Profile
Dr Cinzia Giannetti is Associate Professor in Mechanical Engineering. Cinzia’s research is driven by a passion for developing innovative technologies and tools that can have a transformational impact on our lives, society and the economy. She has been an active member of the Digital Manufacturing research community and advocate since 2010, when she transitioned from industry to academia.
Cinzia’s mission is to support the growth of the UK Manufacturing sector through development of autonomous, collaborative and intelligent production systems by using knowledge-intensive advanced digital technologies. Cinzia has been recipient of the UKRI EPSRC Digital Manufacturing Fellowship (EP/S001387/1 2018-2021) and she is Deputy Director of the Materials Made Smarter Research Centre (EP/V061798/1), where she leads digitalisation initiatives, pioneering the development and integration of AI in manufacturing industries. Cinzia is co-Investigator and part of the multidisciplinary leadership team in the EPSRC Centre for Doctoral Training in “Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems”, which train and nurture future leaders in digital data-driven innovations.
Cinzia has significant experience in delivering innovation and impact through applied industrial R&D projects gained both in industry and academia. She held positions in senior engineering roles for global companies (Siemens/Motorola/NextGen) and she has a successful track record in the delivery of Innovate UK projects.
Area of Experties
- Manufacturing Systems Engineering
- Big Data
- Manufacturing Informatics and IoTs
- Machine Learning/Deep Learning
- Artificial Intelligence
- Process Optimisation
Projects
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Predictive Analytics in Steelmaking
Project Overview Industry 4.0 represents a paradigm shift in manufacturing that leverages technologies such as Internet of Things (IoT), Artificial Intelligence (AI), big data and many others to create smart, automated and interconnected systems. Steelmaking is one example of a domain undergoing transformation. Steelmaking constitutes the many processes that are used to create the strong…
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Hybrid AI for optimisation in industry
Project Overview Hybrid AI for supply chain optimization in Industry 4.0 Supply chains (SC) are increasingly challenged by uncertainty and risks in this high-tech era. In this context, to effectively identify and mitigate SC-related risks, data, information, and knowledge from different sources must be smartly exploited. This research project focuses on optimizing supply chains through…
Specialist Areas
Events
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Registration open for event with Hartree Centre
“Extreme Scaling Computing and Emerging Computational Approaches in Science and Engineering” Find further programme details here Venue: Y Twyni, Room 105 (Bay Campus) Registration Deadline: Wed 18th September Register here The main purpose of the workshop is to scope future collaborations between Swansea and the Hartree Centre. Subjects covered include Artificial Intelligence, Automotive, Transport and Logistics, Computational Chemistry,…