Sheet Molding compounds are a state-of-the-art material system in lightweight design featuring a high design freedom but a relatively low mechanical performance. This may be enhanced by local reinforcements based on continuous fiber prepregs. The failure of such hybrids – as of fiber reinforced polymers in general - is a complex interaction of different microstructural mechanisms. In order to assign those mechanisms to the macroscopic material response, in-situ methods as acoustic emission can be applied. This allows for the detection of initiation and growth as well as for the localization of damage in mechanically loaded materials. Additionally, an identification of the acting damage mechanisms is potentially feasible.
In this study, mechanical material testing under bending loads of continuous and discontinuous fiber reinforced plastics was coupled with acoustic emission analysis. Results have shown that different failure mechanisms resulting from different reinforcement architectures can be distinguished due to a systematic analysis of the acoustic emission signals detected during the destructive testing. For that purpose, machine learning algorithms were trained to differentiate various failure mechanisms from experimentally captured acoustic emission signals. Additionally, the analysis of the damage behavior of the single constituents such as the continuous and the discontinuous fiber reinforced components offers the possibility to investigate damage of hybrid continuous-discontinuous Sheet Molding Compounds exposed to bending loads.
The research documented in this manuscript has been funded by the German Research Foundation (DFG) within the International Research Training Group “Integrated engineering of continuous-discontinuous long fiber reinforced polymer structures“ (GRK 2078). The support by the German Research Foundation (DFG) is gratefully acknowledged.