tool and drilled workpiece were also characterized using a scanning electron microscope (SEM).