Issue 70

M.Verezhak et alii, Frattura ed Integrità Strutturale, 70 (2024) 121-132; DOI: 10.3221/IGF-ESIS.70.07

ρ , kg/m 3

E, GPa

ν

4424 0.324 Table 3: Physical constants of titanium alloy Ti-6Al-4V 113

Numerical calculation for verification of the constructed mathematical model of residual stress generation process and finite element model are shown in Fig. 2. An area of 11x11 mm with a 1x1 mm square laser beam cross-section with a power density of 10 GW/cm 2 was treated. The energy of the pulse was 1 J . For the considered energy parameters of exposure, the duration of the pressure pulse was 60 ns and the peak pressure value was 5.36 GPa . No movement was allowed on the surface opposite the treatment area. The sides of the plate are free of load. The mesh is tetragonal, the number of used elements is 149590 , the element size in the zone of pressure application did not exceed 0.2 mm , a coarser mesh with a maximum finite element size of 2 mm was formed at a distance from the load setting zone. The convergence of the solution was checked by mesh refinement.

Figure 2: Numerical and finite-element model of alloy Ti-6Al-4V.

In order to verify the proposed model, the residual stress profiles obtained numerically were compared with experimental data. The residual stress profiles after LSP  x and  y , determined experimentally and numerically, are shown in Fig. 3 . The difference in minimum value of residual stress between experimental results and numerical results given in Fig. 3 is nearly 16%. This minor disagreement can be explained by possible measurement inaccuracies of the incremental hole drilling method due to its incremental and destructive character. A specific feature of the hole drilling method is the averaging of the obtained data over the drilled area. Therefore, it can affect the ability of it to register properly the above-described effect.

Figure 3: Residual stress profiles after LSP

The availability of a verified model significantly expands the possibilities of building a database sufficient for training a neural network and allows to significantly reduce the number of need experiments.

124