Global adaptive slicing of NURBS based sculptured surface for minimum texture error in rapid prototyping
Bibliographic record
Abstract
Purpose – This paper aims to propose a global adaptive direct slicing technique of Non-Uniform Rational B-Spline (NURBS)-based sculptured surface for rapid prototyping where the NURBS representation is directly extracted from the computer-aided design (CAD) model. The imported NURBS surface is directly sliced to avoid inaccuracies due to tessellation methods used in common practice. The major objective is to globally optimize texture error function based on the available range of layer thicknesses of the utilized rapid prototyping machine. The total texture error is computed with the defined error function to verify slicing efficiency of this global adaptive slicing algorithm and to find the optimum number of slices. A variety of experiments are conducted to study the accuracy of the developed procedure, and the results are compared with previously developed algorithms. Design/methodology/approach – This paper proposes a new adaptive algorithm which globally optimizes a texture error function produced by staircase effect for a user-defined number of layers. The adaptive slicing algorithm dynamically calculates optimized slicing thicknesses based on the rapid prototyping machine’s specifications to minimize the texture error function. This paper also compares the results of implementing the developed methodology with the results of previously developed algorithms and presents cost-effective optimum slicing layer thicknesses. Findings – A new methodology for global adaptive direct slicing algorithm of CAD models, based on a texture error function for the final product and the possible layer thicknesses in rapid prototyping, has been developed and implemented. Comparing the results of implementation with the common practice for several case studies shows that the proposed approach has greater slicing efficiency. Typically, by utilizing this approach, the number of prototyping layers can be reduced by 20-50 per cent compared to the slicing with other algorithms, while maintaining or improving the accuracy of the final manufactured surfaces. Therefore, the developed slicing method provides a better solution to trade-off between the rapid prototyping time and the rapid prototyping accuracy. For the many advantages of global direct slicing, it can be seen as the future solution to the slicing process in rapid prototyping systems. Originality/value – This paper presents an innovative approach in direct global adaptive slicing of the additive manufacturing parts. The novel definition of an error function which comprehensively addresses the resulting manufactured surface quality of the entire product allows presenting an objective function to solve and to find the optimum selection of all the layer thicknesses during the slicing process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".