Spark Assisted Chemical Engraving: A Novel Approach for Quantifying the Machining Zone Parameters Using Drilling Forces
Bibliographic record
Abstract
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".