Optimization the Parameters of Hotwire Cutting Process to Enhance the Properties of Polystyrene Foam
Bibliographic record
Abstract
Hot –Wire cutting process is one of the important method to produce different shapes and prototypes of Extruded Polystyrene (XPS) and Expanded Polystyrene (EPS) material . The study and analysis of Hot-Wire cutting parameters play an important role to enhance the quality and accuracy of the process and products . The effect on the surface has been investigated by using the experimental test which designed according to the Taguchi orthogonal array (OA). In this study, four parameters,(temperature of wire(A) (°C), diameter of wire (B) (mm) , velocity of cutting (C) (mm/min), and density of foam (D)(gm/cm3) , with five levels for each parameter have been used. A full process would require (45 =625) experiments .The design of experiments(DOE)n performed L25(45 ) orthogonal array , which suggested by Taguchi to reduce the high required number of experiments to 25 effected tests. In the present study, the signal to noise (s/n) ratio have been performed for analysis the results , by statistical software(MINITAB17) to establish the optimum condition for a process and estimate the contributions and response under optimum condition. In addition , the analysis of variance (ANOVA) has been performed to identify the significant parameters affected on accuracy and quality. Keywords : hotwire cutting, Polystyrene, foam cutting, Taguchi, ANOVA. DOI : 10.7176/ISDE/10-5-01 Publication date :June 30 th 2019
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".