Emissivity and temperature determination on steel above the melting point

Goett, G; Kozakov, Ruslan; Uhrlandt, Dirk; Schoepp, Heinz; Sperl, A

doi:10.1007/s40194-013-0054-2

Cited by 17 publications

(10 citation statements)

References 7 publications

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“…This result is consistent with Ref. 10, where the emissivity above the melt temperature is lower than at any cooler temperature. As can be seen, the pyrometer consistently measures lower temperatures than the FLIR.…”

Section: Resultssupporting

confidence: 93%

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Measurement of Laser Weld Temperatures for 3D Model Input

Dagel

Grossetete

MacCallum

2016

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Laser welding is a key joining process used extensively in the manufacture and assembly of critical components for several weapons systems. Sandia National Laboratories advances the understanding of the laser welding process through coupled experimentation and modeling. This report summarizes the experimental portion of the research program, which focused on measuring temperatures and thermal history of laser welds on steel plates. To increase confidence in measurement accuracy, researchers utilized multiple complementary techniques to acquire temperatures during laser welding. This data serves as input to and validation of 3D laser welding models aimed at predicting microstructure and the formation of defects and their impact on weld-joint reliability, a crucial step in rapid prototyping of weapons components.

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Section: Resultssupporting

confidence: 93%

“…Overall, the Gleeble results follow the form predicted in Ref. 10, although with a lower point of inversion. Figure 13.…”

Section: Resultssupporting

confidence: 82%

Measurement of Laser Weld Temperatures for 3D Model Input

Dagel

Grossetete

MacCallum

2016

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“…Del Campo et al [23] reported the emissivity measurements of oxidized iron below 570 • C. Shi et al [24], [25] investigated the emissivity behavior of oxidized stainless steel between 800 and 1100 K at 1.5 μm. Goett et al [26] measured the emissivity of polished iron above its melting point. Wang et al [27] measured the spectral emissivity of SS304 between 800 • C and 1100 • C with an induction furnace.…”

Section: Introductionmentioning

confidence: 99%

“…This, in turn, leads to the second problem of rendering temperature measurement uncertainty calculations, and with it traceability, invalid. For example, the results published by Wen and Mudawar [20]- [22] and Goett et al [26]. Another example is Wang et al [27] who only analyzed instrument uncertainty at one temperature: uncertainty at 1000 • C of 0.0606 (at k = 2).…”

Section: Introductionmentioning

confidence: 99%

An Accurate Device for Apparent Emissivity Characterization in Controlled Atmospheric Conditions Up To 1423 K

Zhu

Hobbs

Masters

et al. 2020

IEEE Trans. Instrum. Meas.

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Emissivity is a material property that must be measured before an accurate noncontact temperature measurement can be made. We have developed a novel instrument for measuring apparent emissivity under a controlled atmosphere, providing data for applications in radiation thermometry. Our instrument employs a split furnace, a sample-blackbody component, two custom-designed radiometers, and a controlled atmospheric system. We measure across the temperature range from 973 to 1423 K and the spectral range from 0.85 to 1.1 µm; this range is matched to the majority of high-temperature radiation thermometers. The sample and reference approximate blackbody are heated and maintained in the thermal equilibrium, with a temperature difference of better than 1 K at 1423 K. The combined standard uncertainty of the system is lower than 0.0590 (at k = 2) over the whole temperature range. Apparent emissivity of type 304 stainless steel (SS304) was studied under different oxidizing procedures. Nitrogen and compressed air were input into the system to control the oxidization process. We elucidated the relationship between the apparent emissivity variations and the surface composition changes of SS304 during oxidization. This article aims toward accurate and traceable apparent emissivity data, with well-investigated uncertainty, for use in radiation thermometry.

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“…According to Equation (2), the emissivity ε of the measured object is also required for the quantitatively correct conversion of the measured radiance values into temperatures. Unfortunately, due to the phase change of the metallic material, the required emissivity changes in an unknown manner and is hard to determine by experiments [1,15]. One way to approximate the emissivity is to use the characteristic point of the solidification plateau [16,17].…”

Section: Measurement Data Processingmentioning

confidence: 99%

Identification of a Spatio-Temporal Temperature Model for Laser Metal Deposition

Kahl

Schramm

Neumann

et al. 2021

Metals

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Laser-based additive manufacturing enables the production of complex geometries viaayer-wise cladding. Laser metal deposition (LMD) uses a scanningaser source to fuse in situ deposited metal powderayer byayer. However, due to the excessive number of influential factors in the physical transformation of the metal powder and the highly dynamic temperature fields caused by the melt pool dynamics and phase transitions, the quality and repeatability of parts built by this process is still challenging. In order to analyze and/or predict the spatially varying and time dependent thermal behavior in LMD, extensive work has been done to develop predictive models usually by using finite element method (FEM). From a control-oriented perspective, simulations based on these models are computationally too expensive and are thus not suitable for real-time control applications. In this contribution, a spatio-temporal input–output model based on the heat equation is proposed. In contrast to other works, the parameters of the model are directly estimated from measurements of the LMD process acquired with an infrared (IR) camera during processing specimens using AISI 316 L stainless steel. In order to deal with noisy data, system identification techniques are used taking different disturbing noise into account. By doing so, spatio-temporal models are developed, enabling the prediction of the thermal behavior by means of the radiance measured by the IR camera in the range of the considered processing parameters. Furthermore, in the considered modeling framework, the computational effort for thermal prediction is reduced compared to FEM, thus enabling the use in real-time control applications.

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Emissivity and temperature determination on steel above the melting point

Cited by 17 publications

References 7 publications

Measurement of Laser Weld Temperatures for 3D Model Input

Measurement of Laser Weld Temperatures for 3D Model Input

An Accurate Device for Apparent Emissivity Characterization in Controlled Atmospheric Conditions Up To 1423 K

Identification of a Spatio-Temporal Temperature Model for Laser Metal Deposition

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