Research Article
Coping with Complexity When Predicting Surface Roughness in Milling Processes: Hybrid Incremental Model with Optimal Parametrization
Table 3
Comparative study with conventional models.
| Theoretical model | SCEC model [14] | Taguchi model [14] | HIM + SA | Ra (μm) | Raκ (μm) | Prediction error (%) | Ra (μm) | (μm) | Prediction error (%) | Ra (μm) | Raη (μm) | Prediction error (%) | Ra (μm) | (μm) | Prediction error (%) |
| 0.160 | 0.182 | 13.750 | 0.111 | 0.115 | 3.604 | 0.249 | 0.269 | 8.032 | 0.341 | 0.341 | 0.015 | 0.206 | 0.273 | 32.524 | 0.153 | 0.171 | 11.765 | 0.195 | 0.159 | 18.462 | 0.157 | 0.156 | 0.414 | 0.222 | 0.364 | 63.964 | 0.197 | 0.207 | 5.076 | 0.219 | 0.221 | 0.913 | 0.285 | 0.286 | 0.312 | 0.136 | 0.182 | 33.824 | 0.095 | 0.105 | 10.526 | 0.222 | 0.257 | 15.766 | 0.472 | 0.473 | 0.199 | 0.194 | 0.273 | 40.722 | 0.158 | 0.155 | 1.899 | 0.136 | 0.122 | 10.294 | 0.257 | 0.256 | 0.261 | 0.205 | 0.364 | 77.561 | 0.190 | 0.184 | 3.158 | 0.168 | 0.170 | 1.190 | 0.576 | 0.578 | 0.366 | 0.126 | 0.182 | 44.444 | 0.081 | 0.083 | 2.469 | 0.111 | 0.123 | 10.811 | 0.363 | 0.363 | 0.019 | 0.168 | 0.273 | 62.500 | 0.136 | 0.136 | 0.044 | 0.158 | 0.126 | 20.253 | 0.260 | 0.260 | 0.150 | 0.203 | 0.364 | 79.310 | 0.171 | 0.173 | 1.170 | 0.171 | 0.166 | 2.924 | 0.730 | 0.732 | 0.260 |
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