Conventional versus Additive Manufacturing—Comparative Eddy Current Testing on Reference Blocks
Bibliographic record
Abstract
Metal-based additive manufacturing (AM) is a topic that cannot escape attention in most engineering fields. Its capabilities of having complex parts manufactured on demand, while reducing waste and cost, has raised the attention of the aerospace, automotive, and biomedical industries. Nevertheless, additively manufactured parts need to be proven to meet or exceed the quality and performance characteristics of their conventionally manufactured counterparts. Nondestructive evaluation (NDE) plays a critical role in the acceptance of AM, and reference specimens should be available for the performance evaluation of applicable techniques. From an NDE perspective, AM poses unique challenges, such as geometrically complex parts and manufacture-introduced flaws; anisotropy of mechanical, electrical, and elastic properties; as well as rough surface finish. Part quality and performance have to be assessed through a series of tests, including nondestructive ones. The detection capability of an NDE technique has to allow finding discontinuities smaller than the maximum allowable size. Development of NDE procedures along with design of suitable reference blocks need to be prioritized before the adoption of AM for mass production. Considering the lack of reference blocks for AM, with discontinuities specific to these manufacturing technologies and known critical defect type and size, some insights could be gained from the existing knowledge of inspecting conventionally manufactured parts. In this study, common eddy current reference blocks of aluminum and titanium alloys are examined by comparison with respect to fabrication modes (i.e., conventional versus additive). Maintaining the same instrumentation, parameters, procedures, and interpretation schemes, the eddy current technique is used to evaluate potential changes in inspection indications between the two manufacturing technologies. Moreover, metallography is performed on all blocks in an attempt to link microstructure with eddy current responses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".