A study of the temperature variation effect in a steel sample for rapid analysis using LIBS

Lin, Jingjun; Yang, Jiangfei; Huang, Yutao; Lin, Xiao

doi:10.1016/j.optlastec.2021.107707

Cited by 8 publications

(6 citation statements)

References 28 publications

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“…Two papers have discussed the effects of sample temperature on the LIBS analyses of steels. 14,15 In the paper by Lin et al the spectral intensity of both ions and atoms were reported to increase with increasing temperature. 14 However, the temperature of the plasma formed hardly changed (14 709 K and 14 227 K at 1432 C (the melting point of the steel) and at 20 C, respectively).…”

Section: Ferrous Metalsmentioning

confidence: 99%

“…14,15 In the paper by Lin et al the spectral intensity of both ions and atoms were reported to increase with increasing temperature. 14 However, the temperature of the plasma formed hardly changed (14 709 K and 14 227 K at 1432 C (the melting point of the steel) and at 20 C, respectively). It was noted that both the correlation coefficients and the root mean square error of prediction (RMSEP) for C (193.03 nm), Cr (205.56 nm), Mn (293.31 nm) and Si (288.16 nm) improved at 1432 C compared with those at 20 C. Although the correlation coefficients were better, the improvement was only marginal, e.g.…”

Section: Ferrous Metalsmentioning

confidence: 99%

See 1 more Smart Citation

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

Carter¹,

Clough

Fisher

et al. 2022

J. Anal. At. Spectrom.

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show abstract

Section: Ferrous Metalsmentioning

confidence: 99%

Section: Ferrous Metalsmentioning

confidence: 99%

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

Carter¹,

Clough

Fisher

et al. 2022

J. Anal. At. Spectrom.

View full text Add to dashboard Cite

show abstract

“…Figure 3 illustrates online detection technologies used in China’s steel industry. The detection technology development direction is to use advanced modern detection technologies such as machine vision [ 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 ], laser-induced breakdown spectroscopy (LIBS) [ 94 , 95 , 96 , 97 , 98 ], ultrasonic microscopy technology, and others, in conjunction with deep-learning algorithms and statistical modeling theory; to apply or develop intelligent perception technology on the production line; and to conduct online or rapid detection of key parameters throughout the manufacturing process. The ultimate purposes of online detecting technologies are to provide intelligent management and process optimization in the steel industry, improve the quality of terminal products, increase labor productivity and reduce labor costs, and provide vital fundamental data for quality control and big data platforms.…”

Section: Key Technologies For Intelligent Manufacturing In Steel Indu...mentioning

confidence: 99%

Intelligent Manufacturing Technology in the Steel Industry of China: A Review

Zhou

et al. 2022

Sensors

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Intelligent manufacturing, defined as the integration of manufacturing with modern information technologies such as 5G, digitalization, networking, and intelligence, has grown in popularity as a means of boosting the productivity, intelligence, and flexibility of traditional manufacturing processes. The steel industry is a necessary support for modern life and economic development, and the Chinese steel industry’s capacity has expanded to roughly half of global production. However, the Chinese steel industry is now confronted with high labor costs, massive carbon emissions, a low level of intelligence, low production efficiency, and unstable quality control. Therefore, China’s steel industry has launched several large-scale intelligent manufacturing initiatives to improve production efficiency, product quality, manual labor intensity, and employee working conditions. Unfortunately, there is no comprehensive overview of intelligent manufacturing in China’s steel industry. We began this research by summarizing the construction goals and overall framework for intelligent manufacturing of the steel industry in China. Following that, we offered a brief review of intelligent manufacturing for China’s steel industry, as well as descriptions of two typical intelligent manufacturing models. Finally, some major technologies employed for intelligent production in China’s steel industry were introduced. This research not only helps to comprehend the development model, essential technologies, and construction techniques of intelligent manufacturing in China’s steel industry, but it also provides vital inspiration for the manufacturing industry’s digital and intelligence updates and quality improvement.

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“…This is considered an essential thermodynamic state parameter [27]. The Boltzmann slope method [28] was used in this paper to determine the intensity of the spectrum. When the plasma is in local thermal equilibrium, the following equation can be used to calculate the plasma temperature:…”

Section: Plasma Temperature Compensation and Spectral Normalizationmentioning

confidence: 99%

Detection of Carbon Content from Pulverized Coal Using LIBS Coupled with DSC-PLS Method

Guan

Chen

et al. 2022

Chemosensors

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The dust from pulverized coal weakens the acquired signal and increases the analysis difficulty for the quantitative analysis of the carbon content of pulverized coal when using laser-induced breakdown spectroscopy (LIBS). Moreover, there is a serious matrix effect and a self-absorption phenomenon. To improve the analysis accuracy, the DSC-PLS (double spectral correction-partial-least-squares) method was proposed to predict the carbon content of pulverized coal. Initially, the LIBS signal was corrected twice using P-operation-assisted adaptive iterative-weighted penalized-least-squares (P-airPLS), plasma temperature compensation, and spectral normalization algorithms. The goodness of fit of the carbon element was improved from nonlinearity to above 0.948. The modified signal was then used to establish DCS-PLS models for predicting unknown samples. In comparison to the conventional PLS model, the DSC-PLS method proposed in this paper significantly improves the ability to predict carbon content. The prediction error of the developed method was dropped from an average of 4.66% to about 0.41%, with the goodness of fit R2 of around 0.991.

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A study of the temperature variation effect in a steel sample for rapid analysis using LIBS

Cited by 8 publications

References 28 publications

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

Intelligent Manufacturing Technology in the Steel Industry of China: A Review

Detection of Carbon Content from Pulverized Coal Using LIBS Coupled with DSC-PLS Method

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