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Fast identification of steel bloom composition at a rolling mill by laser-induced breakdown spectroscopy
Accepted Manuscript Fast identification of steel bloom composition at a rolling mill by LIBS elemental analysis
Volker Sturm, Christoph Meinhardt, Rüdiger Fleige, Cord FrickeBegemann, Jens Eisbach PII: DOI: Reference:
S0584-8547(17)30252-5 doi: 10.1016/j.sab.2017.08.009 SAB 5290
To appear in:
Spectrochimica Acta Part B: Atomic Spectroscopy
Received date: Revised date: Accepted date:
31 May 2017 14 August 2017 14 August 2017
Please cite this article as: Volker Sturm, Christoph Meinhardt, Rüdiger Fleige, Cord Fricke-Begemann, Jens Eisbach , Fast identification of steel bloom composition at a rolling mill by LIBS elemental analysis, Spectrochimica Acta Part B: Atomic Spectroscopy (2017), doi: 10.1016/j.sab.2017.08.009
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Fast Identification of Steel Bloom Composition at a
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Rolling Mill by LIBS Elemental Analysis
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Volker Sturm a,*, Christoph Meinhardt b, Rüdiger Fleige a, Cord Fricke-Begemann a, Jens Eisbach c
Fraunhofer-Institut für Lasertechnik ILT, Steinbachstrasse 15, 52074 Aachen, Germany
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RWTH Aachen University, Lehrstuhl für Lasertechnik, Steinbachstrasse 15, 52074 Aachen,
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Deutsche Edelstahlwerke Specialty Steel GmbH & Co. KG, Obere Kaiserstrasse, 57078 Siegen,
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Germany
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Germany
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*Corresponding Author.
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E-mail address: [email protected] (V. Sturm)
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ACCEPTED MANUSCRIPT ABSTRACT Laser-induced breakdown spectroscopy (LIBS) is applied for the elemental analysis of steel blooms in a rolling mill. The 2-3 tons steel blooms with superficial scale are transported in a sequence on a roller table to successive processing steps. Laser ablation of the scale and the
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analysis of the subsurface bulk steel is carried out using the same laser in less than 50 s during scheduled stop times of the roller table. Up to 14 elements such as Ni, Cr, and Mo are measured
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for several hundreds of blooms of low and high alloy steel during routine production. The
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comparison of the measured with the nominal compositions, results in root mean square errors of prediction in the range of 0.01-0.2 m.-%. The rolling sequence is clearly reflected by the LIBS
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measurement of the individual blooms demonstrating the feasibility for material identification.
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Identification rates are estimated from computer simulations by permutation of the LIBS
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measured values and the reference values from the rolling sequence.
LIBS
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Steel analysis
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Keywords:
Material identification Rolling mill Production control
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ACCEPTED MANUSCRIPT 1. Introduction A variety of hundreds of different steel grades are typical in rolling mills producing specialty steels. A fast identification of the elemental composition of steel blooms is of interest to improve the production control and to discover deviations e.g. mix-ups of steel grades as early as possible
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in the production line.
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For laser-induced breakdown spectroscopy (LIBS), a pulsed laser beam is focused onto the material to be analyzed. Ablated material is excited in the generated plasma plume and elemental
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analysis is carried out by optical atomic emission spectroscopy (OES). After calibration with
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known reference samples, the elemental composition of unknown samples can be determined by measuring the spectral line intensities or intensity ratios of the regarded elements. Several papers
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describe different aspects of LIBS and its application for the analysis of a variety of materials [1-
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4], including steel samples [5-11], and steel billets [12-14]. In contrast to laboratory techniques which require mechanical sampling, transport and preparation of the samples, the LIBS
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technique can measure directly at the production site, e.g. [11,15-17]. For the application
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described here, on the one hand LIBS enables a faster verification of nominal steel grade composition at an earlier stage of the production line. On the other hand, the analytical
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performance of LIBS measurements under such conditions cannot compete completely with a high-end laboratory analysis. However what does a high-performance analysis help if it comes too late? One challenge for the analysis of steel blooms is that the steel to be analyzed is covered by a thick scale layer with a different composition. In special cases of limited material variances it may be possible to analyze the scale and to transform the results to the bulk composition as
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ACCEPTED MANUSCRIPT described for a remote measurement of hot blooms [12,14]. But in general the removal of the scale layer prior to the analysis of the bulk material will be required. Fig. 1 shows the scale
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surface and two laser craters reaching the subsurface bulk steel.
Fig. 1. Example of a sample cut from a steel bloom with original scale surface and two laser
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craters with a diameter and depth of approx. 1.5 mm.
The same laser (although parameters are changed automatically by the control software) is
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used for a local ablation of the scale before the bulk steel is analyzed at this spot as described in general in [18,19]. In [18] production control samples (“lollipop” samples from steel melt
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sampling) are measured on a sample stand in the laboratory. These production control samples
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exhibit thinner scale layers of a few hundred microns with smaller thickness variations. The variance of the scale layers of the steel blooms – thicknesses (typically up to 1 mm and sometimes more) and composition – is higher than in the former papers and it is a challenge for a fast bulk analysis. In a recent paper [20], experimental parameters, the scale removal schemes as well as suitable laser parameters are described in detail for such scale layers. In this paper, the analytical performance is investigated by series of measurements which are taken during routine production with a LIBS instrument installed on-site for feasibility studies. To our knowledge,
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ACCEPTED MANUSCRIPT there is no other analytical method proofed to measure the hidden steel composition under such scale surfaces within one minute at a rolling mill environment.
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2. Materials and Methods
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The Nd:YAG laser is a Q-switched diode-pumped oscillator followed by a flash lamp pumped
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amplifier emitting pulses of 20-40 ns at a wavelength of 1064 nm. It is focused by a lens with
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300 mm focal length. Fig. 2a shows the standard situation with fixed focal position. The contact of the LIBS plasma with the crater side wall adds impurities of scale into the plasma and these
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interfere the analysis of the bulk steel. Therefore the focal spot is slightly moved by a motorized
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mirror during the scale ablation step in order to enlarge the “cleaned” spot, see Fig. 2b. This
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avoids or – at least – reduces side effects from the scale at the crater wall [19].
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ACCEPTED MANUSCRIPT Fig. 2. (a) Schematic situation for standard LIBS with fixed focal position. (b) situation with a moving laser spot during the ablation step prior to analysis. Further experimental data are described in [20]. The difference in the detection set-up is that a vacuum Paschen-Runge spectrometer with solid-state detectors (OBLF GmbH, Witten) is used.
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The spectrometer has been adapted and modified according to the requirements for LIBS detection by OBLF. The LIBS spectra are registered without time gating by direct imaging of the
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plasma plume to the entrance slit of the spectrometer. Fig. 3 gives a section of the spectrum of a
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pure iron sample together with the spectra of a low alloy and a high alloy steel bloom in the vicinity of the Ni I 341.47 nm spectral line. As well-known from LIBS and OES, the spectral
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lines are numerically integrated and the integral values or ratios of these are taken for evaluation.
Fig. 3. Cut-out of the spectrum of a pure iron sample (Ni < 0.001 m.-%) compared to spectra of steel blooms with Ni mass fractions of 1.5 m.-% and 8.01 m.-%, respectively. The LIBS instrumentation consists of the laser, spectrometer, beam guiding optics, argon inert gas handling and control unit. The instrument is installed in a cabin and it has an access port to the side of the roller table with the steel blooms. Although the spectrometer is sensitive at the
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ACCEPTED MANUSCRIPT VUV range of the carbon line at 193 nm, the detection of this line and therefore the measurement of carbon was presumably precluded by residual air-absorption in the beam path. The installation site is laser shielded. For this study, the degree of automation is adapted to perform efficiently the measurement series and to comply with the cycle time of the production. The measurement is
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started manually, when the bloom is in position, but the subsequent steps runs automatically: the
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sensor head (see Fig. 4) extends from a stand-by position to the side face of the bloom according
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to an integrated distance sensor, control of the gas purging, the laser and motorized mirror during the scale ablation step, control of laser settings for ablation and analysis, spectrometer and
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spectra read-out during the analyzing step, return of the sensor head and other devices to stand-
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Fig. 4. Steel bloom (roughly 0.3 x 0.3 x 4 m3) on the roller table. The extendible sensor head is
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in stand-by position before the measurement (a), extended during the LIBS measurement (b) and
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returned to stand-by when the steel bloom is moving forward (c).
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3. Results and discussion
3.1. Calibration with reference samples
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The LIBS analyzer has been calibrated with a set of 30 reference samples (“calibration sample
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set”) provided by Deutsche Edelstahlwerke Specialty Steel with reference analysis, i.e. mass fractions wa in m.-% for the elements a. The reference samples are sawed out of blooms. They
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are without scale due to the preparation. Nevertheless, the calibration measurements are performed with the ablation step included in order to measure with the same laser parameters for calibration and analysis. For these measurements, the samples are fixed in a holder in front of the sensor head. Examples of univariate analytical evaluation curves [22], i.e. reference analysis ratios vs. LIBS spectral intensity ratios, are given in Fig. 5 for Cr, Ni, Mo, Cu and Fe for the total calibration range (see “min…max” in Table 1 for different elements). The Fe mass fractions, wFe,
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ACCEPTED MANUSCRIPT are determined from the polynomial fitting curve in Fig. 5f, where the abscissa values are calculated from the measured Cr/Fe and Ni/Fe mass fraction ratios as given in the graph. Each of the Cr/Fe and Ni/Fe mass fraction ratios are calculated from the corresponding LIBS intensity ratios by their analytical evaluation functions. The abscissa values (1 – Cr/Fe – Ni/Fe) are an
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approximation according to the 100% summation for the matrix element Fe and they include
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only the major alloys Cr and Ni. For our data base it works very well, but further elements can be
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included if required.
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Fig. 5. Analytical evaluation curves for the calibration sample set: (a) Cr, (b) Ni, (d) Mo and
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(e) Cu. For Ni, the low alloy range curve is given in graph (c). Iron is determined according to curve (f) from the measured Cr/Fe and Ni/Fe ratios. The spectral lines are denoted by the
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element symbol followed by the wavelength in units of 0.1 nm.
3.2. Comparison of LIBS vs. reference analysis In more detail, there are different ratios of spectral lines used for low and high alloy steel as common in spectral analysis. For example, in Fig. 5c the low alloy range for Ni is shown.
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ACCEPTED MANUSCRIPT Additionally, in some cases mass fraction ratios (wa/wFe)j are evaluated from different line ratios j and their average value <(wa/wFe)j> is taken for more robust results. Finally, the mass fractions, wa, (in m.-%) are calculated by multiplying (wa/wFe) with wFe.
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In Fig. 6 the Cr mass fractions determined by LIBS are compared with the reference values.
Fig. 6. LIBS vs. reference analysis of Cr for the calibration sample set. (a) total calibration range 0.1 to 17 m.-%, RMSEP 0.15 m.-%, (b) low alloy range 0.1 to 2.5 m.-%, enlarged detail from (a), RMSEP 0.035 m.-%.
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To assess the congruence of the measured values with the reference analysis, the root mean square error of prediction, RMSEP, is taken. It is defined for element a as [19]:
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1 n 2 RMSEPa wa, LIBS;i wa, ref; i n i 1
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where wa,LIBS;i is the mass fraction predicted from the analytical evaluation curve, wa,ref;i is the reference mass fraction of sample i and n is the total number of samples of the verification
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set. Table 1 summarizes the RMSEP values for the calibration samples taking the same sample set for verification which in general is a first lower estimation. For the on-site LIBS analysis, the
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batch reference analysis of the steel blooms are used for verification. It is clear that the resulting
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RMSEP values are an upper estimation because of the production environment with varying
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scale layers and bloom positions, vibrations, humidity, drifts due to longer measurement periods of several days as well as differences of the actual compositions to the batch analysis within the
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steel grade standard specifications. In Table 1, only elements are listed for which reference
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values are given for all 481 blooms - with exception of C which was not measured. In general, the calibration ranges should cover the ranges of measured steel blooms. For some elements (e.g.
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Cr, Mn, Cu) this is not exactly the case, see Table 1. The reasons are (a) the ranges of the steel blooms vary due to the actual rolling sequence of the routine production which was not exactly known beforehand at the time of the calibration, and (b) the restricted availability of calibration samples with reference analysis and arbitrary composition at that time. Further detected elements are Al, Nb, Pb, Sn, Ti, V and W but these are omitted here due to the different reference data basis. In general, other elements are possible due to the nearly continuous spectra of the spectrometer with solid-state detectors.
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calibr. samples N=30 steel blooms N=481
in m.-% min…max RMSEP min…max RMSEP
Ni 0.05...13 0.031 0.09...8.0 0.16
Cr 0.1...17 0.15 0.13...19 0.26
Mo 0.02...2.5 0.021 0.02...1.3 0.11
Mn 0.43...1.6 0.061 0.42...2.8 0.16
Cu 0.02...0.36 0.0091 0.03...3.1 0.1
Co 0.005...0.11 0.0024 0.008...0.09 0.004
Si 0.08...2.9 0.059 0.06...1.1 0.15
Fe 65…98.3 0.74 68…98.1 0.63
Table 1. Range of mass fractions w (“min…max”) for calibration and steel bloom
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measurements together with RMSEP values.
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3.3. LIBS measurements during production
LIBS measurements were taken for 481 steel blooms during routine production on several days
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with the instrumentation described above. According to the rolling sequence, the steel blooms on
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the roller table are coming in different batch quantities from a few up to roughly one hundred blooms which have the same nominal material and reference analysis. The measured mass
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fractions of each bloom in m.-% are plotted in Fig. 7 for Ni (a,c), Cr (b) and Fe (d) together with
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the nominal reference mass fractions. Each bloom has an individual LIBS value but there is only a single reference value for the blooms of the same batch, i.e. for these sections the (green)
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reference values are horizontal straight lines in Fig. 7.
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Fig. 7. Direct tracking of the rolling sequence and comparison of the LIBS measurements
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(black) of the mass fractions of Ni (a,c), Cr (b), and Fe (d) with the nominal reference values
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according to a batch analysis (green) of the steel bloom material. For clarity, in (a) and (b) the curves are separated by an offset of the right ordinate scale. (c) data for Ni plotted without offset and a logarithmic scale showing the differences in detail. The rolling sequence is clearly reflected by the LIBS measurement of the blooms and the mass fractions coincide well with the reference values. Nevertheless the observed limitations for lower
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ACCEPTED MANUSCRIPT mass fractions are given roughly by the RMSEP values of Table 1. For some other elements such as Si the deviations are greater. The reasons are not clarified in detail up to now. In order to assess the stability of the LIBS instrument, monitoring steel samples are repetitively measured during the on-site serial measurements by fixing them manually in a sample holder in
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front of the optical sensor head. For example, Fig. 8 demonstrates for two monitoring samples the relative good performance with respect to the working environment. The mean values /
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standard deviations (SD) / relative SDs are (for monitoring sample S1 and S2, respectively) for
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Cr (S1): 15.8 m.-% / 0.79 m.-% / 5.0 %, Cr (S2): 0.77 m.-% / 0.042 m.-% / 5.4 %,
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Ni (S1): 1.50 m.-% / 0.04 m.-% / 2.6 %,
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Ni (S2): 1.97 m.-% / 0.06 m.-% / 2.9 %.
Fig. 8. Repetitive measurements of monitoring steel samples S1 and S2 during short breaks of the measurement series of Fig. 7. The abscissa value, i.e. the particular time of the monitoring, is the corresponding adjacent number of the LIBS measurement of Fig. 7. The reference values from the batch analysis are given by the lines in the graph.
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3.4. Identification of steel blooms by the elemental composition For identification, the measured mass fraction wa,LIBS;i is compared with the nominal mass
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fraction wa,ref;i according to the rolling sequence. The following differences divided by their
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mean values are calculated for each steel bloom i and element a according to
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Da,i 2 wa, LIBS;i wa, ref; i / wa, LIBS;i wa, ref; i
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If any of these values is greater than the corresponding threshold value, Da,i,thr , the steel bloom is identified as incorrect, e.g. a mix-up occurred. The threshold values Da,i,thr of the elements
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included for identification are for Ni 1.3, Cr 0.54, Mo 1.3, Mn 1.2, Cu 1.4, Co 0.65, Si 1.61, V
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1.0, and Fe 0.06.
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One criterion to assess the identification of steel blooms by LIBS, is to verify if the LIBS measurements can detect the batch changes in a rolling sequence. In this case, each measured
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mass fraction wa,LIBS;i is compared with the nominal mass fraction wa,ref;i-1 of the previous steel
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bloom i-1. The result of this evaluation is that 24 of the 26 changes within the measured sequence are detected while the two non-recognized changes (positive-false detection) are only
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minor changes in the composition. No false-positive changes are detected (“false alarm”), i.e. no change is signalized for any of the other (481-26) = 455 blooms. This is important since falsepositive signals would lead to false alarms when the technique is used for mix-up detection. identification, case correct, positive-positive correct, false-false error, false-positive error, false-negative
total detected percent. 28 453 28 453 100% 202 908 174 263 86% 28 453 0 0% 202 908 28 645 14%
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ACCEPTED MANUSCRIPT Table 2. Identification of steel blooms by the LIBS measured mass fractions, simulated through a computational permutation of the assigned reference values.
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Another evaluation is carried out by permutation of the LIBS measured values to the reference
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values of the steel blooms in a computer simulation. For the given rolling sequence, there are
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481 x 481 = 231 361 permutations from which 202 908 are simulated mix-ups (reference values of another bloom are incorrectly assigned to the LIBS measured bloom) and 28 453 are identical
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assignments (permutations with a bloom with same batch reference values). Table 2 gives the
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result that 86 % of the incorrect assignments due to the permutation (mix-ups) are detected by the LIBS measurements (correct false-false identification). There are no erroneous false-positive
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detections (“false alarms”) from the total number of 28 453 identical assignments. About 14 % of
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the simulated mix-ups are not detected (false-negative). A detailed view exhibit that these are either very similar steel grades or the majority (> 52 %) of these steel grades are not
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distinguishable on the basis of the measured elements and their overlaps of the grade
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specifications. Every considerable mix-up, e.g. a low alloy with a high alloy bloom, is detected in the measured sequence according to the simulation. These results demonstrate that an
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identification of blooms with significantly different compositions is feasible. Nevertheless the inclusion of a carbon measurement is desirable for a further improvement.
4. Conclusions
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ACCEPTED MANUSCRIPT The subsurface bulk material of steel blooms with superficial scale is analyzed by LIBS within 50 s including the local ablation of the scale and the analysis of the steel composition. The LIBS instrument has been installed temporarily on-site in a rolling mill and its performance is tested in measurement series at 481 steel blooms during routine production. Based on the calibration with
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30 reference samples (RMSEP 0.01-0.15 m.-%), the mass fractions of the low and high alloy
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steel blooms are determined directly on the roller table, i.e. the tracking of the rolling sequence is
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demonstrated. The rolling sequence is clearly reflected by the LIBS measurement of the individual blooms and the mass fractions coincide well with the batch reference values. The
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observed limitations for lower mass fractions are given roughly by the RMSEP values of about
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0.1-0.2 m.-% when compared to the batch reference values.
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The feasibility for identification is assessed by the detection of 24 out of 26 batch changes in the rolling sequence within the measured series – whereupon the residual two changes are only
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minor material changes. Computer simulations by permutation of the LIBS measured values and
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the reference values from the rolling sequence lead to an 86 % identification rate of mix-ups including all crucial mix-ups of very different steel grades. The non-detected 14 % of simulated
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mix-ups are very similar steel grades or grades which are not distinguishable by the measured
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elements and their overlaps of the grade specifications. These results demonstrate that an identification of blooms with significant different compositions is feasible, although carbon was not measured presumably due to constraints of the installed instrument. Further studies will target on long-term instrument performance and carbon measurements will be studied in detail from which a further improvement of the identification rates is expected.
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ACCEPTED MANUSCRIPT Acknowledgment The work was co-financed by the European Union, the Federal State of North-Rhine Westphalia: “Europäischer Fonds für regionale Entwicklung (EFRE) für NRW” (grant number 300113002) and the Fraunhofer Society. We gratefully acknowledge the co-operation with D. Siegmund
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(OBLF, Witten), H. Zouak, L. Peter (Xox, Aachen), C. Gehlen (LSA, Aachen) for the design and
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set-up of the LIBS instrument.
A.W. Miziolek, V. Palleschi, I. Schechter, Laser-induced breakdown spectroscopy,
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[19] German Patent, Analyzing solid materials with laser emission spectrometry, DE 103 61
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727 B3 (2005).
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[20] C. Meinhardt, V. Sturm, R. Fleige, C. Fricke-Begemann, R. Noll, Laser-induced breakdown spectroscopy of scaled steel samples taken from continuous casting blooms,
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Spectrochim. Acta B At. Spectrosc. 123 (2016) 171–178. https://doi.org/10.1016/j.sab.2016.08.013. [21] K.E. Esbensen, Multivariate Data Analysis, 5th ed., Camo Process AS, Oslo, 2002, p 159.
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ACCEPTED MANUSCRIPT Title: Fast Identification of Steel Bloom Composition at a Rolling Mill by LIBS Elemental Analysis Authors: Volker Sturm, Christoph Meinhardt, Rüdiger Fleige, Cord Fricke-Begemann, Jens Eisbach
Highlights
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LIBS elemental analysis tracks the composition of steel blooms on a roller table Laser ablation of scale and LIBS analyses the steel under the superficial scale Up to 14 elements determined without sampling and laboratory analysis within 50 s Hundreds of blooms measured during routine production Comparison with the rolling sequence shows the feasibility for material identification
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Volker Sturm, Christoph Meinhardt, Rüdiger Fleige, Cord FrickeBegemann, Jens Eisbach PII: DOI: Reference:
S0584-8547(17)30252-5 doi: 10.1016/j.sab.2017.08.009 SAB 5290
To appear in:
Spectrochimica Acta Part B: Atomic Spectroscopy
Received date: Revised date: Accepted date:
31 May 2017 14 August 2017 14 August 2017
Please cite this article as: Volker Sturm, Christoph Meinhardt, Rüdiger Fleige, Cord Fricke-Begemann, Jens Eisbach , Fast identification of steel bloom composition at a rolling mill by LIBS elemental analysis, Spectrochimica Acta Part B: Atomic Spectroscopy (2017), doi: 10.1016/j.sab.2017.08.009
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Fast Identification of Steel Bloom Composition at a
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Rolling Mill by LIBS Elemental Analysis
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Volker Sturm a,*, Christoph Meinhardt b, Rüdiger Fleige a, Cord Fricke-Begemann a, Jens Eisbach c
Fraunhofer-Institut für Lasertechnik ILT, Steinbachstrasse 15, 52074 Aachen, Germany
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RWTH Aachen University, Lehrstuhl für Lasertechnik, Steinbachstrasse 15, 52074 Aachen,
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Deutsche Edelstahlwerke Specialty Steel GmbH & Co. KG, Obere Kaiserstrasse, 57078 Siegen,
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Germany
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Germany
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*Corresponding Author.
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E-mail address: [email protected] (V. Sturm)
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ACCEPTED MANUSCRIPT ABSTRACT Laser-induced breakdown spectroscopy (LIBS) is applied for the elemental analysis of steel blooms in a rolling mill. The 2-3 tons steel blooms with superficial scale are transported in a sequence on a roller table to successive processing steps. Laser ablation of the scale and the
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analysis of the subsurface bulk steel is carried out using the same laser in less than 50 s during scheduled stop times of the roller table. Up to 14 elements such as Ni, Cr, and Mo are measured
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for several hundreds of blooms of low and high alloy steel during routine production. The
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comparison of the measured with the nominal compositions, results in root mean square errors of prediction in the range of 0.01-0.2 m.-%. The rolling sequence is clearly reflected by the LIBS
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measurement of the individual blooms demonstrating the feasibility for material identification.
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Identification rates are estimated from computer simulations by permutation of the LIBS
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measured values and the reference values from the rolling sequence.
LIBS
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Steel analysis
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Keywords:
Material identification Rolling mill Production control
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ACCEPTED MANUSCRIPT 1. Introduction A variety of hundreds of different steel grades are typical in rolling mills producing specialty steels. A fast identification of the elemental composition of steel blooms is of interest to improve the production control and to discover deviations e.g. mix-ups of steel grades as early as possible
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in the production line.
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For laser-induced breakdown spectroscopy (LIBS), a pulsed laser beam is focused onto the material to be analyzed. Ablated material is excited in the generated plasma plume and elemental
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analysis is carried out by optical atomic emission spectroscopy (OES). After calibration with
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known reference samples, the elemental composition of unknown samples can be determined by measuring the spectral line intensities or intensity ratios of the regarded elements. Several papers
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describe different aspects of LIBS and its application for the analysis of a variety of materials [1-
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4], including steel samples [5-11], and steel billets [12-14]. In contrast to laboratory techniques which require mechanical sampling, transport and preparation of the samples, the LIBS
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technique can measure directly at the production site, e.g. [11,15-17]. For the application
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described here, on the one hand LIBS enables a faster verification of nominal steel grade composition at an earlier stage of the production line. On the other hand, the analytical
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performance of LIBS measurements under such conditions cannot compete completely with a high-end laboratory analysis. However what does a high-performance analysis help if it comes too late? One challenge for the analysis of steel blooms is that the steel to be analyzed is covered by a thick scale layer with a different composition. In special cases of limited material variances it may be possible to analyze the scale and to transform the results to the bulk composition as
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ACCEPTED MANUSCRIPT described for a remote measurement of hot blooms [12,14]. But in general the removal of the scale layer prior to the analysis of the bulk material will be required. Fig. 1 shows the scale
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surface and two laser craters reaching the subsurface bulk steel.
Fig. 1. Example of a sample cut from a steel bloom with original scale surface and two laser
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craters with a diameter and depth of approx. 1.5 mm.
The same laser (although parameters are changed automatically by the control software) is
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used for a local ablation of the scale before the bulk steel is analyzed at this spot as described in general in [18,19]. In [18] production control samples (“lollipop” samples from steel melt
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sampling) are measured on a sample stand in the laboratory. These production control samples
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exhibit thinner scale layers of a few hundred microns with smaller thickness variations. The variance of the scale layers of the steel blooms – thicknesses (typically up to 1 mm and sometimes more) and composition – is higher than in the former papers and it is a challenge for a fast bulk analysis. In a recent paper [20], experimental parameters, the scale removal schemes as well as suitable laser parameters are described in detail for such scale layers. In this paper, the analytical performance is investigated by series of measurements which are taken during routine production with a LIBS instrument installed on-site for feasibility studies. To our knowledge,
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ACCEPTED MANUSCRIPT there is no other analytical method proofed to measure the hidden steel composition under such scale surfaces within one minute at a rolling mill environment.
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2. Materials and Methods
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The Nd:YAG laser is a Q-switched diode-pumped oscillator followed by a flash lamp pumped
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amplifier emitting pulses of 20-40 ns at a wavelength of 1064 nm. It is focused by a lens with
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300 mm focal length. Fig. 2a shows the standard situation with fixed focal position. The contact of the LIBS plasma with the crater side wall adds impurities of scale into the plasma and these
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interfere the analysis of the bulk steel. Therefore the focal spot is slightly moved by a motorized
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mirror during the scale ablation step in order to enlarge the “cleaned” spot, see Fig. 2b. This
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avoids or – at least – reduces side effects from the scale at the crater wall [19].
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ACCEPTED MANUSCRIPT Fig. 2. (a) Schematic situation for standard LIBS with fixed focal position. (b) situation with a moving laser spot during the ablation step prior to analysis. Further experimental data are described in [20]. The difference in the detection set-up is that a vacuum Paschen-Runge spectrometer with solid-state detectors (OBLF GmbH, Witten) is used.
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The spectrometer has been adapted and modified according to the requirements for LIBS detection by OBLF. The LIBS spectra are registered without time gating by direct imaging of the
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plasma plume to the entrance slit of the spectrometer. Fig. 3 gives a section of the spectrum of a
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pure iron sample together with the spectra of a low alloy and a high alloy steel bloom in the vicinity of the Ni I 341.47 nm spectral line. As well-known from LIBS and OES, the spectral
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lines are numerically integrated and the integral values or ratios of these are taken for evaluation.
Fig. 3. Cut-out of the spectrum of a pure iron sample (Ni < 0.001 m.-%) compared to spectra of steel blooms with Ni mass fractions of 1.5 m.-% and 8.01 m.-%, respectively. The LIBS instrumentation consists of the laser, spectrometer, beam guiding optics, argon inert gas handling and control unit. The instrument is installed in a cabin and it has an access port to the side of the roller table with the steel blooms. Although the spectrometer is sensitive at the
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ACCEPTED MANUSCRIPT VUV range of the carbon line at 193 nm, the detection of this line and therefore the measurement of carbon was presumably precluded by residual air-absorption in the beam path. The installation site is laser shielded. For this study, the degree of automation is adapted to perform efficiently the measurement series and to comply with the cycle time of the production. The measurement is
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started manually, when the bloom is in position, but the subsequent steps runs automatically: the
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sensor head (see Fig. 4) extends from a stand-by position to the side face of the bloom according
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to an integrated distance sensor, control of the gas purging, the laser and motorized mirror during the scale ablation step, control of laser settings for ablation and analysis, spectrometer and
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spectra read-out during the analyzing step, return of the sensor head and other devices to stand-
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Fig. 4. Steel bloom (roughly 0.3 x 0.3 x 4 m3) on the roller table. The extendible sensor head is
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in stand-by position before the measurement (a), extended during the LIBS measurement (b) and
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returned to stand-by when the steel bloom is moving forward (c).
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3. Results and discussion
3.1. Calibration with reference samples
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The LIBS analyzer has been calibrated with a set of 30 reference samples (“calibration sample
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set”) provided by Deutsche Edelstahlwerke Specialty Steel with reference analysis, i.e. mass fractions wa in m.-% for the elements a. The reference samples are sawed out of blooms. They
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are without scale due to the preparation. Nevertheless, the calibration measurements are performed with the ablation step included in order to measure with the same laser parameters for calibration and analysis. For these measurements, the samples are fixed in a holder in front of the sensor head. Examples of univariate analytical evaluation curves [22], i.e. reference analysis ratios vs. LIBS spectral intensity ratios, are given in Fig. 5 for Cr, Ni, Mo, Cu and Fe for the total calibration range (see “min…max” in Table 1 for different elements). The Fe mass fractions, wFe,
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ACCEPTED MANUSCRIPT are determined from the polynomial fitting curve in Fig. 5f, where the abscissa values are calculated from the measured Cr/Fe and Ni/Fe mass fraction ratios as given in the graph. Each of the Cr/Fe and Ni/Fe mass fraction ratios are calculated from the corresponding LIBS intensity ratios by their analytical evaluation functions. The abscissa values (1 – Cr/Fe – Ni/Fe) are an
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approximation according to the 100% summation for the matrix element Fe and they include
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only the major alloys Cr and Ni. For our data base it works very well, but further elements can be
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included if required.
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Fig. 5. Analytical evaluation curves for the calibration sample set: (a) Cr, (b) Ni, (d) Mo and
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(e) Cu. For Ni, the low alloy range curve is given in graph (c). Iron is determined according to curve (f) from the measured Cr/Fe and Ni/Fe ratios. The spectral lines are denoted by the
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element symbol followed by the wavelength in units of 0.1 nm.
3.2. Comparison of LIBS vs. reference analysis In more detail, there are different ratios of spectral lines used for low and high alloy steel as common in spectral analysis. For example, in Fig. 5c the low alloy range for Ni is shown.
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ACCEPTED MANUSCRIPT Additionally, in some cases mass fraction ratios (wa/wFe)j are evaluated from different line ratios j and their average value <(wa/wFe)j> is taken for more robust results. Finally, the mass fractions, wa, (in m.-%) are calculated by multiplying (wa/wFe) with wFe.
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In Fig. 6 the Cr mass fractions determined by LIBS are compared with the reference values.
Fig. 6. LIBS vs. reference analysis of Cr for the calibration sample set. (a) total calibration range 0.1 to 17 m.-%, RMSEP 0.15 m.-%, (b) low alloy range 0.1 to 2.5 m.-%, enlarged detail from (a), RMSEP 0.035 m.-%.
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To assess the congruence of the measured values with the reference analysis, the root mean square error of prediction, RMSEP, is taken. It is defined for element a as [19]:
(1)
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1 n 2 RMSEPa wa, LIBS;i wa, ref; i n i 1
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where wa,LIBS;i is the mass fraction predicted from the analytical evaluation curve, wa,ref;i is the reference mass fraction of sample i and n is the total number of samples of the verification
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set. Table 1 summarizes the RMSEP values for the calibration samples taking the same sample set for verification which in general is a first lower estimation. For the on-site LIBS analysis, the
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batch reference analysis of the steel blooms are used for verification. It is clear that the resulting
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RMSEP values are an upper estimation because of the production environment with varying
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scale layers and bloom positions, vibrations, humidity, drifts due to longer measurement periods of several days as well as differences of the actual compositions to the batch analysis within the
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steel grade standard specifications. In Table 1, only elements are listed for which reference
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values are given for all 481 blooms - with exception of C which was not measured. In general, the calibration ranges should cover the ranges of measured steel blooms. For some elements (e.g.
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Cr, Mn, Cu) this is not exactly the case, see Table 1. The reasons are (a) the ranges of the steel blooms vary due to the actual rolling sequence of the routine production which was not exactly known beforehand at the time of the calibration, and (b) the restricted availability of calibration samples with reference analysis and arbitrary composition at that time. Further detected elements are Al, Nb, Pb, Sn, Ti, V and W but these are omitted here due to the different reference data basis. In general, other elements are possible due to the nearly continuous spectra of the spectrometer with solid-state detectors.
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calibr. samples N=30 steel blooms N=481
in m.-% min…max RMSEP min…max RMSEP
Ni 0.05...13 0.031 0.09...8.0 0.16
Cr 0.1...17 0.15 0.13...19 0.26
Mo 0.02...2.5 0.021 0.02...1.3 0.11
Mn 0.43...1.6 0.061 0.42...2.8 0.16
Cu 0.02...0.36 0.0091 0.03...3.1 0.1
Co 0.005...0.11 0.0024 0.008...0.09 0.004
Si 0.08...2.9 0.059 0.06...1.1 0.15
Fe 65…98.3 0.74 68…98.1 0.63
Table 1. Range of mass fractions w (“min…max”) for calibration and steel bloom
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measurements together with RMSEP values.
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3.3. LIBS measurements during production
LIBS measurements were taken for 481 steel blooms during routine production on several days
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with the instrumentation described above. According to the rolling sequence, the steel blooms on
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the roller table are coming in different batch quantities from a few up to roughly one hundred blooms which have the same nominal material and reference analysis. The measured mass
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fractions of each bloom in m.-% are plotted in Fig. 7 for Ni (a,c), Cr (b) and Fe (d) together with
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the nominal reference mass fractions. Each bloom has an individual LIBS value but there is only a single reference value for the blooms of the same batch, i.e. for these sections the (green)
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reference values are horizontal straight lines in Fig. 7.
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Fig. 7. Direct tracking of the rolling sequence and comparison of the LIBS measurements
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(black) of the mass fractions of Ni (a,c), Cr (b), and Fe (d) with the nominal reference values
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according to a batch analysis (green) of the steel bloom material. For clarity, in (a) and (b) the curves are separated by an offset of the right ordinate scale. (c) data for Ni plotted without offset and a logarithmic scale showing the differences in detail. The rolling sequence is clearly reflected by the LIBS measurement of the blooms and the mass fractions coincide well with the reference values. Nevertheless the observed limitations for lower
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ACCEPTED MANUSCRIPT mass fractions are given roughly by the RMSEP values of Table 1. For some other elements such as Si the deviations are greater. The reasons are not clarified in detail up to now. In order to assess the stability of the LIBS instrument, monitoring steel samples are repetitively measured during the on-site serial measurements by fixing them manually in a sample holder in
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front of the optical sensor head. For example, Fig. 8 demonstrates for two monitoring samples the relative good performance with respect to the working environment. The mean values /
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standard deviations (SD) / relative SDs are (for monitoring sample S1 and S2, respectively) for
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Cr (S1): 15.8 m.-% / 0.79 m.-% / 5.0 %, Cr (S2): 0.77 m.-% / 0.042 m.-% / 5.4 %,
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Ni (S1): 1.50 m.-% / 0.04 m.-% / 2.6 %,
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Ni (S2): 1.97 m.-% / 0.06 m.-% / 2.9 %.
Fig. 8. Repetitive measurements of monitoring steel samples S1 and S2 during short breaks of the measurement series of Fig. 7. The abscissa value, i.e. the particular time of the monitoring, is the corresponding adjacent number of the LIBS measurement of Fig. 7. The reference values from the batch analysis are given by the lines in the graph.
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3.4. Identification of steel blooms by the elemental composition For identification, the measured mass fraction wa,LIBS;i is compared with the nominal mass
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fraction wa,ref;i according to the rolling sequence. The following differences divided by their
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mean values are calculated for each steel bloom i and element a according to
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Da,i 2 wa, LIBS;i wa, ref; i / wa, LIBS;i wa, ref; i
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If any of these values is greater than the corresponding threshold value, Da,i,thr , the steel bloom is identified as incorrect, e.g. a mix-up occurred. The threshold values Da,i,thr of the elements
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included for identification are for Ni 1.3, Cr 0.54, Mo 1.3, Mn 1.2, Cu 1.4, Co 0.65, Si 1.61, V
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1.0, and Fe 0.06.
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One criterion to assess the identification of steel blooms by LIBS, is to verify if the LIBS measurements can detect the batch changes in a rolling sequence. In this case, each measured
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mass fraction wa,LIBS;i is compared with the nominal mass fraction wa,ref;i-1 of the previous steel
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bloom i-1. The result of this evaluation is that 24 of the 26 changes within the measured sequence are detected while the two non-recognized changes (positive-false detection) are only
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minor changes in the composition. No false-positive changes are detected (“false alarm”), i.e. no change is signalized for any of the other (481-26) = 455 blooms. This is important since falsepositive signals would lead to false alarms when the technique is used for mix-up detection. identification, case correct, positive-positive correct, false-false error, false-positive error, false-negative
total detected percent. 28 453 28 453 100% 202 908 174 263 86% 28 453 0 0% 202 908 28 645 14%
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ACCEPTED MANUSCRIPT Table 2. Identification of steel blooms by the LIBS measured mass fractions, simulated through a computational permutation of the assigned reference values.
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Another evaluation is carried out by permutation of the LIBS measured values to the reference
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values of the steel blooms in a computer simulation. For the given rolling sequence, there are
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481 x 481 = 231 361 permutations from which 202 908 are simulated mix-ups (reference values of another bloom are incorrectly assigned to the LIBS measured bloom) and 28 453 are identical
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assignments (permutations with a bloom with same batch reference values). Table 2 gives the
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result that 86 % of the incorrect assignments due to the permutation (mix-ups) are detected by the LIBS measurements (correct false-false identification). There are no erroneous false-positive
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detections (“false alarms”) from the total number of 28 453 identical assignments. About 14 % of
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the simulated mix-ups are not detected (false-negative). A detailed view exhibit that these are either very similar steel grades or the majority (> 52 %) of these steel grades are not
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distinguishable on the basis of the measured elements and their overlaps of the grade
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specifications. Every considerable mix-up, e.g. a low alloy with a high alloy bloom, is detected in the measured sequence according to the simulation. These results demonstrate that an
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identification of blooms with significantly different compositions is feasible. Nevertheless the inclusion of a carbon measurement is desirable for a further improvement.
4. Conclusions
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ACCEPTED MANUSCRIPT The subsurface bulk material of steel blooms with superficial scale is analyzed by LIBS within 50 s including the local ablation of the scale and the analysis of the steel composition. The LIBS instrument has been installed temporarily on-site in a rolling mill and its performance is tested in measurement series at 481 steel blooms during routine production. Based on the calibration with
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30 reference samples (RMSEP 0.01-0.15 m.-%), the mass fractions of the low and high alloy
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steel blooms are determined directly on the roller table, i.e. the tracking of the rolling sequence is
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demonstrated. The rolling sequence is clearly reflected by the LIBS measurement of the individual blooms and the mass fractions coincide well with the batch reference values. The
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observed limitations for lower mass fractions are given roughly by the RMSEP values of about
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0.1-0.2 m.-% when compared to the batch reference values.
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The feasibility for identification is assessed by the detection of 24 out of 26 batch changes in the rolling sequence within the measured series – whereupon the residual two changes are only
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minor material changes. Computer simulations by permutation of the LIBS measured values and
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the reference values from the rolling sequence lead to an 86 % identification rate of mix-ups including all crucial mix-ups of very different steel grades. The non-detected 14 % of simulated
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mix-ups are very similar steel grades or grades which are not distinguishable by the measured
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elements and their overlaps of the grade specifications. These results demonstrate that an identification of blooms with significant different compositions is feasible, although carbon was not measured presumably due to constraints of the installed instrument. Further studies will target on long-term instrument performance and carbon measurements will be studied in detail from which a further improvement of the identification rates is expected.
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ACCEPTED MANUSCRIPT Acknowledgment The work was co-financed by the European Union, the Federal State of North-Rhine Westphalia: “Europäischer Fonds für regionale Entwicklung (EFRE) für NRW” (grant number 300113002) and the Fraunhofer Society. We gratefully acknowledge the co-operation with D. Siegmund
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(OBLF, Witten), H. Zouak, L. Peter (Xox, Aachen), C. Gehlen (LSA, Aachen) for the design and
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set-up of the LIBS instrument.
A.W. Miziolek, V. Palleschi, I. Schechter, Laser-induced breakdown spectroscopy,
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Spectrosc. 112 (2015) 1–5. https://doi.org/10.1016/j.sab.2015.07.006. [14] T. Delgado, J. Ruiz, L.M. Cabalin, J.J. Laserna, Distinction strategies based on
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discriminant function analysis for particular steel grades at elevated temperature using stand-off LIBS, J. Anal. At. Spectrom. 31 (2016) 2242-2252. http://dx.doi.org/10.1039/C6JA00219F. [15] V. Sturm, R. Fleige, M. de Kanter, R. Leitner, K. Pilz, D. Fischer, G. Hubmer, R. Noll, Laser-Induced Breakdown Spectroscopy for 24/7 Automatic Liquid Slag Analysis at a Steel Works, Anal. Chem. 86 (2014) 9687-9692. http://dx.doi.org/10.1021/ac5022425.
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ACCEPTED MANUSCRIPT [16] R. Noll, V. Sturm, Ü. Aydin, D. Eilers, C. Gehlen, M. Höhne, A. Lamott, J. Makowe, J. Vrenegor, Laser-induced breakdown spectroscopy—From research to industry, new frontiers for process control, Spectrochim. Acta B At. Spectrosc. 63 (2008) 1159–1156. https://doi.org/10.1016/j.sab.2008.08.011.
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[17] A. Weisberg, J. Craparo, R. De Saro, C. Romero, R. Pawluczyk, A.I. Whitehouse, Conference paper AMC4, Applied Industrial Optics: Spectroscopy, Imaging and Metrology,
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Tucson, Arizona United States, June 7-8, 2010, ISBN: 978-1-55752-892-6.
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https://doi.org/10.1364/AIO.2010.AMC4.
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[18] V. Sturm, J. Vrenegor, R. Noll, M. Hemmerlin, Bulk analysis of steel samples with surface scale layers by enhanced laser ablation and LIBS analysis of C, P, S, Al, Cr, Cu, Mn and
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Mo, J. Anal. At. Spectrom. 19 (2004) 451-456. http://dx.doi.org/10.1039/B315637K.
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[19] German Patent, Analyzing solid materials with laser emission spectrometry, DE 103 61
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727 B3 (2005).
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[20] C. Meinhardt, V. Sturm, R. Fleige, C. Fricke-Begemann, R. Noll, Laser-induced breakdown spectroscopy of scaled steel samples taken from continuous casting blooms,
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Spectrochim. Acta B At. Spectrosc. 123 (2016) 171–178. https://doi.org/10.1016/j.sab.2016.08.013. [21] K.E. Esbensen, Multivariate Data Analysis, 5th ed., Camo Process AS, Oslo, 2002, p 159.
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ACCEPTED MANUSCRIPT [22] International Union of Pure and Applied Chemistry, Nomenclature, Symbols, Units and their Usage in Spectrochemical Analysis - II. Data, Pure and Applied Chemistry 45, 99-103,
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1976.
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ACCEPTED MANUSCRIPT
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For graphical abstract
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ACCEPTED MANUSCRIPT Title: Fast Identification of Steel Bloom Composition at a Rolling Mill by LIBS Elemental Analysis Authors: Volker Sturm, Christoph Meinhardt, Rüdiger Fleige, Cord Fricke-Begemann, Jens Eisbach
Highlights
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LIBS elemental analysis tracks the composition of steel blooms on a roller table Laser ablation of scale and LIBS analyses the steel under the superficial scale Up to 14 elements determined without sampling and laboratory analysis within 50 s Hundreds of blooms measured during routine production Comparison with the rolling sequence shows the feasibility for material identification
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