Bibliographic record
Abstract
Controlling and eliminating defects, such as macro-porosity, in die casting processes is an on-going challenge for manufacturers. Current strategies for eliminating macro-porosity focus on the execution of pre-set casting cycles, die structure design or the combination of both. To respond to process variability and mitigate its negative effects, advanced process control methodology has been developed to dynamically drive the process towards optimal dynamic or static operational conditions, hence minimizing macro-porosity in the casting. In this thesis, a Finite Element heat transfer model has been developed to predict the evolution of temperatures and the volume of encapsulated liquid in a casting with a high propensity to form macro-porosity. The model was validated by comparison to plant trial data. A virtual process has then been developed based on the model to simulate the continuous operation of a real process, for use as a platform to evaluate a controller’s performance. Since macro-porosity cannot be measured during casting, die temperature has been used as an indirect indicator of this defect. A model-based methodology has been developed to analyze the correlation between die temperature and encapsulated liquid volume, a precursor to the formation of macro-porosity. This methodology is employed to assess the suitability of different in-cycle die temperatures for use as indicators of macro-porosity formation. The optimal locations have then been determined to monitor die temperatures for the purpose of minimizing macro-porosity. A nonlinear state-space model, based on data from the virtual process, has been developed to provide a reliable representation of this virtual process. The control variable-driven portion exhibits linear dynamic behavior with nonlinear static gain. The resulting MIMO state-space model facilitated the design of a controller for this process. Finally, the performance of the nonlinear model-based predictive controller was evaluated using the virtual process. Independent of the initial state of the process - i.e. steady state or startup, the controller exhibited the capability to automatically adjust the process toward the dynamic or static optimal operational condition during disturbances examined. The advanced control methodology developed for LPDC provides a novel solution to improve the operational conditions in die casting process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".