Designing optimized geometry and fiber orientation of composites for dynamic tension via artificial intelligence

Machine learning is quickly becoming an invaluable tool for examining large databases to find patterns and predictions based on similar parameters tested in previous studies. A natural application of this subset of artificial intelligence revolves around material data sets where tests are repeated for basic studies (e.g. tension, compression, bending, or shear) and extrapolating the data for tests not yet considered. Dynamic testing and validation with machine learning is examined here, as a significant time and cost is required for understanding larger-sized specimens (cm scale and larger) especially when the samples are composite materials in tension. This is due in part to the challenges involved in ensuring failure at the region of interests for an anisotropic material and minimizing the number of repeated tests. In this study, the data sets were generated artificially via finite element analysis with failure, and a machine learning code applied to infer the shape of the tensile test coupon knowing only the failure and stress field. Then the algorithm was run to generate a library of ideal coupon geometries based on ply angle and area location of failure within the test specimen. The result is a highly efficient prediction algorithm that can create the ideal specimen shape for tensile testing at speeds orders of magnitude faster than comparable finite element codes.