A group multicriteria decision making with ANOVA to select optimum parameters of drilling flax fibre composites: A case study
Bibliographic record
Abstract
Composite parts are often drilled during assembly. However, it has been well established that drilling process can damage long-fibre composites, and the ideal process parameters need to be investigated based on each given material system, yet under different conflicting design criteria. Here, a multi-criteria decision making (MCDM) approach along with the analysis of variance is aimed to find the best-compromised solution for drilling parameters of a flax fibre composite plate; namely to minimize the top and bottom surface delamination factors while simultaneously maximizing the residual tensile strength of the drilled laminate. Different criteria importance weights along with different MCDM techniques have been modeled to capture different practical design scenarios. Overall, the majority of employed methods suggested a higher spindle speed, a lower feed rate, and a step drill bit geometry. Among the design factors, the feed rate by far played a statistically significant role (>95% confidence level) in controlling the damage outcome and is deemed of prime design concern. It is also shown that the inclusion of subjective weights by experts is a key in such design problems to avoid statistical overinterpretation.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".