Optimization of Fixture and Joint Positions in Sheet Metal Assembly: The Effect of Fixture Numbers and Constraints
Bibliographic record
Abstract
Assembly process is widely used in the manufacturing processes. Fabrication processes such as machining, casting and metal forming are not perfect and introduce variation in the components. Variations of components and tools accumulate and cause the assembly variation. In this paper, after reviewing the literature and presenting sheet metal assembly variation analysis, an optimization method is used to minimize the assembly variation by optimizing the location of joints and fixtures. The model is constructed in ANSYS with three fixtures and two joints. When a black-box function calculated numerically in software is used as the objective function, using deterministic methods for optimization is not suitable because the deterministic methods need knowledge of the objective functions. Also, using stochastic methods such as genetic algorithm is not suitable because of the large number of function evaluations they normally need. In this paper, an optimization algorithm based on mode-pursuing sampling (MPS) method is used to minimize the assembly variation. The optimization method is explained and after implementing the method, results are presented. It is learned that, in addition to the number of fixtures, the constraints on neighboring fixture locations also affect the optimal fixture layout, as well as the final assembly stiffness and spring-back.
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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 | 0.000 | 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 | 0.000 | 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".