Biobased hybrid composite design for optimum hardness and wear resistance
Bibliographic record
Abstract
The present investigation considered the design of a biobased hybrid particulate composite for optimal hardness and wear resistance. Tests were conducted based on the plan of 20 sets of experiments generated through Model-Based Calibration Toolbox™ contained in MATLAB routines. A Portable Ultrasonic Hardness tester was used to record the hardness properties while the wear behavior of the composite was tested using a pin-on-disk machine. The optimization study was applied to the Calibration Generation (CAGE) platform utilizing the Normal Boundary Intersection (NBI) algorithm which enables the development of a Pareto optimal set with a continuous and equally distributed chart. Scanning Electron Microscopy (SEM) was used to perform morphological examination. From the optimized results, it was observed that a particle size of 1752 µm, a volume fraction of 45%, and a stirring time of 70 s gave the best-ranked composite exhibiting optimal values of 784.91 Leeb hardness, 643.19 Rockwell hardness, 593.17 Brinell hardness, and 0.000139 mm3/Nm specific wear rate. Under the same conditions, the predicted values of the optimization model closely matched the experimental results. The NBI optimization technique proves to be a viable method for performing material design and property improvement tasks. Surface morphology analysis via SEM revealed that the wearing of bio-based hybrid particulate composite parts is associated with delamination and abrasion mechanisms. It is implied that the new material can be used for applications such as furniture, automotive spare parts, and other inexpensive technical solutions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".