A Reverse CAD Approach for Estimating Geometric and Mechanical Behavior of FDM Printed Parts
Bibliographic record
Abstract
Fused Deposition Modeling (FDM) printed parts are widely used in various applications. To avoid material and time wastage, it is necessary to assess the geometric and mechanical behavior of the part beforehand. The geometric and mechanical behavior of FDM printed parts is analyzed by various virtual and experimental approaches. The virtual approaches are based on analytical models, which take solid computer-aided design (CAD) models or STL files as input for the analysis. However, in reality, the input CAD model is converted to a combination of slices (toolpath) before it is sent to print. The difference between the CAD and the toolpath model creates a research gap for estimating the properties accurately. The reason being that the printed part is not the replica of the original CAD model but of the sliced model which is dependent on various slicing parameters. This paper presents a novel algorithm, which is capable of converting the sliced file back to a CAD model (called the Reverse CAD model). The Reverse CAD model is capable of providing an accurate assessment of the geometric and mechanical behavior of the printed part as it also incorporates the effect of slicing parameters. In order to validate the algorithm, primitive geometries are printed, and their geometric deviation and mass properties are compared to the Reverse CAD model. Standardized tensile test specimens are also printed with two different materials to compare the experimental mechanical behavior with the finite element analysis of the Reverse CAD model. Comparative studies validate the Reverse CAD model as a better and more accurate estimator of the FDM printed part properties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".