The evolution of manufacturing systems from loose collections of cyber and physical components into true cyber-physical systems has expanded the opportunities for cyber-attacks against manufacturing. To ensure the continued production of high-quality parts in this new environment requires the development of novel security tools that transcend both the cyber and physical worlds. Potential cyber-attacks can cause undetectable changes in a manufacturing system that can adversely affect the product's design intent, performance, quality, or perceived quality. The result of this could be financially devastating by delaying a product's launch, ruining equipment, increasing warranty costs, or losing customer trust. More importantly, these attacks pose a risk to human safety, as operators and consumers could be using faulty equipment/products. New methods for detecting and diagnosing cyber-physical attacks will be studied and evaluated through our established industrial partners. The expected results of this project will contribute significantly in further securing our nation's manufacturing infrastructure. This project establishes a new vision for manufacturing cyber-security based upon modeling and understanding the correlation between cyber events that occur in a product/process development-cycle and the physical data generated during manufacturing. Specifically, the proposed research will take advantage of this correlation to characterize the relationships between cyber-attacks, process data, product quality observations, and side-channel impacts for the purpose of attack detection and diagnosis. These process characterizations will be coupled with new manufacturing specific cyber-attack taxonomies to provide a comprehensive understanding of attack surfaces for advanced manufacturing systems and their cyber-physical manifestations in manufacturing processes. This is a fundamental missing element in the manufacturing cyber-security body of knowledge. Finally, new forensic techniques, based on constraint optimization and machine learning, will be researched to differentiate process changes indicative of cyber-attacks from common variations in manufacturing due to inherent system variability.

Additive Manufacturing holds the promise of revolutionizing manufacturing. One important trend is the emergence of cyber additive manufacturing communities for innovative design and fabrication. However, due to variations in materials and processes, design and computational algorithms currently have limited adaptability and scalability across different additive manufacturing systems. This award will establish the scientific foundation and engineering principles needed to achieve adaptability, extensibility, and system scalability in cyber-physical additive manufacturing systems, resulting in high efficiency and accuracy fabrication. The research will facilitate the evolution of existing isolated and loosely-connected additive manufacturing facilities into fully functioning cyber-physical additive manufacturing systems with increased capabilities. The application-based, smart interfacing infrastructure will complement existing cyber additive communities and enhance partnerships between academia, industry, and the general public. The research will contribute to the technology and engineering of Cyber-physical Systems and the economic competitiveness of US manufacturing. This interdisciplinary research will generate new curricular materials and help educate a new generation of cybermanufacturing workforce. The research will establish smart and dynamic system calibration methods and algorithms through deep learning that will enable high-confidence and interoperable cyber-physical additive manufacturing systems. The dynamic calibration and re-calibration algorithms will provide a smart interfacing layer of infrastructure between design models and physical additive manufacturing systems. Specific research tasks include: (1) Establishing smart and fast calibration algorithms to make physical additive manufacturing machines adaptable to design models; (2) Deriving prescriptive compensation algorithms to achieve extensible design models; (3) Dynamic recalibration through deep learning for improved predictive modeling and compensation; and (4) Developing a smart calibration server and APP prototype test bed for scalable additive cyberinfractures.

Additive Manufacturing holds the promise of revolutionizing manufacturing. One important trend is the emergence of cyber additive manufacturing communities for innovative design and fabrication. However, due to variations in materials and processes, design and computational algorithms currently have limited adaptability and scalability across different additive manufacturing systems. This award will establish the scientific foundation and engineering principles needed to achieve adaptability, extensibility, and system scalability in cyber-physical additive manufacturing systems, resulting in high efficiency and accuracy fabrication. The research will facilitate the evolution of existing isolated and loosely-connected additive manufacturing facilities into fully functioning cyber-physical additive manufacturing systems with increased capabilities. The application-based, smart interfacing infrastructure will complement existing cyber additive communities and enhance partnerships between academia, industry, and the general public. The research will contribute to the technology and engineering of Cyber-physical Systems and the economic competitiveness of US manufacturing. This interdisciplinary research will generate new curricular materials and help educate a new generation of cybermanufacturing workforce. The research will establish smart and dynamic system calibration methods and algorithms through deep learning that will enable high-confidence and interoperable cyber-physical additive manufacturing systems. The dynamic calibration and re-calibration algorithms will provide a smart interfacing layer of infrastructure between design models and physical additive manufacturing systems. Specific research tasks include: (1) Establishing smart and fast calibration algorithms to make physical additive manufacturing machines adaptable to design models; (2) Deriving prescriptive compensation algorithms to achieve extensible design models; (3) Dynamic recalibration through deep learning for improved predictive modeling and compensation; and (4) Developing a smart calibration server and APP prototype test bed for scalable additive cyberinfrastructures.