Machine learning-assited optical thermometer for continuous temperature analysis inside molten metal

Qian, Jiaru; Zhao, Zijian; Zhang, Qinming; Werner, M.; Petty, Randy; Abraham, Sunday; Lu, Meng

doi:10.1016/j.sna.2021.112626

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…The literature specifically on NN temperature analysis is very limited and is either applied in one-or two-dimensions. One-dimensional studies include atmospheric temperature profiles as functions of altitude (a feedforward neural network, FFNN, with a root mean squared error (RMSE) between ±0.4-1 K [26] and ±1.61 K [27]), temperature and concentration profiles of gases during a combustion reaction (multilayer perceptron (MLP) network with an RMSE between 1.2-8.1% [28]), and a 1D convolutional neural network (CNN) for optical IR thermometer in molten metals [29]. For two-dimensional studies, hyperspectral imaging (RMSE of ±1.14 K [30]), time-of-flight for ultrasonic waves (a mean error near ±0.12 K [31]), 2D flame surface and temperature by CNN [32] (with an unreported RMSE), and flame spectral absorption images (RMSE of ±13.5K [33]) were used to determine temperature.…”

Section: Neural Network Approaches To Temperature Analysismentioning

confidence: 99%

Demonstration of Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data towards use in Bio-Microfluidics

Kullberg

Colton

Gregory

et al. 2022

Preprint

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Biological systems often have a narrow temperature range of operation, which require highly accurate spatially resolved temperature measurements, often near ±0.1K. However, many temperature sensors cannot meet both accuracy and spatial distribution requirements, often because their accuracy is limited by data fitting and temperature reconstruction models. Machine learning algorithms have the potential to meet this need, but their usage in generating spatial distributions of temperature is severely lacking in the literature. This work presents the first instance of using neural networks to process fluorescent images to map the spatial distribution of temperature. Three standard network architectures were investigated using non-spatially resolved fluorescent thermometry (simply-connected feed-forward network) or during image or pixel identification (U-net and convolutional neural network, CNN). Simulated fluorescent images based on experimental data were generated based on known temperature distributions where Gaussian white noise with a standard deviation of ±0.1 K was added. The poor results from these standard networks motivated the creation of what is termed a moving CNN, with an RMSE error of ±0.23 K, where the elements of the matrix represent the neighboring pixels. Finally, the performance of this MCNN is investigated when trained and applied to three distinctive temperature distributions characteristic within microfluidic devices, where the fluorescent image is simulated at either three or five different wavelengths. The results demonstrate that having a minimum of 103.5 data points per temperature and the broadest range of temperatures during training provides temperature predictions nearest to the true temperatures of the images, with a minimum RMSE of ±0.15 K. When compared to traditional curve fitting techniques, this work demonstrates that greater accuracy when spatially mapping temperature from fluorescent images can be achieved when using convolutional neural networks.

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Section: Neural Network Approaches To Temperature Analysismentioning

confidence: 99%

Demonstration of Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data towards use in Bio-Microfluidics

Kullberg

Colton

Gregory

et al. 2022

Preprint

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“…Temperature is a crucial physical parameter in numerous industrial operations and scientific research. Because of their accuracy, sensitivity, and applicability to complex environments, non-contact fluorescence thermometers have attracted considerable research interest in recent years and are used for temperature measurements in medicine, chemistry, and other industries [ 1 , 2 ]. Two main temperature measurement strategies are generally practiced: one is temperature measurement using a pair of thermally coupled levels (TCL), where the relative sensitivity (S r ) is directly proportional to the energy gap of the relevant TCLs [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Novel phosphor GdY2SbO7 co-dope with Eu3+ and Bi3+ for optical thermometer

Yin,

Jiang,

Tian

2024

Heliyon

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“…This hypothesis is based on planar temperature reconstruction from a series of images taken using thermochromic liquid crystals (TLC), where a pixel-topixel based simply connected feedforward network (P2P-SCFFNN) achieved a mean absolute deviation near ±0.1 K over a 4.4 K range from 291.1 to 295.5 K [14]. NNs have been used to recreate temperature from other signals as well, such as IR thermometry of a point [15], atmospheric temperature at varying altitudes [16], flame temperature and composition [17][18][19], and time-of-flight for ultrasonic waves [20]. Previous works' fundamental limitations are that they cannot create a 2D image or have not been applied to fluorescence thermometry.…”

Section: Introductionmentioning

confidence: 99%

Using Recurrent Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data for use in Bio-Microfluidics

Kullberg,

Sanchez,

Mitchell

et al. 2023

Preprint

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Many biological systems have a narrow temperature range of operation, meaning high accuracy and spatial distribution level are needed to study these systems. Most temperature sensors cannot meet both the accuracy and spatial distribution required in the microfluidic systems that are often used to study these systems in isolation. This paper introduces a neural network called the Multi-Directional Fluorescent Temperature Long Short-Term Memory Network (MFTLSTM) that can accurately calculate the temperature at every pixel in a fluorescent image to improve upon the standard fitting practice and other machine learning methods use to relate fluorescent data to temperature. This network takes advantage of the nature of heat diffusion in the image to achieve an accuracy of ±0.0199 K RMSE within the temperature range of 298K to 308 K with simulated data. When applied to experimental data from a 3D printed microfluidic device with a temperature range of 290 K to 380 K, it achieved an accuracy of ±0.0684 K RMSE. These results have the potential to allow high temperature resolution in biological systems than is available in many microfluidic devices.

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Machine learning-assited optical thermometer for continuous temperature analysis inside molten metal

Cited by 10 publications

References 18 publications

Demonstration of Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data towards use in Bio-Microfluidics

Demonstration of Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data towards use in Bio-Microfluidics

Novel phosphor GdY2SbO7 co-dope with Eu3+ and Bi3+ for optical thermometer

Using Recurrent Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data for use in Bio-Microfluidics

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