Optimization of autoclave processing of polymer composites using a genetic algorithm
Bibliographic record
Abstract
Current industrial autoclave cure cycles used to process composites in the aerospace industry are conservative and costly.It is therefore desirable to optimize cure cycles based on industrial quality requirements to reduce costs.Process optimization requires several key components including a process model, an optimizing scheme, and an objective function.To address this problem, the University of Manitoba and its industrial partners have set out to develop the Advanced Process Control System (ApCS).As a portion of this project, the primary objective of this thesis was to develop an objective function and interface with a genetic algorithm based path generator and a process model COMPRO, developed at The University of British Columbia, to develop the optimizer for autoclave process cycle optimization.Models run by the process model used Cytec- Fiberite 934 with Toray T300 fibres.As a secondary objective for the thesis, material characterization was performed for Hexcel F155 resin with Toho T300 car-bon fibres for use in future work with APCS.Heat transfer and warpage predictions made by the process model were validated by experiments performed at Boeing Technology Canada -'Winnipeg Division.This thesis discusses the effects of various versions of the objective function and the inputs required by the genetic algorithm.Based on the optimization results for various optimizer settings used in the study, the thesis concludes by providing the best optimizer configuration. ACKNO\MLEDGEMEI\TSThank you to my fellow students for their support and words of encouragement:-Michael Hudek for his assistance with boundary conditions for the process model as well as all the support with the GA code.-Madhava Koteshwara
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".