The performance of various machine learning methods has been tested for the material classification and the error-free result has been obtained with fine KNN.
📌 3 Key Takeaways
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Objective & Hypothesis:In industrial processes, dimension measurement and classification of metallic materials at the macroscopic level are performed for various purposes with various methodsIn this study, dimensions of metal materials belonging to three different types such as copper, aluminum and steel have been obtained by using image processing, and their classification has been performed by using machine learning.
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Key Findings & Data:For the size measurement, over 99.5% accuracy has been achieved based on the quality of the camera module used and the image quality received.
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Conclusion & Impact:The performance of various machine learning methods has been tested for the material classification and the error-free result has been obtained with fine KNN.
In industrial processes, dimension measurement and classification of metallic materials at the macroscopic level are performed for various purposes with various methodsIn this study, dimensions of metal materials belonging to three different types such as copper, aluminum and steel have been obtained by using image processing, and their classification has been performed by using machine learning. For the size measurement, over 99.5% accuracy has been achieved based on the quality of the camera module used and the image quality received. The performance of various machine learning methods has been tested for the material classification and the error-free result has been obtained with fine KNN.
YENER, T., SAKACI, F. H., YENER, Ş. Ç. (2022). Dimension Measurement and Classification of Metallic Materials Using Image Processing and Machine Learning. Journal of Smart Systems Research, Cilt: 3 - Sayı: 2, 61–69. https://ajindex.com/articles/873643
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