Transfer Learning for Robust, Resilient and Transferable Cyber Manufacturing Systems
Lead Research Organisation:
Swansea University
Department Name: College of Engineering
Abstract
Digital Manufacturing relies on pervasive and ubiquitous use of Information and Communication Technology (ICT), sensors, intelligent robots to deliver the next generation of intelligent, co-operating and interconnected manufacturing systems. The research is aimed to improve techniques that can be used to develop digitalised manufacturing systems to reduce existing inefficiencies in production processes that impact on production costs, unplanned downtime, quality and yields. This is not only detrimental to manufacturing businesses but has a negative impact on the UK Economy. The current productivity levels of UK manufacturers and suppliers is lagging behind global competitors and prevents the UK from successfully competing with other countries in the manufacturing domain - which is vital to keep businesses and jobs in the UK rather than relocate production abroad.
The UK Government wants to increase the strength of the UK Manufacturing Sector. A key means of doing this is the widespread adoption of industrial digital technologies (IDT). Cyber Manufacturing Systems (CMS) are the building blocks of digitalised manufacturing and generate vast amount of data that can be used for real time decision making to achieve optimised performance through predictive and prescriptive analytics. The latter are techniques that use, combine and analyse available data to develop computational models that can predict future outcomes and determine the best course of action.The research, under the fellowship, solves some of the existing problems in this area (CMS), developing new techniques and resources for predictive and prescriptive analytics with the potential to increase efficiency, accuracy and productivity of manufacturing processes. Businesses are therefore more likely to adopt IDTs and improve profitability and sustainability and provide high-quality jobs in a thriving part of the economy.
This project will study novel and robust data analytics methods that will enable to build predictive models that take into account uncertainty, complexity and dynamic behaviour of productions systems. The project will involve:
Objective 1 - develop algorithms that can reuse previously acquired data/knowledge to build more accurate predictive models that work well in the presence of noise (i.e. 'robust'), are able to adapt to changes over time (i.e. 'resilient') and can be scaled up across multiple factories (i.e. 'transferable').
Objective 2 - develop and test novel non-parametric methods for estimation of uncertainty and risks associated to a decision to enable real time mission and safety critical decision making (both automated and human driven) based on predictions.
Objective 3 - iteratively develop, deploy and test predictive and prescriptive models in real and simulated industrial scenarios to obtain acceptable level of performance, usability and robustness.
There will be significant involvement from industrial collaborators who will provide labelled and aggregated datasets for testing the proposed methods through computer simulations and enable feasibility studies to be conducted in factory environments.
The outcomes of the research, as mentioned above, are ultimately to improve the quality of products, achieving less wastage and unnecessary costs. Through increased adoption of IDTs, the production of goods will, importantly, be more efficient, reliable and profitable. This will support the regeneration of the Manufacturing Sector and boost the global competitiveness of the UK.
The UK Government wants to increase the strength of the UK Manufacturing Sector. A key means of doing this is the widespread adoption of industrial digital technologies (IDT). Cyber Manufacturing Systems (CMS) are the building blocks of digitalised manufacturing and generate vast amount of data that can be used for real time decision making to achieve optimised performance through predictive and prescriptive analytics. The latter are techniques that use, combine and analyse available data to develop computational models that can predict future outcomes and determine the best course of action.The research, under the fellowship, solves some of the existing problems in this area (CMS), developing new techniques and resources for predictive and prescriptive analytics with the potential to increase efficiency, accuracy and productivity of manufacturing processes. Businesses are therefore more likely to adopt IDTs and improve profitability and sustainability and provide high-quality jobs in a thriving part of the economy.
This project will study novel and robust data analytics methods that will enable to build predictive models that take into account uncertainty, complexity and dynamic behaviour of productions systems. The project will involve:
Objective 1 - develop algorithms that can reuse previously acquired data/knowledge to build more accurate predictive models that work well in the presence of noise (i.e. 'robust'), are able to adapt to changes over time (i.e. 'resilient') and can be scaled up across multiple factories (i.e. 'transferable').
Objective 2 - develop and test novel non-parametric methods for estimation of uncertainty and risks associated to a decision to enable real time mission and safety critical decision making (both automated and human driven) based on predictions.
Objective 3 - iteratively develop, deploy and test predictive and prescriptive models in real and simulated industrial scenarios to obtain acceptable level of performance, usability and robustness.
There will be significant involvement from industrial collaborators who will provide labelled and aggregated datasets for testing the proposed methods through computer simulations and enable feasibility studies to be conducted in factory environments.
The outcomes of the research, as mentioned above, are ultimately to improve the quality of products, achieving less wastage and unnecessary costs. Through increased adoption of IDTs, the production of goods will, importantly, be more efficient, reliable and profitable. This will support the regeneration of the Manufacturing Sector and boost the global competitiveness of the UK.
Planned Impact
The UK Government has crystallised the necessity for growth in the manufacturing sector via the adoption and integration of industrial digital technologies [1, 2]. The Government's aspiration is for the UK to become a World leader in Digital Manufacturing by 2030. However, at present the manufacturing sector has a 20% deficit in productivity compared to its main European competitors. Against this backdrop, my research will help to combat the UK productivity gap, stimulating the incorporation of digital technology within UK industry. Cyber Manufacturing Systems (CMS) are the foundations of digitalised production systems - my research will develop new machine learning techniques and toolkits for robust, resilient and transferable CMS. This will manifest in improved understanding of the benefits of IDTs adoption, and their value,and inspire confidence for manufacturers and suppliers to invest in this area. The beneficiaries of my research are a) the UK Economy, b) Organisations within the UK and International Manufacturing Sector, c) members of the public.
The UK Economy will ultimately benefit from my research, as aforementioned, via uptake in industry and the subsequent increase in productivity within the Manufacturing Sector (also potentially leading to job creation and re-shoring to the UK). This will foster global competitiveness, whilst helping to realise well publicised political aims.
Organisations within the UK and International Manufacturing Sector, beginning with my industry collaborators (TATA Steel, Crown Packaging and Vortex IoT), will prosper in terms of economic enhancement. My work during this Fellowship will bequeath new and improved processes that can be implemented in multiple manufacturing domains. This will achieve increased accuracy, efficiency and flexibility, less wastage of products through quality improvement, reduced costs and enhanced profit margins through failure reduction. Corporate performance will be markedly improved. This will have strong impact, particularly on SMEs, supporting sustainability, investment and expansion.
Members of the public will be engaged, through the outreach activity that is integrated in the Fellowship. This includes Science Festivals and initiatives such as the Fujitsu Young Ambassadors Programme. The dissemination of the research will acutely increase public awareness and comprehension of Engineering and the relevance of industrial digital techniques in the Manufacturing Sector. Specific audiences that will be targeted, in alignment with my commitment to developing the future workforce and supporting economic regeneration, will be School Children and those in Further Education.
In short, the research will produce a range of far reaching impacts that will culminate to drive forward economic prosperity; help to promote and propel the incorporation of digital technologies within the Manufacturing arena; and complement the Governments agenda for achieving productivity growth and economic competitiveness.
[1] UK Government. Building Our Industrial Strategy Green Paper. 2017 (https://beisgovuk.citizenspace.com/strategy/industrial strategy/supporting_documents/buildingourindustrialstrategygreenpaper.pdf)
[2] Group IDRW, Made Smarter Review 2017 (https://www.gov.uk/government/publications/made-smarter-review)
The UK Economy will ultimately benefit from my research, as aforementioned, via uptake in industry and the subsequent increase in productivity within the Manufacturing Sector (also potentially leading to job creation and re-shoring to the UK). This will foster global competitiveness, whilst helping to realise well publicised political aims.
Organisations within the UK and International Manufacturing Sector, beginning with my industry collaborators (TATA Steel, Crown Packaging and Vortex IoT), will prosper in terms of economic enhancement. My work during this Fellowship will bequeath new and improved processes that can be implemented in multiple manufacturing domains. This will achieve increased accuracy, efficiency and flexibility, less wastage of products through quality improvement, reduced costs and enhanced profit margins through failure reduction. Corporate performance will be markedly improved. This will have strong impact, particularly on SMEs, supporting sustainability, investment and expansion.
Members of the public will be engaged, through the outreach activity that is integrated in the Fellowship. This includes Science Festivals and initiatives such as the Fujitsu Young Ambassadors Programme. The dissemination of the research will acutely increase public awareness and comprehension of Engineering and the relevance of industrial digital techniques in the Manufacturing Sector. Specific audiences that will be targeted, in alignment with my commitment to developing the future workforce and supporting economic regeneration, will be School Children and those in Further Education.
In short, the research will produce a range of far reaching impacts that will culminate to drive forward economic prosperity; help to promote and propel the incorporation of digital technologies within the Manufacturing arena; and complement the Governments agenda for achieving productivity growth and economic competitiveness.
[1] UK Government. Building Our Industrial Strategy Green Paper. 2017 (https://beisgovuk.citizenspace.com/strategy/industrial strategy/supporting_documents/buildingourindustrialstrategygreenpaper.pdf)
[2] Group IDRW, Made Smarter Review 2017 (https://www.gov.uk/government/publications/made-smarter-review)
Publications
Essien A
(2020)
A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders
in IEEE Transactions on Industrial Informatics
Giannetti C.
(2019)
A novel deep learning approach for event detection in smart manufacturing
in Proceedings of International Conference on Computers and Industrial Engineering, CIE
Giannetti C.
(2019)
A Novel Deep Learning Approach for Event Detection in Smart Manufacturing
O'Donovan C.
(2021)
A novel deep learning power quality disturbance classification method using autoencoders
in ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence
Lakshmanan K
(2023)
A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods
in AI
Todeschini G
(2022)
An image-based deep transfer learning approach to classify power quality disturbances
in Electric Power Systems Research
Flynn J
(2023)
Anomaly Detection of DC Nut Runner Processes in Engine Assembly
in AI
Griffiths C
(2022)
Comparison of a Bat and Genetic Algorithm Generated Sequence Against Lead Through Programming When Assembling a PCB Using a Six-Axis Robot With Multiple Motions and Speeds
in IEEE Transactions on Industrial Informatics
Borghini E
(2021)
Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation
in Energies
Giannetti C
(2024)
Deep learning for robust forecasting of hot metal silicon content in a blast furnace
in The International Journal of Advanced Manufacturing Technology
Cao Q
(2022)
KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0
in Robotics and Computer-Integrated Manufacturing
O'Donovan C
(2023)
Ladle pouring process parameter and quality estimation using Mask R-CNN and contrast-limited adaptive histogram equalisation
in The International Journal of Advanced Manufacturing Technology
Giannetti C
(2019)
Machine Learning as a universal tool for quantitative investigations of phase transitions
in Nuclear Physics B
Aldoumani N
(2020)
Optimisation of the Filament Winding Approach Using a Newly Developed In-House Uncertainty Model
in Eng
Andrzejewski K
(2018)
Optimisation process for robotic assembly of electronic components
in The International Journal of Advanced Manufacturing Technology
Latham S.
(2022)
Root Cause Classification of Temperature-related Failure Modes in a Hot Strip Mill
in IN4PL - Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics
Li C
(2019)
Segmentation and generalisation for writing skills transfer from humans to robots
in Cognitive Computation and Systems
| Description | The research, under the Fellowship, explored the application of Machine Learning and Transfer Learning to various manufacturing domains (Steel, Metal Packaging and new smart sensors). Through the collaborative work with the industrial partners, the Fellowship has demonstrated the application of Deep Learning to the manufacturing domain to achieve improvement of yields and higher efficiencies. These developments have been successfully demonstrated to different areas of manufacturing industry including: i) detect reduction in steel making processes: ii) optimisation of the performance of Blast Furnace; iii) detection for the automated analysis of production inefficiencies in can manufacturing and iv) optimization of assembly processes. Furthermore, the use of Deep Learning has been demonstrated in 'real world prototype' for monitoring traffic/air quality. A novel deep learning architecture has been proposed for multi-step ahead prediction of machine speed using real world data from an UK manufacturing plant. A novel method to apply Transfer learning for timeseries prediction in Smart Manufacturing has been developed and open source code published on a public repository. The research also explored the application of Deep Learning to other domains such as energy and automotive sectors. Industrial partners have provided insights on the challenges they face related to adoption of IDTs and provided datasets and use cases to evaluate the proposed methodologies. The engagement generated high quality publications and further funding opportunities such as the Made Smarter Innovation - Materials Made Smarter Centre. Industrial input has been crucial to obtaining new grant funding to continue to address challenges in adoption of IDTs. The Fellowship has also provided opportunities for industrial partners to discover gaps in their digitalization strategies and, consequently, address these issues, leading to improved data collection methods and use of IDTs. The fellowship has provided further insights into novel research areas. In particular it was found that effective adoption of AI in manufacturing processes requires a better integration of data-generated and human knowledge. Future research effort of the team will focus on the interplay between machine learning and human-generated knowledge allowing the development of human centric cognitive manufacturing systems that, through better utilization of data and knowledge, can self-optimize, reducing inefficiencies and waste, hence contributing towards the development of sustainable factories. |
| Exploitation Route | The Fellowship findings can help other organizations to tackle their productivity gap and achieve sustainability objectives through adoption of Industrial Digital Technologies. The techniques that have been developed during the fellowship can be of benefits to other industrial domains such as offshore renewable energy systems, smart grid, and sustainable infrastructure. |
| Sectors | Energy Environment Manufacturing including Industrial Biotechology |
| Description | The grant research findings led to the development of novel data-driven methodologies to support decision making in manufacturing and improve the quality of products, achieving less wastage and avoid unnecessary costs. The research activities and knowledge exchange programme have helped partners organizations to improve their understanding of the benefits of adoption of big data technologies in their factories. As a result, partners organizations have become more confident to invest in this area. Industrial partners have made improvements to their digitalization strategies to address some of the challenges identified during this research, hence they have increased their competitiveness. The research team has successfully demonstrated the application of Deep Learning technologies to different application domains (steel making, packaging and automotive). Further research will investigate the deployment of some of these use cases on real-world processes to assess the economic impact of these technologies. The research team has actively engaged with wider industries and professional networks through participation and organization of knowledge exchange events, leading to increased awareness of the potential of Industrial Digital Technologies to drive improvements in the Manufacturing Sector. |
| First Year Of Impact | 2021 |
| Sector | Manufacturing, including Industrial Biotechology |
| Impact Types | Societal Economic |
| Description | EPSRC Centre for Doctoral Training in Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems |
| Amount | £4,986,846 (GBP) |
| Funding ID | EP/S021892/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 03/2019 |
| End | 09/2027 |
| Description | Innovate UK |
| Amount | £255,653 (GBP) |
| Funding ID | TS/S015183/1 |
| Organisation | Innovate UK |
| Sector | Public |
| Country | United Kingdom |
| Start | 03/2019 |
| End | 04/2021 |
| Description | Made Smarter Innovation - Materials Made Smarter Research Centre |
| Amount | £4,049,203 (GBP) |
| Funding ID | EP/V061798/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 08/2021 |
| End | 02/2025 |
| Title | 2D-Convolutional LSTM Autoencoder |
| Description | The research team has developed and published a novel deep ConvLSTM autoencoder architecture for machine speed prediction in a smart manufacturing process. The model has been published in IEEE Transactions on Industrial Informatics "A. E. Essien and C. Giannetti, "A Deep Learning model for Smart Manufacturing using Convolutional LSTM Neural Network Autoencoders.". This code implements a ConvLSTM-based neural network (known as 2D-ConvLSTMAE) for multi-step machine speed prediction. The proposed architecture combines a stacked ConvLSTM encoder model with a bidirectional LSTM decoder network to learn representatively using an encoder-decoder architecture. A GitHub repository for the model will be released open source. Unfortunately, this dataset cannot be made publicly available. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | This is the first end to end deep learning model that has been developed for multi-step ahead machine speed time series prediction. In smart factories, machine speed prediction can be used to dynamically adjust production processes, enabling improvement of production scheduling and planning. The model has been validated in a case study using real-world data obtained from a metal packaging plant in the UK. |
| Title | PV Data Challenge |
| Description | Code of the open data challenge competition organized by the Energy Catapult, Western Power Distribution and the Centre for Sustainable Energy. The task was to develop an optimal control strategy for a battery storage device to support the distribution network with the aim to maximise the daily evening peak reduction using as much solar photovoltaic energy as possible. The challenge was spread over 7 weeks and include 5 tasks (including a practice task) and involved 55 teams of 142 individuals from 72 different institutions and 37 countries. Our team achieved the third place of the leader board and we have been invited to publish our results on the MDPI journal Energies. Borghini, E.; Giannetti, C.; Flynn, J.; Todeschini, G. Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation. Energies 2021, 14, 3453. https://doi.org/10.3390/en14123453 The code is under refactoring and the code will be made available as soon as it is ready. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2022 |
| Provided To Others? | No |
| Impact | The code model was submitted to the open data challenge competition organized by the Energy Catapult, Western Power Distribution and the Centre for Sustainable Energy and achieved 3rd place. It raised interest across industrial partners who were supporting the challenge. |
| Title | TL4SM Generic Model for applying Transfer Learning to timeseries data |
| Description | TL4SM is a Python package for performing TL between multivariate (continuous) time series using data from various similar, but not necessarily related sources. The package uses as baseline a ConvLSTM autoencoder model presented in our paper entitled "A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders". It has been released as open source code. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | The package has been used to predict speed of machines in industrial environment for process scheduling applications |
| URL | https://github.com/cinziagiannetti/tl4sm_generic |
| Description | Collaboration with Crown Holdings |
| Organisation | Crown Packaging UK |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Research was undertaken to develop novel Machine Learning techniques to support root cause analysis in can production processes to reduce waste and avoid unnecessary costs. Contributions include: development of two use cases, regular meetings, knowledge exchange and visits to the can making plant and R&D offices. The main outcome of the plant visit was to develop awareness of the possible application of big data technologies for improvement of can making processes and the development of two use cases. The research led to publications of two conference papers and two journal papers. The research undertaken led to the identification of limitations of data collection methods with subsequent improvements being made by the industrial partner. Further deployment and testing of the Machine Learning methods was not possible due to the travel and site access restrictions caused by the pandemic. |
| Collaborator Contribution | Crown Technology have actively contributed by providing data sets and participating in brainstorming sessions. Crown also provided access to their facilities. |
| Impact | Knowledge Exchange - The knowledge exchange has been useful to Crown R&D and plant team to better understand the potential in developing advanced predictive models that can support decision making in their plants. Crown has improved their data collection methods leading to enhancements of their digital systems towards realization of Industry 4.0 capabilities. The research has demonstrated the potential application of Machine Learning to production scheduling and improvements of manufacturing processes, providing insights towards effective adoption of data-driven innovations in their manufacturing plants. |
| Start Year | 2018 |
| Description | Collaboration with Vortex IoT for LiDAR object detection |
| Organisation | Vortex IoT |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Dr Giannetti has actively collaborated with Vortex IoT in the development of Machine Learning applications for LiDAR object detection. The research was funded through an Innovate UK proposal 'PARSER: Parking and AiR pollutionSEnsoRs for Smart Cities'. |
| Collaborator Contribution | Research to support the development of a LiDAR based object detection systems has been carried out. The research included the deployment of a test facility to collect ground truth data and the development of a Machine Learning model for object detection. |
| Impact | Knowledge exchange and initial research in LiDAR object detection. A methodology to acquire LiDAR dats and train a Machine Learning model. Ground truth data for training. |
| Start Year | 2019 |
| Description | Tata Steel Collaboration |
| Organisation | TATA Steel |
| Country | India |
| Sector | Private |
| PI Contribution | Research was undertaken to develop novel Machine Learning techniques to support decision making in the steel manufacturing industry. The research was demonstrated on three case studies: i) data driven optimization of Blast Furnace; ii) digital twin of cold rolling process; iii) prediction of hot metal silicon content in Blast Furnace. Knowledge exchange activities have been organised and delivered. The research team has provided advice and support to Tata Steel employees, contributing to use case and development of skills and knowledge related to the application of data-driven approaches in manufacturing. |
| Collaborator Contribution | TATA has actively engaged in meetings and provided real world data sets and access to their facilities. |
| Impact | The insights generated through the proposed data analytics technique have allowed the company to modify the process, achieving improvement of quality and reduction of costs. |
| Start Year | 2018 |
| Description | "Machine Learning in Metallurgy" IOM3 talk |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Online seminar organised by IOM. I presented a talk with title "Machine Learning in Metallurgy" alongside two other speakers. The event featured presentations from academia and industry with focus on understanding the use of machine learning in metallurgy, it's impact on industry and the development of machine learning in the UK. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://www.iom3.org/events-awards/ems-event-calendar/machine-learning-in-metallurgy.html |
| Description | CHERISH-DE funded outreach activity at Cambridge University |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Undergraduate students |
| Results and Impact | Participation to the development of an Outreach activity with colleagues from other institutions (Cambridge University, De Montford University, Queen's University Belfast), undertaking chemistry experiments using bubble wrap and mobile phones. |
| Year(s) Of Engagement Activity | 2019 |
| Description | Chemistry in a Bubble Wrap |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Schools |
| Results and Impact | Outreach activity video developed to show use of mobile technology as a tool for chemical testing. |
| Year(s) Of Engagement Activity | 2019 |
| Description | Digital Manufacturing 2050 Roundtable - Connected Everything - Nottingham University |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Dr Giannetti participated (by invitation) to Connected Everything Digital Manufacturing 2050 roundtable panel. This panel discussion is part of horizon scanning activities conducted by the Connected Everything network with industrial partners and policymakers. The discussion was around the following topics: 1. Current and Future Manufacturing Challenges 2. Current and Future Perspectives Towards Industrial Digital Technologies 3. Emerging Industrial Digital Technologies 4. Policy, Initiatives, and Support to Improve Uptake of Industrial Digital Technologies The aim of the discussion was to identify key points and recommendations surrounding the above mentioned topics that will inform a foresight report on Digital Manufacturing 2050 which will be published later this year. Dr Giannetti contributed to the discussion and provided insights that may inform future policies and research directions. |
| Year(s) Of Engagement Activity | 2022 |
| Description | EPSRC ECR Forum Meeting - June 2021 |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Dr Giannetti organised and chaired the 26th meeting of the EPSRC Early Career Forum in Manufacturing. The virtual meeting was held across two days. This is a networking and career development event for early career researchers whose research is aligned to the EPSRC theme Manufacturing The Future. The following topics were covered: ECR Personal Development, University Partnerships and Outreach. The main outcome of the event is to inform ECR of current topics relevant to their career development, influencing future choices. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://ecfmanufacturing.com/2021/07/16/26th-meeting-of-the-epsrc-early-career-forum-in-manufacturin... |
| Description | Early Career Forum in Manufacturing |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Dr Giannetti is a member of the Early Career Forum in Manufacturing. Through membership and participation to activities she interacts with other researchers and EPSRC Portfolio Manager in manufacturing. |
| Year(s) Of Engagement Activity | 2020 |
| Description | IOM3 Sir Henry Bessemer Master class 2019 - Invited lecture |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Industry/Business |
| Results and Impact | Invited lecture entitled "Are you ready for Industry 4.0?", which included group discussions around Industry 4.0 and the steel industry with focus on steel in the digital world and what a digitalized UK steel supply chain could look like. |
| Year(s) Of Engagement Activity | 2019 |
| Description | IOM3 interview published on IO3M online magazine |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | The fellowship research activity was featured on the IOM3 online magazine. The interview appeared online in Dec 2018. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://www.iom3.org/materials-world-magazine/news/2018/dec/10/pursuing-smart-factory |
| Description | IROHMS Future Leaders Academy - EPSRC Fellowships Workshop talk |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Professional Practitioners |
| Results and Impact | Dr Giannetti talked about her experience of applying for a Fellowship grant and participated to Q&A as a panelist. She had the opportunity to share her experience with Early career researchers and also promote research opportunities and career development of researchers. |
| Year(s) Of Engagement Activity | 2020 |
| Description | Industry 4.0 & Beyond - invited talk organised by the South Wales Material Association |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Professional Practitioners |
| Results and Impact | Dr Giannetti gave a general purpose talk aimed at the wider professional network organised by the South Wales Material Association on 24th February 2021 (online). The talk was promoted online and about 100 people signed up for the event. Following the talk, several professionals contacted me to ask further questions and to develop potential collaborations in the field of Industry 4.0. |
| Year(s) Of Engagement Activity | 2021 |
| Description | International Women in Engineering Day Building Smart Things Blog |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Undergraduate students |
| Results and Impact | Blog of Dr Giannetti's personal journey to becoming Associate Professor and EPSRC Research Fellow. |
| Year(s) Of Engagement Activity | 2019 |
| URL | http://engineeringimpact.co.uk/impact_post/international-women-in-engineering-day-building-smart-thi... |
| Description | Invited lecture at the Tata MAGNET Symposium |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Invited lecture on Industry 4.0 at the MAGNET Symposium. The lecture was well received and generated many questions and interest around the topic. I was asked to attend future events. |
| Year(s) Of Engagement Activity | 2020 |
| Description | Knowledge Exchange Workshop entitled "Digital Transformation in Manufacturing & Beyond" - 30th October 2019 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Industry/Business |
| Results and Impact | This workshop at Swansea University brought academic and industrial participants together to discuss opportunities and barriers to the adoption of AI-based solutions in manufacturing and its social and economic implications. Participants undertook interactive activities to manually learn and reflect on the following topics: • State of the art and current solutions for AI-driven manufacturing • Specific needs of industry for deployment of AI systems in manufacturing • Human-centric AI in Smart Factories • Routes to lower adoption barriers • Industry and academia collaboration to co-create solutions and innovate by building trustworthy AI systems A panel discussion with Q&A session was followed by two use case scenario based-tasks. The outcome of the workshop was a challenge briefing document, including the group discussion activities, which was circulated to participants to highlight challenges, expertise and resources needed in three main areas: i) machine learning; ii) making a business case; iii) human factors: iv) data. The report has been circulated to participating companies with the view of developing future collaboration in these thematic areas. |
| Year(s) Of Engagement Activity | 2019 |
| Description | Super Science Kit - Swansea Science Festival - 2021 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Public/other audiences |
| Results and Impact | The Fellowship team developed a public engagement activity that was featured at the Swansea Science Festival 2021. The activity involved hands-on science experiments enabled by mobile technology. A tutorial video of the experiment was provided https://www.youtube.com/watch?v=-wWLkiz-gZU The activity also involved the assembly and distribution of 50 'Super Science Kits' for registered visitors to collect at the event - recipients would then follow along with the online experiment at home. All 50 kits were reserved with the success of the activity leading to the festival organisers requesting a repeat activity for 2022. A dedicated hashtag was used for publicity on social media and the 'Super Scientists' were asked to send in images of their kits in use at home! |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://engineeringimpact.co.uk/impact_post/swansea-science-festival-super-science-kits/ |
| Description | The Times Future Manufacturing supplement |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Dr Giannetti's comments have been included in the Times "Raconteur" supplement (19/02/19) on the Future of Manufacturing. The piece is on page 3 (https://www.raconteur.net/manufacturing/hardware-software-manufacturing) and it asks whether manufacturers should make investments in hardware or software technologies to remain competitive. The Times is a prestigious outlet which reaches around 1 million people a day - print and online combined so the piece has a national and international reach. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://www.raconteur.net/manufacturing/hardware-software-manufacturing |
| Description | Web site - TRANSFER LEARNING FOR ROBUST AND RESILIENT CYBER MANUFACTURING SYSTEMS |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | A website was built to showcase the research and engagement activities for wider dissemination. The website also contains lists to blog posts made by the fellow https://engineeringimpact.co.uk/search-results/?_sft_impactstaff=dr-cinzia-giannetti The website has made visible the research outputs delivered through the fellowship, leading to further engagement. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://www.swansea.ac.uk/engineering/impact/-research-at-impact/transfer-learning-for-robust-and-re... |
| Description | Workshop: Industrial Digital Technologies (IDTs) adoption in the COVID Pandemic |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Industry/Business |
| Results and Impact | An industrial engagement workshop was organized by the fellowship team on 20th October 2021. The workshop was led by Dr Cinzia Giannetti and examined the challenges, successes and failures of the rapid adoption of Industrial Digital Technologies during the COVID Pandemic era and invited participants to explore the lessons learned that can be carried forward. Panel members included: • Alex Carr, Process Technology Specialist at Tata Steel Europe • Yew Onn Pang, Director of Engineering at Crown Technology • Sharadha Kariyawasam, Chief Innovation Officer at Vortex • Eugenio Borghini, Postdoctoral Researcher in Industrial AI, Swansea University The format consisted of a project update followed by guest presentations from the panel and a Q&A led by CG. 43 delegates registered for the event including delegates representing industry and academia. The workshop was an opportunity to showcase the work of the fellowship and, at the same time, helped to shape future directions of research beyond the fellowship. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://engineeringimpact.co.uk/impact_post/workshop-industrial-digital-technologies-idts-adoption-i... |