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As manufacturing processes become increasingly complex, maintaining quality and improving efficiency requires mapping of process flows. Mapping process flows, in turn, depends on comprehensive end-to-end data traceability. Such traceability relies on lifecycle data that capture every stage, from raw-material handling to final-product assembly, and provide indispensable insights for process refinement. However, conventional centralized database-based systems for managing these data introduce single points of failure and remain vulnerable to tampering and cyberattacks. As a result, data traceability and authenticity are compromised. Therefore, this research develops a novel blockchain architecture coupled with digital twin (DT) model to secure end-to-end documentation of manufacturing process flows. First, a hierarchical blockchain framework is developed to record production events and ensure comprehensive, tamper-proof records of process activities. Second, the DT model, operating in collaboration with the blockchain tiers, enables real-time alignment between the manufacturing floor and its virtual twin. Third, a unified data representation is designed to transform diverse manufacturing datasets into a homogeneously structured format. Experimental results show that the proposed framework significantly enhances data authenticity while reducing the time required to map manufacturing process flows. 

Abstract Industry 4.0 drives exponential growth in the amount of operational data collected in factories. These data are commonly distributed and stored in different business units or cooperative companies. Such data-rich environments increase the likelihood of cyber attacks, privacy breaches, and security violations. Also, this poses significant challenges on analytical computing on sensitive data that are distributed among different business units. To fill this gap, this article presents a novel privacy-preserving framework to enable federated learning on siloed and encrypted data for smart manufacturing. Specifically, we leverage fully homomorphic encryption (FHE) to allow for computation on ciphertexts and generate encrypted results that, when decrypted, match the results of mathematical operations performed on the plaintexts. Multilayer encryption and privacy protection reduce the likelihood of data breaches while maintaining the prediction performance of analytical models. Experimental results in real-world case studies show that the proposed framework yields superior performance to reduce the risk of cyber attacks and harness siloed data for smart manufacturing.