Integrating Digital Twins, Smart Materials, and Human Machine Collaboration for Sustainable Smart Manufacturing: Smart CNC & Industry 4.0 Applications - Sandeep Kumar Chouksey (International Journal of Manufacturing and Production Engineering, 2025)
The rapid evolution of Industry 4.0 and the emerging transition toward Industry 5.0 have been catalyzed by the convergence of intelligent digital technologies such as digital twins, cyber–physical systems (CPS), artificial intelligence (AI), the Internet of Things (IoT), and human-in-the-loop (HITL) frameworks. These technologies have transformed traditional manufacturing into adaptive, data-centric ecosystems capable of real-time optimization and predictive decision-making. In recent years, the fusion of computer numerical control (CNC) machines, nanocellulose-based smart materials, and cloud-enabled manufacturing analytics has expanded the operational intelligence of smart factories. Additionally, the advent of AI-generated content (AIGC) and ontology-driven architectures has introduced new paradigms for knowledge representation, semantic interoperability, and self-adaptive industrial systems Despite these advancements, a significant research gap persists in integrating multi-modal sensing, digital twin-assisted learning environments, and agentic workflow automation into a unified, interoperable framework. Addressing this gap is crucial for realizing holistic industrial intelligence, where data, algorithms, and human expertise co-evolve symbiotically. This paper proposes a multi-layered framework that harmonizes software–hardware co-design, adaptive material intelligence, and collaborative human–machine interactions to enhance manufacturing resilience and sustainability. The proposed framework bridges industrial-scale foundation models with adaptive sensing networks and real-time control architectures, creating a scalable infrastructure for intelligent decision-making, energy-efficient operations, and agile supply-chain management. Through this integration, the study outlines pathways for embedding contextual awareness, self-learning mechanisms, and cognitive adaptability within industrial ecosystems. Ultimately, this approach extends beyond the automation-driven logic of Industry 4.0, envisioning an Industry 5.0 paradigm where human creativity, sustainability, and ethical intelligence are central to manufacturing innovation. The findings and conceptual model presented herein aim to guide future research toward developing resilient, human-centric, and sustainable smart factories powered by continuous human–machine collaboration.
Keywords: AIGC; Digital Twin; Industry 4.0; Industry 5.0; cyber-physical systems; human-in-the-loop; intelligent decision-making; multi-sensor fusion; nano cellulose packaging; smart manufacturing; sustainable production