Dimensional variation analysis and optimal process design for non-rigid sheet metal assemblies
Bibliographic record
Abstract
Non-rigid sheet metal assembly is widely used in manufacturing industries, such as aerospace and autornotive industries.Lnproving product quality and reducing the cost are main concerned issues for a manufacfuring company to achieve higher product competitiveness in current global market.The dimensional quality of a non-rigid sheet metal assembly is a crucial and yet challenging quality indicator due to the non-rigidity of the sheet metal components.Although the product dimensional variation analysis and process design for rigid assembly have been studied for rnany years, such study for non-rigid assemblies is emerging, and also challenging.There relnain many uffecognized and/or unsolved issues in the study of non-rigid assemblies.This thesis presents a number of new, systematical, and generally applicable methods for analyzing and minimizing the non-rigid sheet metal assembly variations.Firstly, a novel fractal-based method for sheet metal assembly variation analysis is developed to deal with the fraclalvariations of parls (i.e., component of an assembly)-The surface microstructure of part variation is rnodeled by fractal geometry and its influence on the final assembly variation is studied by modeling the sheet metal assembly process' Next, a new methodology based on wavelet transfonn is proposed for analyzing the contribution of variation components with various scales to the final assembly dimensional variation, considering possible sources of variation frorn both parls and the assembly process.It is more general and advantageous than the approach based on the fraclal geornetry.The integrated procedure of wavelet transfonn and Finite Element Method (FEM) for non-rigid assembly variation analysis is developed and irnplemented.Its effectiveness is demonstrated via an application example.Thirdty, a sirnultaneous optimization method for fixture layout and joint positions ts developed.The optimization variables from both the product design (assembly joint positions) and the production plan (the fixture layout) are included in the mathematical model.The mode-pursuing sampling rnethod (MPS) is modified and ernployed to search for the global optimal solution.Finally, the elastic contact phenomenon in the sheet rnetal assembly process is studied.A non-linear assembly dimensional variation analysis method is developed by establishing the elastic contact rnodel between the assembly surfaces.The assernbly dimensional variation analysis with and without contact rnodeling is respectively conducted.The corresponding physical experiments are also carried out and used to validate the contact FEM models.The work enables us to gain more in-depth understanding on the characteristics of the non-rigid sheet metal assembly dimensional variation.It provides not only the fundamental analysis and modeling methodologies, but also the corresponding software tools that can be easily integrated with rnost current general-purpose coÍlmercial FEA packages (such as ANSYS and CAIIA).The developed approaches, technologies and tools presented in this thesis can benefit both the academic research and industrial applications on the design and manufacturing of non-rigid sheet metal assemblies.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".