1 Introduction
2 Simulated Annealing-Based Fitting of CAD Models
3 Sensitivity Analysis
4 Results and Discussion
k | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
\(p_{k,0}\) | 25.0 | 20.0 | 2.0 | 3.0 | 2.0 |
\(S_{k,0}\) | −8.0E+4 | 3.0E+4 | 4.0E+4 | 0.0E+00 | 1.0E+4 |
\(\delta p_{k,0}\) | 0.56 | −0.45 | −0.22 | 0.15 | 0.16 |
\(\Delta p_{k,0}\) | 0.02 | −0.02 | −0.11 | 0.05 | 0.09 |
\( \overline{S}_{{k,j \in \left[ {0..9} \right]}}\) | 4.4E+4 | 2.9E+4 | 4.1E+4 | 6.0E+3 | 6.0E+3 |
\(\overline{\delta p}_{{k,j \in \left[ {0..9} \right]}}\) | 2.55 | 1.25 | 1.76 | 1.79 | 1.60 |
\( \overline{\Delta p}_{{k,j \in \left[ {0..9} \right]}} { }\) | 0.10 | 0.06 | 0.88 | 0.60 | 0.80 |
\(p_{k,D}\) | 35 | 30 | 5 | 6 | 5 |
\(p_{k,F}\) | 34.92 | 29.84 | 4.89 | 5.93 | 5.49 |
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the parameters \(p_{k}\) could be grouped according to their levels of sensitivity, so as to treat them in several batches for which the sensitivity is comparable;
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the way the SA algorithm changes the parameters values could be further optimized while considering the sensitivity analysis all along the process;
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the sensitivity analysis could be performed at a lower level, while decomposing the energy function and tracking which parts of the energy function are affected by the changes to save time when computing the overall energy.