Dimensional variation analysis and optimal process design for non-rigid sheet metal assemblies
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».