Estimation of Thickness Samples Using Gamma Scattering Techniques Based on Machine Learning Approach

Abstract

Gamma-ray scattering is a powerful method in the non-destructive testing field. Many researches related to gamma-ray scattering is being used in the world. Gamma-ray scattering can be used to determine thickness, structure as well as components in a material. Along with computer science, application of computer science in many scientific fields may constitute good achievements such as precision and speed of data analysis. In this paper, Machine learning is being used in gamma-ray scattering to determine thickness of material based on gamma-ray spectrum. To provide a dataset for machine learning, Monte Carlo was used for Ti, Mn, Fe, Co, Cu, Zn samples from 1mm to 50mm. In Machine learning, 8^th-degree polynomial regression method is used.