Table 1 RSM-based design of experiment with significant input parameters and corresponding output.
From: Machine learning based approach for surface roughness prediction in precision dental prototyping
Layer thickness | Exposure duration | Print angle | Infill density | Lift speed | Surface roughness |
|---|---|---|---|---|---|
(mm) | (sec) | (degree) | (%) | (mm/sec) | (Microns) |
X1 | X2 | X3 | X4 | X5 | Y |
0.065 | 4 | 67 | 40 | 3.5 | 0.465 |
0.065 | 4 | 67 | 80 | 3.5 | 0.412 |
0.05 | 5.5 | 90 | 60 | 5 | 0.293 |
0.08 | 5.5 | 90 | 60 | 2 | 0.519 |
0.08 | 5.5 | 90 | 100 | 5 | 0.462 |
0.08 | 2.5 | 90 | 100 | 2 | 0.473 |
0.065 | 4 | 67 | 80 | 3.5 | 0.416 |
0.065 | 4 | 67 | 80 | 3.5 | 0.414 |
0.065 | 4 | 90 | 80 | 3.5 | 0.385 |
0.08 | 2.5 | 45 | 60 | 2 | 0.611 |
0.05 | 2.5 | 45 | 60 | 5 | 0.371 |
0.05 | 2.5 | 90 | 100 | 5 | 0.26 |
0.05 | 5.5 | 45 | 60 | 2 | 0.346 |
0.055 | 4 | 67 | 80 | 3.5 | 0.326 |
0.05 | 5.5 | 90 | 100 | 2 | 0.239 |
0.065 | 4 | 67 | 80 | 6.5 | 0.425 |
0.065 | 4 | 22 | 80 | 3.5 | 0.465 |
0.065 | 4 | 67 | 100 | 3.5 | 0.384 |
0.065 | 4 | 67 | 80 | 3.5 | 0.427 |
0.065 | 4 | 67 | 80 | 3.5 | 0.419 |
0.065 | 4 | 67 | 80 | 1.5 | 0.418 |
0.05 | 5.5 | 45 | 100 | 5 | 0.291 |
0.065 | 2 | 67 | 80 | 3.5 | 0.411 |
0.08 | 2.5 | 90 | 60 | 5 | 0.551 |
0.05 | 2.5 | 90 | 60 | 2 | 0.3 |
0.08 | 2.5 | 45 | 100 | 5 | 0.525 |
0.08 | 5.5 | 45 | 60 | 5 | 0.605 |
0.065 | 4 | 67 | 80 | 3.5 | 0.431 |
0.095 | 3.5 | 67 | 80 | 3.5 | 0.649 |
0.08 | 5.5 | 45 | 100 | 2 | 0.5 |
0.065 | 6.5 | 67 | 80 | 3.5 | 0.395 |
0.05 | 2.5 | 45 | 100 | 2 | 0.297 |
