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Classification and Mechanical Properties of Synthetic Polymers01:28

Classification and Mechanical Properties of Synthetic Polymers

Synthetic polymers are classified as elastomers, fibers, or plastics based on their crystallinity. Crystallinity, the degree of long-range order in the solid state, influences the mechanical properties (stretching or contracting) of elastomers. Elastomers are flexible polymers that can expand or contract easily upon the application of an external force. They have numerous crosslinks that pull them back into their original shape when stress is removed. Silicones, for instance, are highly elastic...

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Sensitivity Enhancement of Soft Capacitive Pressure Sensors Using a Solvent Evaporation-Based Porosity Control Technique
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Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes.

Shuihua Zheng1, Kaixin Liu1, Yili Xu2

  • 1Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

Sensors (Basel, Switzerland)
|February 5, 2020
PubMed
Summary

A new robust semi-supervised soft sensor, ensemble deep correntropy kernel regression (EDCKR), reliably predicts Mooney viscosity in rubber mixing. This method enhances industrial process monitoring and control using advanced machine learning techniques.

Keywords:
Mooney viscositydeep learningensemble strategyrobust estimatorrubber mixing processsemi-supervised learningsoft sensor

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Area of Science:

  • Chemical Engineering
  • Materials Science
  • Data Science

Background:

  • Online prediction of Mooney viscosity in industrial rubber mixing remains challenging despite existing data-driven soft sensors.
  • Reliable real-time monitoring is crucial for optimizing rubber compound quality and process efficiency.

Purpose of the Study:

  • To propose a robust semi-supervised soft sensor for accurate online Mooney viscosity prediction.
  • To integrate ensemble strategy, deep belief network (DBN), and correntropy kernel regression (CKR) for enhanced soft sensing.

Main Methods:

  • Developed an ensemble deep correntropy kernel regression (EDCKR) framework.
  • Utilized a multilevel DBN for unsupervised feature extraction from secondary variables.
  • Employed a supervised CKR model to establish feature-viscosity relationships, reducing outlier effects.

Main Results:

  • The EDCKR model demonstrated robust and reliable online Mooney viscosity prediction.
  • The integrated approach effectively handled noisy data and reduced the impact of outliers.
  • An industrial case study confirmed the practicality and effectiveness of the proposed method.

Conclusions:

  • EDCKR offers a reliable solution for the challenging task of online Mooney viscosity prediction in rubber mixing.
  • The soft sensor's robustness and accuracy are validated through industrial application.
  • This approach advances soft sensing capabilities in polymer processing.