## 3 Results
### Spt  
Recently, new analysis protocols have been demonstrated for extracting the uniaxial (tension or compression) stress-strain curves from the SPT measured load-displacement curves. [PERSON] et al. [33] utilized a FE library approach to predict the parameters of the Swift hardening law which were used to extract uniaxial stress-strain curves from SPT experimental data. [PERSON] and [PERSON] [15] used a neural network trained on FE simulations to extract 5 out of 12 parameters of the Gurson-Twegerard-Needleman model and their approach was referenced in [PERSON]'s work. The protocols employed for this research utilized an FE model (see Figure 4c) to connect the material's uniaxial stress-strain material response (provided as an input to the FE model) and the SPT load-displacement curve (obtained as an output from the FE model). The FE model was comprised of 700 elements which were biased towards the center of the sample as seen in Figure 4c. Through a dictionary approach, the uniaxial stress-strain response input to the FE model was systematically revised until the FE-predicted SPT load-displacement curve matched well with the measured SPT load-displacement curve. [PERSON] et al. [35] applied a similar approach to successfully determine the orientation of grains using electron backscatter diffraction images. [PERSON] et al. [36] also used a dictionary approach to reconstruct microstructure images by means of spatial statistics of a microstructure.  
The procedure described above has been applied in this work to recover uniaxial stress-strain responses of the samples from their SPT load-displacement measurements. [PERSON] and [PERSON] [37] investigated the use of a variety of strain hardening laws, including Swift law, for their applicability to describing stress-strain curves. In this work, approximately 2500 simulations were performed using Abaqus CAE for each sample thickness, with each simulation using a different set of plasticity parameters defined by the Swift law as  
\[\sigma=K(\varepsilon_{0}+\varepsilon_{P})^{n} \tag{14}\]  
where \(K\) is the strength coefficient, \(\varepsilon_{0}\) is the prior plastic strain value, \(\varepsilon_{P}\) is the true plastic strain, \(n\) is the strain hardening exponent, and \(\sigma\) is the true stress. In the formalism of Swift law in Equation (14), \(K\), \(\varepsilon_{0}\), and \(n\), serve as the plasticity parameters. The FE-predicted load-displacement curves of a large library of simulations, that covers a broad range of possible plasticity parameters, were compared to the measured load-displacement curve using an error measure defined as the mean absolute differences between the two curves computed at uniform intervals on the displacement axis. The best three curves (with the lowest errors with respect to the experimental curve) are shown in Figure 8a along with a direct measurement for Sample number 4. These three curves were essentially indistinguishable and the use of the three best simulation curves represents the negligible variation between the best-matching simulation curves.  
An average of the \(K\), \(\varepsilon_{0}\), and \(n\) values for the best-fit curves was taken to produce the estimated uniaxial stress-strain response (see Figure 8b) from the SPT load-displacement measurement. Clearly, the stress-plastic strain curves recovered from the SPT measurements were in very good agreement with the one measured in standard UTT protocols. Similar agreements were obtained for (no less than 3) SPTs on each of the eight samples tested in this work.