Comprehensive thermal optimization of liquid composite molding to reduce cycle time and processing stresses
Bibliographic record
Abstract
Abstract Liquid composite molding (LCM) is a well‐established and flexible composite manufacturing technology capable of producing large parts at a relatively low cost. In this family of related injection processes, a large number of design variables have strong impact on manufacturing performance. The determination of adequate process parameters is key to yield successful molding conditions and reduce cycle time. In addition, properties and durability of composite parts are strongly affected by internal stresses. Excessive stress levels may lead to important defects in the part at the curing stage and after processing, when the part is cooled to room temperature. In this investigation, a comprehensive curing optimization algorithm is proposed to reduce internal stresses during composite processing. This study focuses on the minimization of the macroscopic residual stresses that appear during cure and cooling in thermoset composite laminates as a result of temperature and degree of cure gradients. The proposed fitness function to be minimized is based on the physics of the matrix material transformation and on the mechanical behavior of the composite material. An evolutionary strategy based on genetic algorithms (GA) is implemented for the minimization of the fitness function. Optimization is carried out for thin and thick glass/polyester laminated composites. Different optimization schemes with thermo‐elastic and viscoelastic models of the composite mechanical properties are studied. The advantages and drawbacks of each model are stated and discussed. POLYM. COMPOS., 26:209–230, 2005. © 2005 Society of Plastics Engineers
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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 | 0.000 |
| 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) | 0.001 | 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".