Bibliographic record
Abstract
To manufacture a component, one first needs to assess its structural design performance, damage tolerance, and service experience, and validate them with pertinent test results. Finite Element (FE) modeling can predict mechanical performance, save time and cost by limiting required structural testing. 3D printing is a layer-by-layer manufacturing technology that has been widely used for rapid prototyping applications in product design and development. Recently, there has been a move towards manufacturing functional products using 3D printing, which requires materials mechanical characterization and simulation. Mechanical characterization testing results are available for 3D printed ASTM D638 tensile coupons without defects, i.e. tension along (0°) and transverse (90°) to the printing direction, and a quasiisotropic stacking sequence. In addition, tensile test results of a quasi-isotropic coupon with intentional defects are also available. In this project, FE models of the coupons are created to obtain their tensile strength, modulus, and failure strain. First ply, last ply failure and stiffness reduction iterative approach have been implemented on a 2D shell model. MSC Software is used to simulate the analyses due to its ease of use for composites using 2d shell elements. This simulation is then extended to predict strength and stiffness of a quasi-isotropic coupon with defects. The analysis is also extended to implement progressive failure analysis to predict the ultimate strength of the laminate. For coupons without defects, FE models estimated test results of stiffness and strength within 1% error, while the error for estimating failure strain is higher. For coupons with defects, the error in calculating stiffness and strength is below 8%, while it is higher for failure strain. Although the stress-strain curve from FE simulation looks similar to experimental result, it is found that progressive failure analysis is necessary for obtaining failure strain values with acceptable error percentage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".