Inspection & modelling of cusp geometry in additive manufacturing to predict product???s surface roughness
Bibliographic record
Abstract
The final dimensional and geometric inaccuracies, and the resulting high surface roughness of the products have been the major problems in employing Additive Manufacturing (AM) technologies. Most of commonly used Additive manufacturing (AM) technologies are developed based on a layer-based manufacturing process to fabricate 3D models. However, a critical drawback that reduces the surface quality of the AM parts is the stair case effect as a direct result of the layered deposition of the material. In this thesis, a new approach to model surface roughness in Fused Deposition Modeling (FDM) is proposed. Based on actual observations and modeling of the cusp geometry under various setups and fabrication conditions, an empirical model to express the surface roughness distribution is presented. The developed methodology presents mathematical expressions for the profile of cusps classified based on two parameters of additive manufacturing layer thickness and the slope of the fabricated surface. Considering the fact that the cusp profile crucially affects the surface quality, the developed model is used directly to estimate surface roughness of the final product. The proposed expression is verified by implementation and comparison with the experimental case studies. The developed models can be used for optimum selection of the build direction or layer thickness when a certain surface roughness range is targeted. It can also be used as a tool for modification of the design to control the final surface roughness of the AM products.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".