An Integrated Framework for Optimal Design of Complex Mechanical Products
Bibliographic record
Abstract
Abstract The presently achieved research results are not effective for the design of complex mechanical products when various methods and tools in different schemes have to be employed at different design stages. A new integrated framework for the optimal design of complex mechanical products is introduced in this research considering modeling, simulation, and optimization aspects. First, a hybrid scheme is developed for the integrated modeling of complex mechanical products. In this hybrid scheme, descriptions of a generic product are modeled by an and-or tree. Feasible design candidates are created from the and-or tree through tree-based search. Geometric descriptions in a design candidate are associated with a computer-aided design (CAD) system. Second, a hybrid simulation method is developed for the evaluation of different product aspects with different simulation tools which are integrated through the hybrid modeling scheme. Simulations with geometric descriptions are conducted by analysis functions of the CAD system. Simulations with non-geometric descriptions are conducted by the knowledge-based systems. Third, a hybrid optimization method is developed to identify the optimal design of the complex mechanical product. For each design candidate, parameter optimization is conducted to obtain the optimal parameter values. The optimal design solution is identified from all design candidates through configuration optimization. A prototype system has been implemented for conceptual design and detailed design of complex mechanical products.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".