A Novel In-Line Polymer Melt Viscosity Sensing System of Integrated Soft Sensor and Machine Learning

Wang, Zhi-Hao; Li, Yi-Ting; Wen, Fu-Chi

doi:10.1109/jsen.2023.3267682

Cited by 3 publications

(3 citation statements)

References 43 publications

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“…The results demonstrate the feasibility of the constructed network 25 . Wang et al study utilized an ensemble machine learning model which consists of RFs and CNNs achieved a significant reduction in mean absolute error from 6.08 to 2.86 and prediction error rate from 4% to 1.1% 26 . Perera developed a robust data‐driven model, combining deep autoencoder and feedforward NN techniques, achieving accurate predictions of melt pressure 27…”

Section: Introductionmentioning

confidence: 89%

“…CNN model 26 is constructed for real‐time monitoring of melt viscosity in the polymer extrusion process. In this study, a CNN model with two convolutional layers is constructed.…”

Section: Methodsmentioning

confidence: 99%

“…25 Wang et al study utilized an ensemble machine learning model which consists of RFs and CNNs achieved a significant reduction in mean absolute error from 6.08 to 2.86 and prediction error rate from 4% to 1.1%. 26 Perera developed a robust data-driven model, combining deep autoencoder and feedforward NN techniques, achieving accurate predictions of melt pressure. 27 While some areas have successfully utilized deep learning techniques for parameter prediction in polymer processing, there is a scarcity of studies focusing on melt prediction using deep learning.…”

mentioning

confidence: 99%

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Multi‐source data‐driven approach for prediction of melt density during polymer compounding

Zhang,

Chen,

Zhang

et al. 2024

Polymer Engineering & Sci

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Melt density is a crucial quality indicator for polymer composites, yet real‐time measurement remains challenging due to processing complexities. While existing machine learning methods offer solutions, they often fall short in complex compounding scenarios. This study presents a novel multi‐source data‐driven approach for measuring melt density in polycarbonate/acrylonitrile butadiene styrene blends. By incorporating ultrasonic, near‐infrared, and Raman spectra data acquired during melt processing, a deep separable convolutional neural network model is developed to predict melt density accurately. The model effectively fuses multi‐source data to establish the mapping relationship between input data and melt density output. Results demonstrate the model's ability to monitor melt density in real‐time, achieving a prediction accuracy with RMSE and R2 indexes of 0.005 g/cm3 and 0.9841, respectively. The proposed approach outperforms existing methods, showcasing its effectiveness and superiority in melt density prediction for polymer compounding processes.Highlights Establishment of the real‐time monitoring system for polymer extrusion processes. Conversion of multi‐sensor signals into time‐frequency images using wavelet decomposition. Fusion of sensor data into a three‐channel tensor‐image. Development of a data‐driven DSCNN model for predicting melt density. Implementation for online monitoring and prediction in PC/ABS compounding system.

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Section: Introductionmentioning

confidence: 89%

“…CNN model 26 is constructed for real‐time monitoring of melt viscosity in the polymer extrusion process. In this study, a CNN model with two convolutional layers is constructed.…”

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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