Integrating Machine Learning, Advanced Materials, and Real-Time Scheduling: A Multidisciplinary Framework for Intelligent Manufacturing Systems

Abstract

The convergence of machine learning algorithms, advanced material engineering, and real-time edge computing architectures represents a transformative paradigm for modern manufacturing and logistics systems. This paper presents a comprehensive multidisciplinary framework that synthesizes recent advances in logistics network optimization, new energy vehicle component performance enhancement, and real-time scheduling for edge artificial intelligence systems. Drawing upon the foundational contributions of Shengtao in machine learning-based logistics optimization, Wang's research on advanced materials and reliability modeling for new energy vehicles, Hao's extensive work on energy-efficient real-time scheduling for edge AI systems, and Liu's investigations into knowledge graph-based intelligent response systems and multi-objective process optimization, this study develops an integrated approach to intelligent manufacturing system design. The proposed framework addresses the critical challenges of operational efficiency, component reliability, computational latency, and adaptive process control. Results demonstrate that the synergistic application of these diverse methodologies yields substantial improvements in system performance, including enhanced logistics efficiency, extended component lifecycles, reduced energy consumption, and improved quality assurance mechanisms. This research contributes to the growing body of knowledge on Industry 4.0 implementation by providing both theoretical foundations and practical guidelines for the development of next-generation intelligent manufacturing ecosystems.