Objective 1: Define Digitization Groundwork;
Models based on digital twins framework will provide machines model instance that will reflect the structural, performance, maintenance, and operational health characteristics of the physical systems.
Objective 2: Improve reliability by substantially reducing unexpected downtimes, drastically reduce maintenance costs and have a deep impact on productivity and energy efficiency;
Produce a wide set of AI algorithms tailored to system specifications and requirements/framework used for advanced predictive maintenance process of marine equipment and machinery.
Objective 3: Enable Advanced Shipbuilding Automation;
To provide a step change in shipbuilding automation by introducing robotics technology capable to perform a wide set of intelligent tasks (inspection, painting) autonomously or semi-autonomously on state-of-the-art cognitive aerial platforms.
Objective 4: Safeguard Privacy and Security of Data through distributed machine learning approach; Avoid direct data leakage through local model training, addressing the fundamental problems of privacy, ownership, and locality of data.
Objective 5: Provide a unified approach to data across operations and optimised data-driven production planning;
Objective 6: Validate the aforementioned approaches and technologies on three demonstrators involving shipyards (NO, ES, PT) with respect to maintenance and processes optimization.
Objective 7: Set a cornerstone for the building of standards in the Shipyard Industry at an EU and global level, ensuring best practices being shared for the benefit of the industry and the public; adhering to the Safety and Health in shipbuilding and ship repair;
Objective 8: Boost the exploitation potential of the project’s technologies, as well as to support the dissemination of the projects outcomes by making them accessible to the scientific community, the field experts and the general public;