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
Affiliations
Vadi Su Yılmaz
Department of Electrical-Electronics Engineering, Atilim University, Incek Golbasi, Ankara 06830, Turkey
Kemal Efe Eseller
Department of Electrical-Electronics Engineering, Atilim University, Incek Golbasi, Ankara 06830, Turkey
Ozgur Aslan
Department of Mechanical Engineering, Atilim University, Incek Golbasi, Ankara 06830, Turkey
Emin Bayraktar
School of Mechanical and Manufacturing Engineering, ISAE-Supmeca-Paris, Saint Ouen, 93407 Paris, France
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.
