Classification of Different Recycled Rubber-Epoxy Composite Based on their Hardness Using Laser-Induced Breakdown Spectroscopy (LIBS) with Comparison Machine Learning Algorithms

Vadi Su Yılmaz , Kemal Efe Eseller , Ozgur Aslan , Emin Bayraktar (1, 2)

1 QUARTZ - Laboratoire QUARTZ
2 ISAE-Supméca - ISAE-Supméca Institut Supérieur de Mécanique de Paris

This paper aims toward the successful detection of harmful materials in a substance by integrating machine learning (ML) into laser-induced breakdown spectroscopy (LIBS). LIBS is used to distinguish five different synthetic polymers where eight different heavy material contents are also detected by LIBS. Each material intensity-wavelength graph is obtained and the dataset is constructed for classification by a machine learning (ML) algorithm. Seven popular machine learning algorithms are applied to the dataset which include eight different substances with their wavelength-intensity value. Machine learning algorithms are used to train the dataset, results are discussed and which classification algorithm is appropriate for this dataset is determined.

Keywords: Recycled Rubber- Polymers; hardness; machine learning; classification; LIBS

Domains: Engineering Sciences [physics]

Vadi Su Yılmaz, Kemal Efe Eseller, Ozgur Aslan, Emin Bayraktar. Classification of Different Recycled Rubber-Epoxy Composite Based on their Hardness Using Laser-Induced Breakdown Spectroscopy (LIBS) with Comparison Machine Learning Algorithms. MDPI , 2023, ⟨10.3390/inventions8020054⟩. ⟨hal-04019384⟩