Machine vision system for automated spectroscopy

Ukwatta, Eranga; Samarabandu, Jagath; Hall, Mike R

doi:10.1007/s00138-011-0338-8

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…Machine vision technology is quite popular in industrial manufacturing, and there have been many studies on intelligence and automation in the industrial eld. 19,20 The experimental setup used is shown in Fig. 1.…”

Section: Instrumentationmentioning

confidence: 99%

Rapid quantitative analysis of raw rocks by LIBS coupled with feature-based transfer learning

Rao,

Ren,

Kong

et al. 2024

J. Anal. At. Spectrom.

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Section: Instrumentationmentioning

confidence: 99%

Rapid quantitative analysis of raw rocks by LIBS coupled with feature-based transfer learning

Rao,

Ren,

Kong

et al. 2024

J. Anal. At. Spectrom.

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“…An overview of industrial applications of LIBS and especially on development from 2014–2018 has been published ( Noll et al, 2018 ) addressing recent R&D activities in the field of automated LIBS: (a) combined use of in-line measured three-dimensional (3D) geometry information and LIBS analyses for high-speed sorting tasks of piece goods; (b) sorting of refractories; (c) identification of steel blooms in a rolling mill, and, as we will see later for the plastics case; and (d) inverse production application for the recovery of valuable materials from EoL electronic equipment. Also, other works on automated methods have been extensively reported ( Connemann et al, 2016 ; Rosenwasser et al, 2001 ; Rzecki et al, 2018 ; Ukwatta et al, 2012 ). Finally, LIBS-based imaging has promising applications in biology, medicine and geology ( Jolivet et al, 2019 ).…”

Section: Current Photonic Techniques Applied For Monitoring Of Plasticsmentioning

confidence: 99%

“…the plastics case; and (d) inverse production application for the recovery of valuable materials from EoL electronic equipment. Also, other works on automated methods have been extensively reported (Connemann et al, 2016;Rosenwasser et al, 2001;Rzecki et al, 2018;Ukwatta et al, 2012). Finally, LIBS-based imaging has promising applications in biology, medicine and geology (Jolivet et al, 2019).…”

Section: Libs Industrial Applicationsmentioning

confidence: 99%

Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling

et al. 2021

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In the increasingly pressing context of improving recycling, optical technologies present a broad potential to support the adequate sorting of plastics. Nevertheless, the commercially available solutions (for example, employing near-infrared spectroscopy) generally focus on identifying mono-materials of a few selected types which currently have a market-interest as secondary materials. Current progress in photonic sciences together with advanced data analysis, such as artificial intelligence, enable bridging practical challenges previously not feasible, for example in terms of classifying more complex materials. In the present paper, the different techniques are initially reviewed based on their main characteristics. Then, based on academic literature, their suitability for monitoring the composition of multi-materials, such as different types of multi-layered packaging and fibre-reinforced polymer composites as well as black plastics used in the motor vehicle industry, is discussed. Finally, some commercial systems with applications in those sectors are also presented. This review mainly focuses on the materials identification step (taking place after waste collection and before sorting and reprocessing) but in outlook, further insights on sorting are given as well as future prospects which can contribute to increasing the circularity of the plastic composites’ value chains.

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“…The algorithm is implemented by three parts: simulation of the set of spectra corresponding to different temperature and electron density, searching the best correlated pair of a model spectrum and an experimental one, and attributing the peaks with certain lines. Ukwatta et al [36] consider the problem of element detection as a multilabel classification problem, using support vector machines (SVMs) and artificial neural networks (ANNs) for multielement classification. The proposed algorithm is evaluated by using the LIBS image obtained from the experiment.…”

Section: Automatic Identification Of Emission Linesmentioning

confidence: 99%

Calibration Methods of Laser-Induced Breakdown Spectroscopy

Fu¹,

Jia²,

Wang³

et al. 2018

Calibration and Validation of Analytical Methods - A Sampling of Current Approaches

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Laser-induced breakdown spectroscopy (LIBS) has gained great attention over the past two decades due to its many advantages, such as needless sample preparation, capability of remote measurement and fast multielement simultaneous analysis. However, because of its inherent uncertainty features of plasma, it is still a big challenge for LIBS community worldwide to realize high sensitivity and accurate quantitative analysis. Currently, many chemometric analytical methods have been applied to LIBS calibration analysis, including univariate regression, multivariate regression, principal component regression (PCR), partial least squares regression (PLSR) and so on. In addition, appropriate sample and spectral pretreatment can effectively improve the analytical performance (i.e., limit of detection (LOD), accuracy and repeatability) of LIBS. In this chapter, we briefly summarize the progress of these calibration methods and their applications on LIBS and provide our recommendations.

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Machine vision system for automated spectroscopy

Cited by 11 publications

References 24 publications

Rapid quantitative analysis of raw rocks by LIBS coupled with feature-based transfer learning

Rapid quantitative analysis of raw rocks by LIBS coupled with feature-based transfer learning

Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling

Calibration Methods of Laser-Induced Breakdown Spectroscopy

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