The 4th Digital Revolution (4IR): towards a new era of digitalised industrial production

1:00pm - 2:00pm / Wednesday 25th April 2018 / Venue: ELT Electrical Engineering & Electronics
Type: Lecture / Category: Research / Series: WiT (Women in Technology) Lecture Series
  • 0151 795 4297
  • Suitable for: Staff and students within the Faculty of Science & Engineering, and anyone else with an interest in the subject.
  • Admission: Free
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Cyber-physical production systems are expected to generate vast amount of in-process data and revolutionise the way data, knowledge and wisdom are captured and reused in manufacturing industries. The goal is to increase efficiency and productivity by dramatically reducing the occurrence of unexpected process results and waste.
In this talk, Dr Giannetti will give an overview of enabling technologies underpinning the digital transformation of production processes, also called the 4th Industrial Revolution (4IR). Among these technologies, she will focus on Industrial Big Data and the challenges that arise when using (big or small) data to optimise manufacturing operations. Through a practical case study, she will show how data can be used to develop predictive models of complex manufacturing systems and discover new insights that can support decision making capabilities, ultimately leading to increased robustness of manufacturing processes.

Dr Cinzia Giannetti is a Senior Lecturer in the College of Engineering, Swansea University. She is a member of the Advanced Manufacturing Group and the Zienkiewicz Centre for Computational Engineering (ZC2E). After a decade in industry, she transitioned to academia, motivated by her passion for innovative fundamental research and translating this into practical knowledge to create socio-economic impact. She joined Swansea University in 2010, initially working as a Research Assistant in the ASTUTE project (https://www.astutewales.com/en/). Whilst working in the ASTUTE project, she studied towards an Engineering Doctorate (EngD), awarded in 2015. Her thesis “Knowledge Driven Approaches for Defect Reduction and Continual Improvement” focuses on the application of data mining and statistical analysis technique to engineering data to discover new process knowledge. During her career, Cinzia has developed significant experience in delivering and supporting applied industrial R&D projects gained in both industry and academia. Before moving to academia, she has worked in industry in senior technical roles delivering, planning and coordinating software development projects for existing systems and new products. Her research interests include the development of autonomous, collaborative and intelligent production systems by using knowledge-intensive advanced ICT technologies and sensors. Her expertise includes smart manufacturing and sensors technologies, with emphasis on knowledge and information management, data analytics and machine learning techniques to support decision making, through the use of sensors and large-scale databases.