Experimental Characterization and Multi-Objective Optimization of the Orbital Drilling Process of CFRP
Bibliographic record
Abstract
Defects associated with drilling of Carbon Fiber-Reinforced Polymers (CFRPs) are of major economic and safety concerns for aerospace manufacturers. One of the most critical defects associated with drilling of CFRP laminates is delamination of layers which can be avoided by keeping the drilling forces below some threshold levels. Orbital Drilling (OD) is an emerging drilling process that exhibits lower cutting forces and temperatures, easier chip removal, higher produced surface quality, longer tool life, and a high possibility for dry machining. The OD process is featured by cyclic engagement and disengagement between the tool and the workpiece whereby a considerable part of the work done by the tool is directed towards the tangential direction while the work done in the axial direction is reduced. This reduces the risk of delamination at the exit. The objective of this research work is to investigate the effect of the OD process key parameters with respect to the produced hole attributes (surface roughness, delamination, and hole accuracy), as well as the cutting forces and temperatures. All the OD tests were performed under dry conditions using a four-flute 6.35 mm end-mill. The cutting forces were recorded using a 3-component dynamometer Kistler 9255B and cutting temperatures were measured using a FLIR ThermoVision A20M Infrared camera at the holes exit. A full factorial design of the experiment was used whereby the feeds varied from 60 to 360 mm/min and the speeds from 6,000 to 16,000 rpm. The test material used was a quasi-isotropic laminate comprising woven graphite epoxy prepreg. Analysis of the results showed 45% reduction in the axial force component in orbital drilling (OD), compared to conventional drilling. None of the holes produced by the entire set of experiments has experienced any entry or exit delamination. ANOVA was used to identify the significance of the controllable variables on the experimental outputs. To overcome the challenge of optimizing the competing parameters of the hole quality attributes while maximizing the productivity, an algorithm was applied by hybridizing Kriging as a meta-modeling technique with evolutionary multi-objective optimization to optimize the cutting parameters.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".