Melt Density Monitoring of Extruder Extrusion Process Based on Multi-source Data Fusion and Convolutional Long Short-term Memory Neural Network - Binbin ZHANG; Zhuyun CHEN; Fei ZHANG; Gang JIN (工程科学与技术)
Objective This study addresses the challenging task of real-time monitoring of melt density during polymer extrusion, specifically for the polycarbonate-acrylonitrile-butadiene-styrene (PC/ABS) blend system, a critical parameter profoundly impacting the quality of the final product. Ensuring precise control over melt density is imperative for achieving desired product characteristics and maintaining process stability in polymer blending operations.Methods The research proposes a novel methodological framework that integrates multi-source data fusion with a convolutional long short-term memory (LSTM) neural network architecture to address this challenge. Using an independently developed multi-source sensory data acquisition device, three distinct sensor modalities, near-infrared, Raman, and ultrasound, are strategically positioned at the die head of the extruder to provide real-time data streams. These sensors capture vital insights into the dynamic changes occurring within the polymer melt during the extrusion process. The proposed model effectively learns the intricate mapping relationship between sensory data and melt density by amalgamating these multi-source sensory inputs and using the feature extraction capabilities of convolutional neural networks and the temporal dependencies modeling capabilities of LSTM networks.Results and Discussions The application of the proposed method demonstrates significant efficiency in real-time monitoring of polymer melt density by monitoring the melt density during the PC/ABS blending extrusion process. Empirical evaluations reveal a root mean square error (RMSE) of 0.975 g/cm3 and a coefficient of determination (R2) value of 0.0063, underscoring the superior predictive accuracy of this approach compared to conventional methods. In addition, the proposed method exhibits robustness in handling the inherent complexities and variabilities in polymer extrusion processes, thus offering a reliable solution for ensuring product quality and process efficiency. The average prediction time for ten inputs is 1.523 5 s, highlighting its suitability for real-time monitoring in actual production environments. Conclusions The proposed approach facilitates accurate and timely monitoring of melt density during polymer extrusion and represents a significant advancement in polymer processing monitoring by harnessing the power of multi-source data fusion and advanced neural network techniques. This enables manufacturers to make informed decisions in real time, enhancing product consistency, minimizing defects, and optimizing process parameters for improved productivity and cost-effectiveness.