Melt Pressure Prediction in Polymer Extrusion Processes with Deep Learning

Perera, Yasith S.; Li, Jie; Kelly, Adrian L.; Abeykoon, Chamil

doi:10.23919/ecc57647.2023.10178125

Cited by 2 publications

(2 citation statements)

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“…However, with the rise of deep learning, more and more researchers are applying this technology to the field of polymer processing detection. 16,17 The emergence of deep learning has revolutionized the landscape of polymer processing detection, enabling researchers to harness the capabilities of NNs and advanced deep learning techniques. By leveraging the power of NNs and other deep learning techniques, researchers across different fields can more effectively analyze large datasets and extract meaningful insights from complex systems.…”

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“…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. This gap in research is critical as predicting melt density is essential for ensuring product quality and consistency.…”

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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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