Smart Rubber Manufacturing: Integrating Thermoplastic Elastomers and AI-Driven Biotechnological Modifications

Smart rubber manufacturing is undergoing rapid transformation through the convergence of advanced thermoplastic elastomers (TPEs) and artificial intelligence (AI)-enabled biotechnological modification strategies. This study explores an integrated framework that combines the tunable mechanical and thermal properties of TPE systems with data-driven optimization and bio-assisted material enhancement. Emphasis is placed on AI-supported predictive modeling for formulation design, process control, and performance forecasting, alongside enzyme-mediated and microbial modification techniques aimed at improving sustainability, recyclability, and functional adaptability. The proposed approach demonstrates how machine learning algorithms can accelerate material discovery, optimize crosslink density, enhance interfacial compatibility, and reduce waste during production. Furthermore, biotechnology-based surface and structural modifications are examined for their potential to impart self-healing, antimicrobial, and environmentally responsive characteristics. By synthesizing advances in polymer science, computational intelligence, and industrial biotechnology, this research highlights a pathway toward intelligent, sustainable, and high-performance rubber systems suitable for automotive, biomedical, and smart device applications. The findings underscore the importance of interdisciplinary integration in achieving next-generation manufacturing efficiency and eco-conscious material innovation.