Page 102 - MSAM-2-3

Materials Science in Additive Manufacturing Validation of a novel ML model for AM-PSP

A B

Figure 7. Main effects of the control parameters (A) and signal-to-noise ratio (B) indicate that AM-process induced microstructure greatly affects the
resulting machining behavior.

Figure 8. Mean and interaction effects of control factors on specific cutting energy.

Table 6. ANOVA results of the specific cutting energy consumed on the machining of Ti‑6Al‑4V
Variables DoF Sum of squares Adj SS Mean of square F P‑value
Materials 7 7554173 7057373 1008196 34.27 0.000***
DoC 2 159581 33089 16545 0.56 0.574
Feed 2 17631530 4758353 2379176 80.86 0.000***
Speed 2 479466 369950 184975 6.29 0.004**
Material and DoC 14 4500947 4514152 322439 10.96 0.000***
DoC and feed 4 73329 50848 12712 0.43 0.785
DoC and speed 4 15547 14093 3523 0.12 0.975
Feed and speed 4 10677 10677 2669 0.09 0.985
Residual 44 1294626 1294626 29423 \ \
Total 83 31719875 \ \ \ \
*P<0.05, **P<0.005, ***P<0.0005.
Abbreviations: Adj SS: Adjusted sum of squares; DoC: Depth of cut; DoF: Degree of freedom.

Volume 2 Issue 3 (2023) 10 https://doi.org/10.36922/msam.0999