EXPERIMENTAL POLYMERIC NANOCOMPOSITE MATERIAL SELECTION FOR AUTOMOTIVE BUMPER BEAM USING MULTI-CRITERIA DECISION MAKING METHODS
Bibliographic record
Abstract
Material selection is a main purpose in design process and plays an important role in desired performance of the products for diverse engineering applications. In order to solve material selection problem, multi criteria decision making (MCDM) methods can be used as an applicable tool. Bumper beam is one of the most important components of bumper system in absorbing energy. Therefore, selecting the best material that has the highest degree of satisfaction is necessary. In the present study, six polymeric nanocomposite materials were injection molded and considered as material alternatives. Criteria weighting was carried out through analytical hierarchy process (AHP) and Entropy methods. Selecting the most appropriate material was applied using technique for order preference by similarity to ideal solution (TOPSIS) and the multi-objective optimization on the basis of ratio analysis (MOORA) methods respect to the considered criteria. Criteria weighting results illustrated that impact and tensile strengths are the most important criteria using AHP and Entropy methods, respectively. Results of ranking alternatives indicated that polycarbonate containing 0.5 wt% nano Al2O3 is the most appropriate material for automotive bumper beam due to its high impact and tensile strengths in addition to its low cost of raw material. Also, the sensitivity analysis was performed to verify the selection criteria and the results as well.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".