Multi-objective optimization design for the table of a CNC machine tool based on bionic structures
Bibliographic record
Abstract
To improve the static and dynamic performance of CNC machine tool tables, a multi-objective optimization design method based on bionic structures is proposed, which involves bionic design and size optimization design. Based on the bionics, four types of bionic structure tables for the original XK5032 machine tool were designed, namely bamboo cross-section bionic structure table, spider web bionic structure table, honeycomb bionic structure table, and prairie rushes bionic structure table. Static and modal analysis of the original and four types of bionic tables were conducted using finite element simulation methods validated through modal experiments. The results show that the overall mechanics of the four bionic tables have been improved compared to the original table. Using the entropy weight TOPSIS method, the prairie rushes bionic structure table was selected from four types of bionic tables. Conducting multi-objective optimization design on the prairie rushes bionic structure table, three optimization candidate schemes were obtained. The entropy-weighted TOPSIS method was used again to select the optimal solution from these three schemes, resulting in a 0.8% reduction in mass, an increase in first-order natural frequency by 2.8%, a decrease in maximum displacement by 3.2%, and a significant 21.6% reduction in maximum equivalent stress compared to the original table.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".