Noise source identification and dynamic modeling of a pneumatic nailing device
Notice bibliographique
Résumé
Exposure to hazardous noise levels emitted by pneumatic nailing devices contributes significantly to risk of hearing damage among the construction workers throughout the world. This health problem comes from the lack of appropriate technology such as low noise devices which in turn results from the lack of scientific knowledge about designing reduced noise devices. This study contributes to the design improvement of pneumatic nailing devices through identifying the noise sources and developing the simulation tool required to redesign the pneumatic nailing device. \n \nTo identify the noise sources, the study uses a combination of two complementary experimental approaches. The first makes use of time-synchronized data analysis of several variables during the machine operation. This strategy allows identifying the physical processes and provides a detailed separation of the noise generation mechanisms in successive time sequences. However, since multiple noise sources radiate at the same time, this observation approach is not sufficient for noise source identification and ranking. Thus, it is completed by a selective wrapping and muffler procedure. This technique provides overall generated noise associated with each process, as well as ranking of the three major sources: (1) exhaust noise, (2) machine body vibrations, and (3) workpiece vibrations. A special investigation is conducted on this third one with two cases: a workpiece/worktable setup representative of the actual field usage of a nailing device and a workpiece/sandbox setup used in a standardized laboratory test. The study evaluates the efficiency of the workpiece/sandbox setup in reducing the workpiece radiation and obtains a typical workpiece contribution on an actual worksite. \n \nTo provide a simulation tool, a dynamic model of the pneumatic nailing device needs to be developed. Dynamic modeling of the nailing device requires mathematical modeling of the physical processes involved in its operation. All of these processes can be described through already existing mathematical relations, except for the penetration resistance force (PRF) imposed on the nails when penetrating the wood. \n \nThe PRF depends on various factors. This study follows two approaches in parallel to develop an empirical prediction law for the PRF: quasi-static and high-speed. \n \nThe quasi-static approach provides a rapid and precise representation of the law at quasistatic penetration velocities. The law covers the entire displacement range, various nail geometries and sizes, and wood types. \n \nThe high-speed approach aims to provide a law which covers a much wider range of penetration velocities. The approach is complicated since it requires a sophisticated test machine to conduct the nail driving tests at high penetration velocities. The study designs and fabricates an advanced test machine to later extend the prediction range of the PRF law. \n \nThe last part of this study develops the dynamic model of a nail gun while integrating the quasi-static PRF law. The model includes dynamics of all the air chambers and the moving parts, and interactions and impacts/contacts between different parts. The study integrates a comprehensive experimental validation of the model. Future improvements in the dynamic model precision will be possible by using the extended version of the PRF law.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».