Shape optimization of wooden bats using genetic algorithm and artificial intelligence
Bibliographic record
Abstract
Cricket and baseball are popular and increasingly wealthy bat-and-ball sports. The bat is the key instrument used to score runs in both sports. In professional cricket and baseball leagues, the bats are made from a single piece of wood. Wooden cricket and baseball bats are the focus of increasing scientific research, and in this thesis, I describe an innovative approach to improving the performance-related geometry. I use parametric finite element (FE) modelling in combination with genetic algorithm (GA) to optimize the dynamic and vibrational properties of cricket and baseball bats. Parametric FE modelling enables an algorithm to tailor the mass distribution and mechanical properties of bats to converge the location of two points on a bat that are associated with increased velocity of a ball rebounding off bats: vibrational nodal points and center of percussion (COP). Modelling was able to reduce the distance between nodal points and COP from 174.5 to 98.1 mm and from 166.0 to 52.1 mm for cricket and baseball bats, respectively. This change occurred as a result of modifications to the geometry of the bats notably shifting cricket bat’s mass towards its end, and shifting baseball bat’s mass towards the center of the barrel and removing mass from the very end of the barrel. The combination of modelling and GA optimization required a powerful computer and long computational times. I further showed in this thesis that an artificial neural network (ANN) can be trained to replace the FE modelling component of my optimization system, which was the bottle-neck for bat optimization. I conclude that: (1) the combination of parametric modelling and GA optimization is an effective tool for altering the geometry and mass distribution of bats which could improve the rebound velocity of a ball hitting these bats; (2) my approach can reveal new performance-related geometries for both cricket and baseball bats; (3) GA-ANN optimization is a more computationally efficient approach for optimizing the design of bats.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".