# A Novel Low-Cost DIC-Based Residual Stress Measurement Device
## 3 Experimental Procedure
### DIC-Based Measurements  
Following the strain gauge calibration test, the DIC hole-drilling approach with the measurement device was implemented. This involved drilling two holes to the tensile- and compressive-stress-loaded edges of the control sample, near points T and C, respectively, as shown in Figure 5. The testing procedure was as previously described, where bespoke clamps were produced to support the sample, the sample was aligned using the live view from the camera and, after collecting the zero-point and offset measurements, the g-code script was edited to incrementally drill through the sample. To achieve comparability to the strain gauge measurements, similar milling parameters were used. This included a hole diameter of 2 mm with a depth of 1 mm at 10,000 RPM, with a feed rate of 0.1 m per minute and increments of 0.05 mm of the drilling depth. Assessment of the drilling depth was performed, and it was determined that this could reliably be achieved within \(+/-5\)\(\upmu\)m. This precision ensured that the approximation of equal depth increments associated with the hole-drilling procedure could be reliably used. The DIC parameters were optimised based on the requirements of the experiment and the lab environment. It was observed that the best results were achieved when the environmental lights were turned off (to compensate for the flickering of the LED lamps) [37; 60]. Figure 5: 4-point bending experimental setup with strain gauge positions (Point C and T), speckle pattern and DIC hole-drilling locations (Point C DIC and T DIC) highlighted in red. Furthermore, the images were pre-processed in accordance with good practice DIC corrections [61]. This included rigid-body compensation, which aligned the image sequence and compensated for minor displacement errors due to actuation. Another technique used was image averaging, which increased the signal-to-noise ratio in the images [62], allowing for less noisy results. With the optimum parameters determined and the images pre-processed, the displacement data from the image sequence was extracted using the open-source DIC software, Ncorr [63]. Figure 7 shows the X and Y displacements in the last image of the sequences, where the maximum displacements were measured. To estimate the error arising from the size and distribution of DIC particles, the standard approach of determining the deviation from the expected hole-drilling relief curves was used [35]. The complete list of key variables used in the experiment is summarised in Table 5, and the strain fields derived from the displacement fields can be found in Appendix B. The images clearly illustrate the effect of strain relief due to the hole drilling, which can be separated from the background noise. However, a number of small circular artifacts with a ring of displacement at their edges were also observed within the displacement field. Figure 6: Cartesian stresses as a function of depth at (**a**) point T and (**b**) point C.  
A careful review of these locations revealed that this arose from aliasing of markers. Fortunately, the displacement change induced by this effect was equal and opposite on either side of the circle. Therefore, the averaging performed over the virtual strain gauges meant that this phenomenon had little to no effect on the strain measurements obtained. This suggests that despite the significant refinement performed to optimize the surface speckle pattern, further improvements are required in terms of the number/size of contrast points. Calibration of the correlation system was performed via both physical data collection and the generation of virtual data sets via image manipulation. In the first case, two data sets were collected. The first was based on repeated image collection without drilling (or movement), which was used to generate a baseline estimate of the error associated with image noise when no movement or strain change was physically induced.