Modelling of process-microstructure-properties relationships in aluminium components with advanced microstructural characterization and machine learning
Bibliographic record
Abstract
Modelling of process-microstructure-properties relationships in aluminium components with advanced microstructural characterization and machine learning Microstructural characterization, especially using optical microscopy, has been an important part of process development and process operation of aluminium components during the last decades. At the era of digitalisation and artificial intelligence, manufacturing processes are pushed to move toward a new paradigm where data intelligence and machine learning are fully integrated. This work presents a novel approach for optical microscopy and show how it can be integrated into a digital process data workflow and leveraged to gain critical insight on the process-microstructure-properties relationships with the help of machine learning. High pressure vacuum die casting (HPVDC) of Aural™-2 alloy and cold spray for additive manufacturing (CSAM) of AA6061 alloy, both in a research environment, are taken as example to illustrate the approach. It is demonstrated that with a proper database structure, advanced image analysis methods and a custom easy-to-use machine learning tool, it is possible to automate and speed-up a large part of the workflow and improve the overall value of the characterization on one hand by gaining insight on the process to microstructure relationships which can help to understand and improve the process and on the other hand, by developing a predictive model for the mechanical properties using the microstructure data as inputs for the model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".