Bibliographic record
Abstract
Automated assembly processes are being widely used in aircraft manufacturing as a hole must be drilled before fastener installation. Burr formation is of importance for machining processes including drilling. A burr, an unwanted by‐product of machining, is a raised edge that remains attached to a workpiece because of the loss of shear action. In drilling, burrs form as the drill enters and exits the part, particularly when the tool becomes worn. Burrs require an additional process to deburr, incurring extra cost and time. To understand drilling burr formation, this dissertation presents a new model that combines a burr model with a tool‐wear model. The first adopted model describes a burr with respect to its height and thickness and relates burr formation to the drilling thrust force generated from drilling with a sharp tool. This model comprises a number of key factors: part material property, tool geometry, and drilling parameters. In reality, these factors will vary. Preliminary work was done to use this model to develop a sensitivity matrix for the purpose of studying the effect of variation in the key factors on burr formation. Unfortunately, tool wear, which is a major issue, cannot be simply considered as a variation in tool geometry. When a tool becomes worn, the cutting edge radius becomes large, and the cutting action changes from shearing to ploughing. A second model adopted is a tool wear model, leading to the core work of this dissertation. The aim of this study is to develop a drilling burr‐formation model for realistic conditions, incorporating variance within the tool and process parameters. The proposed method applies a weighting model that can consider the wear/no‐wear condition for each segment, thereby leading to a hybrid model for drilling burr formation. The research method is validated through simulation and an experiment entailing the generation of drilling burrs in conditions mimicking those in aerospace manufacturing. The results show that the model successfully estimates burr height and thickness trends with increasing levels of tool wear, for penetrated holes. The methods and results presented in this dissertation show good promise for understanding the effects of tool wear on drilling burr growth.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".