Application of composite models for the prediction of failure ofadditively manufactured polymers
Bibliographic record
Abstract
The field of additive manufacturing (AM) has recently transitioned from a process used predominantly for prototyping and demonstration, into the production of end use components. This has created new pressures to improve the design of parts made from additively manufactured materials. Some of the additive manufacturing processes such as selective laser sintering, selective laser melting, and digital light projection create materials that are similar to their traditionally manufactured counterparts. Fused filament fabrication (FFF), also known as fused deposition modeling (FDM) creates materials with strong anisotropy and properties that are highly dependent on the process parameters. Attempts have been made over the past years to develop predictive models on the yield or ultimate tensile strengths of FFF produced materials from the input parameters. However, the application of these models has been limited in scope due to the data used to generate them. The most common approaches involved constitutive equations, machine learning/genetic programming, or curve fitting. These methods demonstrate an ability to predict the material properties, but usually only within the boundaries of the initial data used to train the model. For future improvement, a model that takes an analytical approach is required in order to account for all possible scenarios. In this paper, the application of composite models such as Tsai-Hill and inter-active polynomial theory (Tsai-Wu) in modeling FFF materials were analyzed . Experimentation performed showed that failure characteristics of FFF materials are to some extent similar to composite materials. If successfully implemented, these models can analytically predict the yield or ultimate tensile strength of AM part under any three dimensional stress scenario after a few calibration tests In this paper, validation tests were performed on polyethylene terephthalate-glycol (PETG) and polycarbonate (PC) samples and the results showed that the models were successful in predicting the yield strengths of AM materials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".