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Master thesis : Classification of aluminum alloy scraps via spectral analysis: A deep learning approach to laser-induced breakdown spectroscopy

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Schwanen, Julien ULiège
Promotor(s) : Louppe, Gilles ULiège
Date of defense : 26-Jan-2024 • Permalink : http://hdl.handle.net/2268.2/19558
Details
Title : Master thesis : Classification of aluminum alloy scraps via spectral analysis: A deep learning approach to laser-induced breakdown spectroscopy
Author : Schwanen, Julien ULiège
Date of defense  : 26-Jan-2024
Advisor(s) : Louppe, Gilles ULiège
Committee's member(s) : Drion, Guillaume ULiège
Geurts, Pierre ULiège
Baudinet, Charles ULiège
Language : English
Number of pages : 137
Keywords : [en] Deep Learning
[en] Alloy Classification
[en] Laser-Induced Breakdown Spectroscopy (LIBS)
[en] Signal Processing
[en] Spectral Analysis
[en] Pattern Recognition
[en] Aluminum Waste
[en] Metal Recycling
[en] Material Identification
[en] Deep Neural Networks
[en] Alloy Characterization
[en] Spectroscopic Data Analysis
Discipline(s) : Engineering, computing & technology > Civil engineering
Funders : Université de Liège
Research unit : Laboratoire du GeMMe
Target public : Other
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] This work explores innovative methodologies in metal classification, focusing on
aluminum, within the GeMMe laboratory’s framework and the SALEMA project.
This research focuses on three main objectives: firstly, to maximize information
extraction from the complete spectral data for aluminum classification; secondly, to
deepen understanding of the set structure of the aluminum samples composed of
multiple Laser-Induced Breakdown Spectroscopy spectra; and thirdly, to enhance
classification techniques by integrating unsupervised datasets.
This work employs various deep learning models, including the Multi-Layer Percep tron (MLP), Residual Network (ResNet), Variational Autoencoder (VAE), and their
skip-connected variant. It compares them against GeMMe’s baseline in preprocessed
and non-preprocessed data scenarios.
Experimental findings suggest that a comprehensive analysis of the entire spectrum
of each Laser-Induced Breakdown Spectroscopy spectra contributes to an improved
weighted F1 of 2% compared to the GeMMe’s PICKIT baseline. Secondly, acknowl edging the set structure of the samples, further refines the classification, particularly
with the application of deep set techniques. This approach has shown a significant
increase in weighted F1 score of 11% compared to the GeMMe’s PICKIT baseline.
Additionally, the introduction of unsupervised datasets in the context of generative
models, particularly the skip-connected VAE, markedly enhanced the weighted F1
of 1.3%, surpassing the previously mentioned approach, especially for smaller sets.
This was achieved by balancing the generative and the classification loss.
The model’s generalization capabilities were tested on unseen datasets, showing
weighted F1 over 85%, yet indicating challenges in samples with fewer spectra.


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Author

  • Schwanen, Julien ULiège Université de Liège > Master ing. civ. inf. fin. spéc.int. sys.

Promotor(s)

Committee's member(s)

  • Drion, Guillaume ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
    ORBi View his publications on ORBi
  • Geurts, Pierre ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
    ORBi View his publications on ORBi
  • Baudinet, Charles ULiège Université de Liège - ULiège > Département ArGEnCo > Département ArGEnCo
    ORBi View his publications on ORBi
  • Total number of views 26
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