Investigating the Effect of Temperature History on Crystal Morphology of Thermoplastic Composites Using In Situ Polarized Light Microscopy and Probabilistic Machine Learning

Abstract

Processing parameters including temperature history affect the morphology of semicrystalline thermoplastic composites, and in turn their performance. In addition, the competition between spherulite growth in resin-rich areas, and transcrystallinity growth from fiber surfaces, determines the final morphology. In this study, growth of crystals in low volume fraction PEEK-carbon fiber composites was studied in situ, using a polarized microscope equipped with a heating and cooling controlled stage and a probabilistic machine learning approach, Gaussian Process Regression (GPR). GPR showed that for spherulites, growth kinetics follows the established Lauritzen-Hoffman equation, while transcrystallinity growth deviates from the theory. Combined GPR model and Lauritzen-Hoffman equation were used to deconvolute the underlying competition between diffusion and secondary nucleation at growth front of spherulites and transcrystalline regions.

Keywords

• thermoplastic composites
• processing
• crystallinity
• polarized light microscopy
• machine learning