Machine Learning for Advanced Polymer Manufacturing, Processing, and Testing
Machine Learning for Advanced Polymer Manufacturing, Processing, and Testing
Description
We are currently at the era of Industry 4.0, which reshapes business and society with an unprecedented depth and breadth. In the industry of polymer manufacturing, processing, and testing, advanced techniques with high automation and intelligence level are required to meet increasing demands on energy, environment, and product quality. To accelerate the design of polymer production processes, without sacrificing product quality or increasing production cost, data-driven techniques for process analysis and control become very important. These techniques are also needed to break through the bottleneck of polymer processing and testing.
Traditional multivariate data analysis methods, such as principal component analysis (PCA), partial least squares (PLS), and support vector machine (SVM), have been widely applied to industrial data. Recent developments in machine learning and advanced analytics provide alternative solutions for leveraging industrial data for solving complex polymer manufacturing, processing, and testing problems. In-depth analysis of process data and integration of various process engineering techniques can improve the production efficiency, process safety, and profitability.
This special issue aims to curate novel advances in the development and application of machine learning techniques to address ever-present challenges in the current polymer industry. Research papers and reviews are both welcome in this special issue.
Potential topics include but are not limited to the following:
- Machine learning methods for modeling polymerization processes
- Advanced monitoring and diagnosis methods for polymer processing
- Image and video-based soft-sensor technologies for polymer quality prediction
- Machine learning/deep learning methods for polymer data analytics and visualization
- Data analytics for nondestructive testing of polymers