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Description
Due to environmental, economic sustainability, and political considerations, recycling processes are gaining heightened significance, focusing on substantially increasing the utilization of secondary raw materials. This paper advances the field by tackling the novel challenge of non-destructively analyzing mixed copper alloys. Building on our previous work in non-destructive analysis of different materials and metal alloys, this paper addresses the novel challenge of analyzing mixed metal alloys, which include similar alloys. The increased similarity among these mixtures poses a greater challenge and enhances their relevance.
In the recycling of copper alloys, online classification is not only concerned with categorizing individual alloys but also with identifying mixtures of these alloys and determining their composition ratios. We employ a non-destructive material analysis based on PGNAA using a High Purity Germanium (HPGe) detector. In our AI application, we utilize three classification methods, such as Maximum Log Likelihood, Neural Network (NN) and Convolutional Neural Network (CNN), to overcome this challenge. Furthermore, we demonstrate a significantly better performance in CNN classification of copper alloys compared to the current state-of-the-art, achieving a higher classification rate in only one-fifth of the time.
We evaluate the classification accuracy of each method and achieve nearly perfect results with less than one second of classification time. This demonstrates the possibility of online classification between mixed materials with even similar alloys.
| Minioral | Yes |
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| IEEE Member | No |
| Are you a student? | Yes |
