Optimal shape determination of shear specimens using Machine Learning

https://doi.org/10.3221/IGF-ESIS.78.07

Shear strength represents an important mechanical property. For the additively manufactured polymers nowadays, there is not a valid standard to determine the shear strength. Finite element analysis of 145 shear specimen geometries was performed. Then an AI-based optimal shape prediction methodology was developed using supervised machine learning, where a Multiple Linear Regression model was trained on selected design parameters to accurately predict shape performance and efficiently explore new design configurations. The ratio between maximum shear stress and maximum normal stress in the shear plane was considered the parameter to be optimized.