A Neural Network-Based Method for Surface Metallization of Polymer Materials

It’s no secret that polymers have been employed extensively in a variety of industries. Polymers, on the other hand, have faced difficulties in their development because of their complicated chemical composition and structure. Data-driven approaches in polymer science and technology have resulted in new directions in research leading to the implementation of deep learning models and vast data assets. In the growing area of polymer informatics, deep learning methods based on factual data are being used to speed up the performance assessment and process improvement of new polymers. Using a deep neural network (DNN), we can now forecast the surface metallization properties of polymer materials, which we describe in this research. First, we collect a raw dataset of polymer materials’ characteristics. The raw data are filtered and normalized using the min–max normalization approach. To convert normalized data into numerical characteristics, principal component analysis (PCA) is employed. Polymer surface metallization characteristics can then be predicted using a suggested DNN technique. The proposed and conventional approaches are also compared so that our research can be done to its full potential.