Real-time classification of aluminum metal scrap with laser-induced breakdown spectroscopy using deep and other machine learning approaches

Díaz‐Romero, Dillam Jossue; Eynde, Simon Van den; Sterkens, Wouter; Eckert, Alexander W.; Zaplana, Isiah; Goedemé, Toon; Peeters, Jef

doi:10.1016/j.sab.2022.106519

Cited by 20 publications

(3 citation statements)

References 43 publications

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“…Diaz-Romero et al used LIBS followed by a machine learning approach and/or a deep learning approach. 50 There were two LIBS setups: a gated lab-based system and the other an ungated industrial-based system. Both were discussed in the paper with the aid of schematic diagrams.…”

Section: Metalsmentioning

confidence: 99%

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

Clough,

Fisher,

Gibson

et al. 2023

J. Anal. At. Spectrom.

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Section: Metalsmentioning

confidence: 99%

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

Clough,

Fisher,

Gibson

et al. 2023

J. Anal. At. Spectrom.

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show abstract

“…The transferred model obtained 0.80 precision, 0.79 recall, and 0.79 F1-score. 26 Chen et al fine-tuned a pre-trained Inception-v3 convolutional neural network and utilized it in the LIBS rock classification based on elemental imaging. The classification accuracy was improved from 64% to 85% compared with a support vector machine classifier.…”

Section: Introductionmentioning

confidence: 99%

Accuracy improvement of laser-induced breakdown spectroscopy coal analysis by hybrid transfer learning

Chen,

Yan,

Kang

et al. 2023

Anal. Methods

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“…They found that surface cleaning improved classi cation accuracy and combined SVM with X-ray uorescence (XRF) technology for assessment, achieving the highest composition score of 97.228%. Dillam Jossue Díaz-Romero et al studied the real-time classi cation of aluminum metal waste using LIBS [23] . By combining BPNN and GHOSTNET to establish the dataset, they achieved a precision of 0.8, recall of 0.81, and F1-score of 0.80 within 9 ms.…”

Section: Introducementioning

confidence: 99%

Research on Automotive Scrap Metal Classification Method Using Laser-Induced Breakdown Spectroscopy and Two-Step Clustering Algorithm

Lin,

Dai,

Che

et al. 2023

Preprint

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In the recycling of scrap metal, the establishment of the classification database of recyclables has the advantages of fast classification speed and high analysis accuracy. However, the classification and recycling of unknown samples become highly significant due to the extensive variety of standard metal samples and the challenges in obtaining them. In this study, a method for multi-element classification of automotive scrap metals in general environmental conditions was achieved by utilizing Laser-Induced Breakdown Spectroscopy (LIBS) and Two-Step Clustering Algorithm (K-means, Hierarchical Clustering). The two unsupervised learning algorithms were employed to cluster the LIBS spectral data of 60 automotive scrap metal samples rapidly and hierarchically. Three rare metal elements and three elements for distinguishing metal categories were selected to meet the recycling requirements. After applying the MSC (Multiplicative Scatter Correction) to the spectral data for calibration, the initial clustering clusters were determined using the DB index, CH index, and silhouette coefficient. Then, the Kruskal-Wallis test was conducted on each cluster to check the significance. And the clusters that failed the test were split and reclustered until all clusters met the significance criterion (α=0.05). The accuracy of the proposed method for classifying the collected automotive scrap metals reached 97.6%. This indicates the great potential of this method in the field of automotive scrap metal classification.

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Real-time classification of aluminum metal scrap with laser-induced breakdown spectroscopy using deep and other machine learning approaches

Cited by 20 publications

References 43 publications

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

Accuracy improvement of laser-induced breakdown spectroscopy coal analysis by hybrid transfer learning

Research on Automotive Scrap Metal Classification Method Using Laser-Induced Breakdown Spectroscopy and Two-Step Clustering Algorithm

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