An ExtensionDecision Tree Algorithm for Lightweight Design of Autobody Structure
Bibliographic record
Abstract
The lightweight design of autobody involves multiple objectives and requires the collaboration between various disciplines.To improve the existing autobody lightweight designs, it is necessary to establish an accurate and objective evaluation method for lightweight effect.This paper proposes an extension decision tree (EDT) algorithm for lightweight design of autobody.The algorithm solves the contradictions in the autobody lightweight design were solved through divergence and convergence.The workflow of the solving process is shaped like a scalable diamond.Specifically, the knowledge of autobody structure was described accurately by extension modeling and extension divergence reasoning.Then, the lightweight design of autobody structure and material was achieved through extension transforms.Next, the EDT algorithm was constructed based on extension theory and the DT algorithm, and used to evaluate the lightweight effect of autobody.Finally, the effectiveness of the proposed algorithm as verified through a case study and a computer-aided engineering (CAE) simulation.The results show that our algorithm can accurately predict the weight reduction effect of autobody based on the case data, and generate a set of intelligent strategies to optimize the current design.The research results shed new light on intelligent evaluation of autobody lightweight design with multiple objectives and constraints.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".