Prediction of Weld Current Using Deep Transfer Image Networks Based on Weld Signatures for Quality Control

Enriquez, Mike Louie; Concepcion, Ronnie; Relano, R-Jay; Francisco, Kate; Mayol, Andres Philip; Española, Jason L.; Vicerra, Ryan Rhay P.; Co, Homer; Dadios, Elmer P.

doi:10.1109/hnicem54116.2021.9731979

Cited by 14 publications

(2 citation statements)

References 30 publications

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“…The results show that the selected ERNN models have the minimum MSE and better accuracy closer to the actual value of current and voltage signals which is essential compared to manual computations of output parameters through Ohm's Law and it is proven that RNN works effectively as presented in [16], [18] by its ability to estimate reference voltage for monitoring purposes without actually performing any measurements and in predicting electrical parameters. Compared with LSTM and GRU, the ERNN model has the ultimate advantages of fast training speed and lower predictive error in this application.…”

Section: Simulation Comparison Of Voltage Prediction Modelsmentioning

confidence: 96%

“…In the study of [17], researchers compared neural network prediction models, support vector machine (SVM) for regression, and equation discovery for predicting the next voltage values without performing measurements. Another prediction modeling was employed through a convolutional neural network (CNN) to detect and categorize welding current to construct an ERT imaging trailer and detect defects [18]. While in [19], the paper suggests a prediction model of the input voltage signal received by an underground imaging system based on genetic programming (GP).…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Network-based Current and Voltage Prediction Models for Digital Measuring Unit of Capacitive Resistivity Underground Imaging Transmitter Subsystem

Jahara Baun,

Genevie Janairo,

Concepcion II

et al. 2024

IJCDS

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Real-time monitoring of output electrical parameters of the transmitted signals in a capacitive resistivity underground imaging system is necessary because these are significant in the calculation of underground resistivity, however, machine learning has not yet been applied in this application to improve the accuracy of measurement. This study aims to develop and select the best prediction models that can be implemented for a digital measuring unit suitable for capacitive resistivity underground imaging. Three deep neural network models namely Elman recurrent neural network (ERNN), long short-term memory (LSTM), and gated recurrent unit (GRU) were explored to build prediction models for the current and voltage of the transmitter circuit. The prediction models' performance was assessed using mean squared error (MSE), which is reduced to its absolute lowest value. The result shows that the best-trained models for current and voltage prediction are the ERNN models with configurations of 900-600-500 hidden neurons network with training MSE of 9.82 X 10 -9 and the configured 1300-1000-900 hidden neurons with training MSE of 0.465, respectively. With the help of the prediction models, it would be possible to measure current and voltage output accurately, allowing simultaneous data acquisition while avoiding the need for a separate measuring device.

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Section: Simulation Comparison Of Voltage Prediction Modelsmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%