An integrated approach of VIKOR and teaching learning based optimization algorithm for milling machinability computations
Bibliographic record
Abstract
The significance of producing Carbon nanomaterials (CNMs) reinforced polymer composites are increasing in manufacturing trades due to their exceptional performances. CNM modified composites are primarily employed in structural component needs due to expanded physicomechanical properties. This paper highlights a coherent approach of the VIšekriterijumsko KOmpromisno Rangiranje(VIKOR) and Teaching learning-based optimization algorithm (TLBO) to evaluatethe Milling efficiency. The machining was performed for the Milling process of0-D carbon nano onion (CNO) reinforced polymer (Epoxy) composite at four different levels of Box Behnken Design (BBD). The Milling performances such as Material Removal Rate (MRR) and Surface roughness (SR) were optimized to enhance product quality and productivity. The control of varying process constraints, viz. Weight % of CNO filler content(A), cutting speed (B), feed rate (C) and depth of cut (D), was used to optimize the machining response. The conflicting response is aggregated through the VIKOR method to develop the fitness function for an algorithm. The process constraints play a significant role in influencing the cost and productivity ofthe machined components. The objective function derived from VIKOR was supplied as input into the TLBO algorithm. The results demonstrated that the spindlespeed, feed rate, and weight % of CNO filler are the most contributing factors for machining indices. Also, the hybrid VIKOR-TLBO module shows a lower error percentage than the conventional VIKOR method. The microstructural investigation of the machined surface reveals the feasibility of the proposed hybrid module in a production environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".