Secured Cyber-Physical System for Distributed Additive Manufacturing of Metallic Aerospace Structural Parts

The proposed research aims to demonstrate the feasibility of a blockchain-enabled digital for secure and collaborative additive manufacturing (AM) in a distributed environment. The key innovation lies in leveraging blockchain technology, smart contracts, and digital twin (DT) integration to address critical issues hindering the widespread adoption of AM, such as data integrity, intellectual property protection, cybersecurity, and quality control. A framework will be developed to incorporate a decentralized attribute-based access control system, empowering users to maintain ownership and control over their data. Smart contracts will define data-sharing rules, manage access permissions, and ensure secure data exchange among authorized parties. The framework will also integrate federated learning with DTs, enabling collaborative model training while preserving data privacy. A novel aspect of the research is the integration of Markov Decision Processes (MDPs) with DTs, facilitating intelligent decision-making and adaptive control policies for AM processes. This approach will enable real-time adjustments to build instructions based on observed data, optimizing part quality and reducing defects. The proposed system will leverage blockchain technology to ensure digital twin model traceability, versioning control, and secure sharing across different manufacturers. Additionally, the framework will incorporate provisions for intellectual property rights management through hardware-enabled access control and real-time tracking of user-end devices. The Phase I effort will focus on the feasibility of the secure blockchain-based framework, implementing smart contracts for data access and ownership control, creating an autonomous DT prototype, and demonstrating the feasibility of integrating DT technology with blockchain-based data management. The outcome will include a robust decentralized access control framework, a DT prototype for AM processes, algorithms for data access and ownership control, and use case demonstrations. The proposed research promises to enhance overall operational efficiency, facilitate secure collaboration, and maintain data privacy in the manufacturing sector, fostering an environment of trust essential for the successful and widespread implementation of AM in a distributed manner.