Characterization of metamaterials used as support structures in additivemanufacturing
Bibliographic record
Abstract
Additive manufacturing (AM) of high temperature alloy components requires a post-processing step consisting in inducing hot isostatic pressure (HIP) which aims to obtain an adequate microstructure and reduce porosity. During this treatment, the printed parts are likely to crack due to the induced deformations. This problem currently limits the deployment of AM in the manufacture of turbomachines. To solve the problem of cracking, this research project proposes the study of the behavior of new "lattice" structures which, integrated into the supports of printed parts, would allow a reduction in the residual stresses causing cracking during the HIP. The main objective of the research project is to provide a complete characterization of new "lattice" structures under complex loading modes from a multi-scale model. This model will enable highly reliable component-scale structural behavior predictions that can be leveraged to rapidly implement optimized metamaterials in engineered components with varying geometries, load cases, and operating temperatures. The optimized metamaterials will have unique macrostructures designed to exhibit specific mechanical properties, such as high strength-to-weight ratios, enhanced ductility, or tailored thermal expansion coefficients, that cannot be achieved using conventional materials. The behavior of these new structures is different from conventional structures in terms of temperature and stress profile. Although preliminary results indicate that they could work in a limited design space, there is a high level of uncertainty that these structures will still provide the expected benefits when considering printability, manufacturability, cost, cooling, damping, or other value attributes. The first stage of the project was to design the test coupons using Rhino Grasshopper which uses mathematical logic to create surfaces. Once the gage section of the tensile specimens was created, the design of the coupon had to be completed using a CAD software. Currently, the research team is designing a static finite element simulation model that would allow the study of the mechanical behavior of the newly developed structures. The finite element model is being validated with experimentally obtained tensile test results. Once the model has been validated, it will be possible to carry out a design of experiments which would make it possible to vary all the design parameters of the lattice structures developed. Additionally, new tensile and compression samples have been designed and are in the process of being printed. These new test specimens all have different amplitudes and wavelengths, and they will be mechanically tested to fully characterize the behavior of the new structures.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".