Assessment of composite material damage based on a regression analysis model of streaming acoustic emission parameters

The aim of this study is to develop an acoustic emission (AE) monitoring methodology that is invariant to the mechanical impact influence. To achieve this objective, regression and statistical analysis algorithms were employed to identify nonlinear relationships between the degree of damage in composite specimens (j) and the values of streaming AE parameters. As the most informative AE parameters correlating with the actual condition of the monitored specimens, the quantile values at the p = 0.9 level of the distribution functions of the front energy ([Eφ]p = 0.9) and specific energy ([EN]p = 0.9) of AE pulses were selected. A key stage in the implementation of the proposed methodology involves partitioning the criterion plane ([EN]p = 0.9 – [Eφ]p = 0.9) into three characteristic segments (I, II, III). Based on the values of the streaming parameters and their weighted distribution across segments I, II, and III (WI, WII, WIII), a regression model was synthesized to assess the degree of damage in composite specimens using AE monitoring results. The maximum reduced error of the developed model was γ = 9.8%, while the mean relative error did not exceed 1.1%, regardless of the initial condition of the monitored specimens.