Extraction of Process-Structure Evolution Linkages from X-ray Scattering Measurements Using Dimensionality Reduction...

Area of Science:

• Materials Science
• Data Science
• Polymer Science

Background:

• Developing new materials requires efficient methods to link processing conditions with structural evolution.
• Hierarchical structures in materials significantly influence their properties and performance.
• Existing frameworks may not fully capture the dynamic nature of structure evolution.

Purpose of the Study:

• To introduce a novel theoretical framework combining time series analysis with the Materials Knowledge Systems (MKS) framework.
• To establish robust, reliable, and reduced-order process-structure evolution linkages.
• To expedite the development and manufacturing of advanced materials.

Main Methods:

• Integration of established time series analysis techniques.
• Application within the recently developed Materials Knowledge Systems (MKS) framework.
• Demonstration using synchrotron X-ray scattering data from semi-crystalline linear low-density polyethylene films.

Main Results:

• A theoretical framework for materials process-structure evolution is presented.
• The framework successfully incorporates time series analysis into the MKS approach.
• The methodology is validated on experimental data from polymer films.

Conclusions:

• The proposed framework offers a powerful tool for understanding and predicting material behavior.
• This integration accelerates the discovery and manufacturing of materials with desired properties.
• The approach is particularly relevant for complex hierarchical structures in polymers.