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Shape optimization of wooden bats using genetic algorithm and artificial intelligence

University of British Columbia

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.

Text

2020-08-10

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/75405

Forestry

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/