Spark Assisted Chemical Engraving: A Novel Approach for Quantifying the Machining Zone Parameters Using Drilling Forces
Notice bibliographique
Résumé
Glass has stirred human interest since the dawn of history due to its unique properties including its high mechanical strength, transparency, thermal and chemical properties. With the great technological advancement that we are witnessing today in the micro-technology field, glass micro-machining has already found applications in the optical, electronics, and biomedical applications. In fact, such applications require high-aspect-ratio structures of defined wall flatness and surface roughness. \nThere exist nowadays several glass micro-machining technologies that are being developed to meet this demand. These are based on thermal (laser), chemical (dry and wet etching), and mechanical (ultrasonic, abrasive and diamond-tool drilling) processes. Spark Assisted Chemical Engraving (SACE) is a non conventional glass micro-machining technology which is based on discharge generation at the tool tip. This is known to heat up the glass surface. \nToday, the machining mechanism is highly questionable where it is explained differently by many researchers in the field. This is due to the fact that the basic understanding about the process and the local variables in the machining zone is still missing. Although research about SACE drilling has allowed achieving deeper and smaller holes, these results remain specific to certain machining conditions. In fact, they are achieved experimentally by trial and error due to limited knowledge about the process fundamentals. Therefore, it can be said that SACE machining is still blind where the idea of doing feed-back drilling has not been explored sufficiently. These are the basic reasons of why SACE glass machining remains in laboratories and is never applied in industry. \nThe aim of this work is to unveil basic information about the SACE machining process and the local parameters in the machining zone. For this purpose, a methodology is developed for measuring the local machining zone parameters based on the force exerted on the tool during machining. Measurement errors caused by tool bending, wear and thermal expansion are quantified and considered while measuring and analysing the machining forces. Thus, in a first step, the machining force is characterized and analysed to get a deeper understanding about its origin and the reasons of its formation. This signal is used in a second step to extract information about local variables including the machining gap size, the local glass surface temperature and the origin of its texture. Based on the understanding of the machining \nprocess that this work brings, a thermal model is built which describes heat transfer to the glass surface. The agreement between calculations and measurements ensures the validity of both the model and measurement methodology. Based on the results, the machining mechanism is explained as a thermal assisted etching process. Machining can exist in two modes based on the electrolyte state (aqueous or molten) which depends on the local flushing. \nForce signal readings showed that tool-glass bonding can occur during machining which may hinder the drilling progress. Based on the understanding that this work brought about the machining mechanism and the factors that influence it, force feedback algorithms are built with the aim to establish a balance between local heating and flushing. The efficiency of the various algorithms in enhancing the drilling performance was compared and assessed based on the resulting drilling time. The knowledge acquired allowed building algorithms that succeeded in drilling high-aspect-ratio holes up to 1:9 while using very small tools (70 microns diameter) without breakage. The resulting drilling time is dramatically reduced to few seconds compared to several minutes in the state-of-the-art SACE drilling.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».