Inclusion mapping and estimation of inclusion contents in ferrous materials by fast scanning laser-induced optical emission spectrometry

Kuß, Heinz-Martin; Mittelstaedt, Horst; Mueller, Gregor

doi:10.1039/b503277f

Cited by 43 publications

(15 citation statements)

References 27 publications

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“…The quantified chemical composition data on nonmetallic inclusions obtained using LIBS were compared to the corresponding results obtained from the scanning electron microscope/energy dispersive X-ray spectroscopy (SEM/EDS) analysis, a conventional method used for steel cleanness assessment, and were proved accurate. Kuss [18] estimated the content of nonmetallic inclusions in ferrous materials using LIBS; signals of inclusion-related elements were evaluated using different statistical algorithms to distinguish the threshold intensity, at which laser pulses focused on the nonmetallic inclusions and the elements dissolved in the metallic matrix. LIBS scanning measurements were performed using samples displaying segregation and decarburization by Bigne [19].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the inclusion size distribution information acquired from SEM/EDS was used to determine the threshold intensity distinguishing insoluble and soluble aluminum, which is different from Kuss's method [18]. Finally, on the basis of a statistical mathematical model, the globular oxide inclusion ratings of carbon steel and bearing steel were assessed in detail.…”

Section: Introductionmentioning

confidence: 99%

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Characterization of the globular oxide inclusion ratings in steel using laser-induced breakdown spectroscopy

Zhang¹,

Jia

Yang

et al. 2016

Front. Phys.

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Grade assessment of steel is generally performed via the metallographic method, which is timeconsuming and is not able to provide the elemental distribution information. In this paper, we present a method to measure the globular oxide inclusion ratings in steel using laser-induced breakdown spectroscopy (LIBS). The measurement is performed in two basic steps: steel samples are polished using metallographic sand paper and the Al 2 O 3 inclusion number and size distribution in a marked area are observed using scanning electron microscope/energy dispersive X-ray spectroscopy (SEM/EDS) for further LIBS scanning analysis. The threshold intensity that distinguishes soluble aluminum and insoluble aluminum inclusions is determined using LIBS combined with the SEM/EDS statistical data. Carbon steel (the sample number is S9256) and bearing steel (the sample number is GCr15) are analyzed in scanning mode, and the number of Al 2 O 3 inclusions in different size ranges is obtained from the statistical information derived from the Al 2 O 3 size calibration curve. According to heavy and thin series for globular oxide inclusions grade assessment, the method we propose is comparable to the traditional metallographic method in terms of accuracy; however, the process is simplified and the measurement speed is significantly improved.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterization of the globular oxide inclusion ratings in steel using laser-induced breakdown spectroscopy

Zhang¹,

Jia

Yang

et al. 2016

Front. Phys.

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“…Other interesting and informative research papers on the subject of metallurgical analysis using LIBS have also been published. [11][12][13][14][15][16] Comparing with similar research about this topic, the present work investigated LIBS instrumental parameters, such as lens-to-sample distance, delay time, background gas and pressure and laser pulse energy, in detail. With optimized experimental condition, analytical performance of LIBS and Spark-OES, such as precision, accuracy and detection limits were compared.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Analytical Performances of Laser-Induced Breakdown Spectroscopy and Spark-OES

et al. 2014

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Most Czerny-Turner based LIBS systems are used widely. In the present work, an instrumental setup based on Paschen-Runge is introduced, and analytical parameters are optimized for LIBS. With optimum parameters, analytical performance measures, such as precision, accuracy and detection limit, are compared for LIBS and Spark-OES. The results show that the analytical accuracy obtained by LIBS is similar to that obtained with Spark-OES, while the precision and detection limit are notably close to those of Spark-OES. Thus, LIBS can be used satisfactorily for process analysis in the metallurgy industry.

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“…The segmented defects can be characterized by their two dimensional shape, and the size distribution can be estimated for the entire steel volume stereologically. If a further chemical analysis is desired, it is possible to ask the system for a map with interesting inclusions, which can be selectively processed by a laser-induced breakdown spectroscopy system (LIBS), 21,22) or a microscope optic. To give a better approximation of the cleanliness and to reconstruct the three-dimensional morphology, in the next processing step the steel surface is milled off, removing a 10 μm chip, and the image recording step is repeated.…”

Section: Introductionmentioning

confidence: 99%

Rapid Inclusion and Defect Detection System for Large Steel Volumes

et al. 2013

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The amount, chemical composition and distribution of nonmetallic inclusions are important factors when determining steel quality. Therefore, in recent years, a great deal of effort has gone into developing robust detection systems for nonmetallic inclusions. Various methods have been suggested, but most of them require extensive sample preparation. As a result, these methods are only suitable for analyzing micro-inclusions. For estimating the macrocleanliness, these methods are too slow because, due to the rarity of these kinds of inclusions, huge sample volumes are needed. To overcome this problem, we propose a new detection methodology that can detect inclusions and defects, like microcracks and shrinkage holes, at a range of between 5 μm and several thousands of a μm. At the same time, it is fast enough to process samples of very large steel volumes of 300 × 120 × 90 mm³ in size. Similar to the classic metallographic approach using a microscope and polished steel surfaces, the steel surface of a sample is recorded by a moving CCD sensor with a pixel size of 2.75 μm. Inclusions are detected using image processing techniques. Then, the steel surface is milled off, removing a 10 μm chip, and the recording step is repeated. Processing the whole sample in this way allows us to reconstruct the three-dimensional shape of inclusions and other defects and gives us information about the spatial distribution of these inclusions. In this paper, we describe this system in greater detail and describe the initial results of our approach.

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Inclusion mapping and estimation of inclusion contents in ferrous materials by fast scanning laser-induced optical emission spectrometry

Cited by 43 publications

References 27 publications

Characterization of the globular oxide inclusion ratings in steel using laser-induced breakdown spectroscopy

Characterization of the globular oxide inclusion ratings in steel using laser-induced breakdown spectroscopy

Comparison of the Analytical Performances of Laser-Induced Breakdown Spectroscopy and Spark-OES

Rapid Inclusion and Defect Detection System for Large Steel Volumes

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