Characterization of the injection molding process of passive vibration isolators
Bibliographic record
Abstract
This article discusses the variability of the mechanical properties (static stiffness, dynamic stiffness, and loss factor) observed in engine mounts used to isolate vibration caused by the engine in recreation utility vehicles. To avoid passenger’s discomfort during engine operation, it is important that the isolation provided by each passive vibration isolator be constant. Transmitted forces should also be minimized to prevent excessive structural stresses in the vehicle. Quantifying how human and machine molding parameters affect the performance of the final product is fundamental. The isolators studied in this article are produced through manual cycles of an injection molding process. This work provides a better understanding of the discrepancies on mechanical properties occurring during the industrial process. Curing temperature ( T) and curing time ( D) were found to be the significant machine parameters. Response surface methodology shows a nonuniform distribution of the solutions across the whole experimental space. A linear model of the output variables appears to be sufficient to achieve an optimization since linear coefficients are prevalent over quadratic or interaction coefficients. A proposed empirical model enables the determination of a set of curing parameters corresponding to specific required properties. The model also shows that, for a polychloroprene rubber mix, the variability of the mechanical properties can be reduced by increasing the curing parameters ( T and D) used during current molding procedures. Finally, the numerical results helped getting a better understanding of how manufacturing parameters can influence the optimization process of elastomeric product properties. Improved production parameters and control standards can be established from this case study.
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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 | 0.001 |
| 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) | 0.001 | 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".