Comprehensive thermal optimization of liquid composite molding to reduce cycle time and processing stresses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
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 it