Modelling of mechanical properties of Al-Si-Cu cast alloys using the neural network
Bibliographic record
Abstract
Purpose: The paper presents some results of the research connected with the development of new approach based on the neural network to predict the chemical composition and cooling rate to the mechanical properties of Al– Si–Cu cast alloys. The independent variables in the model are chemical composition of Al–Si–Cu cast alloys and cooling rate. The dependent parameters are hardness, microhardess, yield strength and apparent elastic limit. Design/methodology/approach: The experimental alloy used for training of neural network was prepared at the University of Windsor (Canada) in the Light Metals Casting Laboratory, in a 10 kg capacity ceramic crucible. Thermal analysis tests were conducted using the UMSA Technology Platform. Compression tests were conducted at room temperature using a Zwick universal testing machine. Prior to testing, an extensometer was used to minimize frame bending strains. Compression specimens were tested corresponding to each of the three cooling rate. Rockwell F–scale hardness tests were conducted at room temperature using a Zwick HR hardness testing machine. Vickers microhardness tests were conducted using a DUH 202 microhardness testing machine. Findings: The results of this investigation show that there is a good correlation between experimental and predicted dates and the neural network has a great potential in mechanical behavior modeling of Al–Si–Cu castings. Practical implications: The worked out model can be applied in computer system of Al–Si–Cu casting alloys selection and designing for Al-Si-Cu casting parts. Originality/value: Original value of the work is applied the artificial intelligence as a tools for designing the required mechanical properties of Al-Si-Cu castings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".