Reverse Engineering of Geometric Surfaces Using Tabu Search Optimization Technique
Bibliographic record
Abstract
Creating unavailable geometric models from existing parts plays an important role in the process of reverse engineering, for which the accuracy and fitting time of the created models are important factors. This paper proposes the use of Tabu Search (TS) technique in the optimal fitting of NURBS (Non Uniform Rational B-Spline) surfaces to laser-scanned point clouds of free-form surfaces for existing parts. The fitting process involves the initial estimation of the NURBS surface control points using least-squares approximation, followed by optimization of NURBS weights to minimize fitting error. Optimization is performed using a hybrid coding scheme, namely; Modified Continuous Reactive Tabu Search (M-C-RTS), in which a combinatorial optimization component, based on Reactive Tabu Search (RTS), co-operates with Sequential Quadratic Programming (SQP), as a local minimizer. The developed fitting algorithm was applied to a number of simulated free-form surfaces in addition to a laser-scanned PC mouse. Implementation was carried out using MATLAB software and the results were compared to those obtained using Genetic Algorithms (GAs) in an earlier publication. The results show the superiority of the proposed algorithm to the GA-based method with respect to the number of objective function evaluations (about 50% reduction). In addition to this time saving achievement, and surprisingly, M-C-RTS proved to be capable of finding better solutions than GAs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".