The Dataset of Quantifying Alignment Deviations for Uniaxial Material Mechanical Testing via Automated Machine Learning

The dataset consists of 4 alignment deviations of the uniaxial testing machine as well as 12 strain measurement points on cruciform specimens. A deep learning model is trained on the dataset to quantify 4 alignment deviations using 12 strain values on a thin plate specimen. The design of experiments includes Optimal Latin Hypercube, numerical modelling of Finite Element Methods. Using the Optimal Latin Hypercube, 12496 distinct groups of DOE simulation tests are constructed. Under the boundary conditions of 4 distinct deviations, 12 strain values at the required location on the cruciform specimen are obtained using Python scripts.

The nine CSV files correspond to the nine analysis steps. The only difference among the nine analysis steps is the pretension force acting on RP1. Each CSV file contains 24 columns of data, and the corresponding contents of each column of data are as follows:

Columns 1-6 are the freedoms of RP1 reference point, which are U1, U2, U3, ur1, UR2 and UR3 respectively;

Columns 7-12 are the freedoms of RP2 reference points, which are U1, U2, U3, ur1, UR2 and UR3 respectively;

Columns 13-24 are the strain values of the last 12 strain measurements of the thin plate rectangular specimen。