The Dataset of Quantifying Alignment Deviations for the In-plane Biaxial Test System

The dataset consists of 12 alignment deviations of an in-plane biaxial tensile testing machine as well as 56 strain measurement points on cruciform specimens. A deep learning model is trained on the dataset to quantify 12 alignment deviations using 56 strain values on a shape-optimized cruciform specimen. The design of experiments includes Optimal Latin Hypercube, numerical modelling of Finite Element Methods. Using the Optimal Latin Hypercube, 55000 distinct groups of DOE simulation tests are constructed. Under the boundary conditions of 12 distinct deviations, 56 strain values at the required location on the cruciform specimen are obtained using Python scripts.