Mechanism of Structure and Property Evolution of ABS During Multiple Extrusion and Aging Degree Prediction via Image Recognition Technology.

The recycling of acrylonitrile-butadiene-styrene (ABS) is crucial for a circular plastics economy, but repeated extrusion induces degradation that limits its reuse. This study establishes a comprehensive structure-property evolution mechanism for ABS 757K over five extrusion cycles and develops a novel image-recognition model for aging degree prediction. Multi-faceted characterization revealed that chain scission, oxidation of the polybutadiene (PB) phase, and the formation of chromophores led to progressive embrittlement, yellowing, and reduced thermal-oxidative stability. A key finding from Energy Dispersive Spectroscopy (EDS) was the stability and homogeneous distribution of sulfur-based antioxidants, which underpin the material's superior resistance to degradation by effectively scavenging free radicals, which function as effective free radical scavengers. This mechanism underpins the material's superior resistance to thermo-oxidative degradation. Consequently, significant molecular weight reduction and property deterioration were delayed until later extrusion cycles. Furthermore, a deep learning model based on the DeepLabV3+ architecture was trained to predict extrusion history directly from scanning electron microscopy (SEM) images of impact-fractured surfaces. The model achieved an average prediction accuracy exceeding 96.5%. Remarkably, it demonstrated excellent generalizability, maintaining high accuracy on two unseen commercial ABS grades. This indicates that the micro-morphological evolution pathway is a universal fingerprint of thermo-mechanical aging in ABS. This work not only elucidates the multi-scale degradation mechanism of recycled ABS but also provides a rapid, non-destructive tool for intelligent quality assessment in plastic recycling streams, bridging advanced machine learning with practical sustainability challenges.