AI-Optimized Polymer Manufacturing: Data-Driven Personalization For E-Commerce Materials

Abstract

The growing demand for personalization in e-commerce, coupled with rising sustainability concerns, is reshaping the future of polymer manufacturing. This study investigates the integration of artificial intelligence (AI) into polymer production to optimize material performance, enhance consumer-driven personalization, and align with circular economy goals. Using machine learning, deep learning, and reinforcement learning models, polymer processing parameters such as extrusion temperature, screw speed, and additive ratios were optimized to improve tensile strength, durability, and defect reduction. Consumer preference data were analyzed to identify five distinct market segments, each exhibiting unique priorities in customization, sustainability, and durability. Experimental trials validated AI predictions, while statistical analyses, including MANOVA, PCA, regression models, and cluster validation, confirmed the robustness of the results. AI-optimized polymers demonstrated significant reductions in carbon footprint, energy use, and waste generation while improving recyclability and biodegradability. The findings underscore AI’s capacity to transform polymer manufacturing into a demand-responsive, sustainable, and consumer-centric process, offering practical implications for industries seeking to adapt to rapidly evolving e-commerce markets.