Surface quality monitoring in abrasive water jet machining of Ti6Al4V–CFRP stacks through wavelet packet analysis of acoustic emission signals

  • Rishi Pahuja
  • M. RamuluEmail author


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 R2 > 90%. A comparative study was conducted to qualify the proposed algorithm against standard time domain analysis measures. The maximum R2 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 R2 = 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.


Abrasive water jet Titanium CFRP Stacks Wavelet analysis Process monitoring 



Hydraulic pressure


Jet traverse speed


AE signal


Wavelet function


Scaling factor


Translation factor


Low-pass filter


High-pass filter


Sampling frequency


Decomposition level


Ti/CFRP configuration, mth AE channel


CFRP/Ti configuration, mth AE channel (m = 1 or 2)



This research was supported by the Boeing-Pennell Professorship funds. Authors sincerely acknowledge the support and encouragement of Dr. M. Hashish, Senior Technical Fellow at Flow International during the investigation.

Funding information

This research was supported by the Boeing-Pennell Professorship funds.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of WashingtonSeattleUSA

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