Optimization of slicing direction in laminated tooling for volume deviation reduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Due to an uncertainty between actual model and assembled slices, there is always an extra material on assembled slices in laminated tooling. Therefore, a post processing, usually CNC machining, is required to remove this extra material and reach the near net shape surface for final product. One of the issues in laminated tooling is to minimize the amount of this extra material and reduce the cost of the post processing. Direction of slicing is an important parameter in this issue. This research aims to introduce a method to find the best slicing direction based on CAD model surface geometry and minimize the amount of the extra material in the assembled slices. Researches on the best slicing direction investigation so far were mostly based on the extra volume calculation for a number of candidate directions. Since the time needed for the extra volume calculation is proportionally high, the number of candidate directions to be investigated was usually limited, whereas, in the proposed method, the best slicing direction is found based on CAD model surface geometry and there is no need to find the actual amount of the extra volume. Moreover, the suggested method is developed to the cases where having more than one slicing direction is desirable for more reduction in the amount of the extra volume. The proposed optimization method can be used to find the best slicing direction in laminated tooling. Moreover, the ability to suggest multiple slicing directions can provide more reduction for the amount of the extra material. However, the number of candidate directions in the case of multiple slicing directions is limited due to joining problems in laminated tooling. Design/methodology/approach The investigation is based on the situation of normal vectors on CAD model surface. The CAD model surface is considered as a combination of planar tiles and all normal vectors of these tiles are considered as the candidate directions. This provides a number of candidates that can cover almost all possible slicing directions. The best slicing direction is then found by estimating the amount of the extra material produced on the tiles by each normal vector. Findings The proposed method applied to some examples. The case studies included the simple predictable models to qualify the reliability of the proposed method. Also more applicable examples were provided to show how the suggested method acts in real cases. Research limitations/implications The proposed method can be applied to each and every CAD model. Therefore, there is no limitation with regard to the type of model which can be investigated by the proposed method. However, there is limitation on the number of times the building direction can be changed in laminated tooling. Practical implications The proposed method can be employed to reduce the post processing time in laminated tooling. Originality/value Following the prior study researchers conducted in optimization of laminated dies, another parameter, slicing direction, is considered in this research. This brings a new approach on laminated dies optimization to reduce the production cost.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it