Surface quality monitoring in abrasive water jet machining of Ti6Al4V-CFRP stacks through wavelet packet analysis of acoustic emission signals
Surface quality monitoring in abrasive water jet machining of Ti6Al4V-CFRP stacks through wavelet packet analysis of acoustic emission signals
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DOI：
10.1007/s00170-019-04177-0
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发表时间：
2019-10-01
影响因子：
3.4
通讯作者：
Ramulu, M.
中科院分区：
文献类型：
--
作者：
Pahuja, Rishi;Ramulu, M.
为了满足预定的几何公差和功能要求，常常需要对航空复合材料结构进行修边和钻孔等加工。磨料水射流(AWJ)是高速加工难加工材料的主要候选技术。AWJ过程的性能对在线故障和非最优过程参数非常敏感，需要有效的在线过程控制技术。本研究采用声发射(AE)信号对叠层钛-碳纤维复合材料的AWJ加工进行监测。由于声发射信号的非平稳特性，本文重点研究了基于精度驱动的时频域同步预测方法。利用小波包变换对声发射信号进行分析，提出了一种识别和表征声发射信号的算法。使用了35种不同的母小波和高达10的分解级别。当识别的信号特征与工艺参数和开口壁质量(表面粗糙度)有很强的相关性时，小波参数(母小波和分解)被认为是最优的。Coiflets和Symlet被确定为最优小波，能量-熵系数作为小波包的限定特征，得到R-2>；90%。进行了一项对比研究，以验证所提出的算法是否符合标准的时间域分析方法。该算法的最大R-2和变异系数(RMSD)分别为88.6%和12.5%，而WPT算法的R2和CV(RMSD)分别为97.12%和6%。提出了一种基于识别出的信号特征对过程质量进行监测和过程参数控制的有效算法。
Machining such as trimming and drilling of aerospace composite structures is often required to meet the intended geometric tolerances and functional requirements. Abrasive water jet (AWJ) is a primary candidate for high speed machining of difficult-to-cut materials. The AWJ process performance is sensitive to the online faults and non-optimal process parameters, necessitating efficient techniques for online process control. In this study, acoustic emission (AE) signals are used to monitor AWJ machining of stacked titanium-CFRP. Owing to the non-stationary nature of the AE signals, this work is focused on the precision-driven predictive approach in simultaneous time-frequency domain. The AE signals were analyzed using wavelet packet transform (WPT), and an algorithm was proposed to identify and characterize these signals. Thirty-five different mother wavelets and decomposition levels up to 10 were used. The wavelet parameters (mother wavelet and decomposition) were deemed optimal when the identified signal characteristics could strongly correlate with the process parameters and kerf wall quality (surface roughness). Coiflets and Symlets were identified as the optimal wavelets with energy-entropy coefficient as the qualifying characteristic of the wavelet packet resulting in R-2 > 90%. A comparative study was conducted to qualify the proposed algorithm against standard time domain analysis measures. The maximum R-2 and CV (RMSD)-coefficient of variation of root mean square deviation for time domain was observed as 88.6% and 12.5% respectively as opposed to R-2 = 97.12% and CV (RMSD)= 6% for the proposed WPT algorithm. Overall, an efficient algorithm was proposed in monitoring the process quality and controlling the process parameters based on the identified signal signatures.