Digital Toolkit for optimisation of operators and technology in manufacturing partnerships (DigiTOP)
Lead Research Organisation:
University of Nottingham
Department Name: Faculty of Engineering
Abstract
The manufacturing industry, with the drive towards 'Industrie 4.0', is experiencing a significant shift towards Digital Manufacturing. This increased digitisation and interconnectivity of manufacturing processes is inevitably going to bring substantial change to worker roles and manual tasks by introducing new digital manufacturing technologies (DMT) to shop floor processes. At the same time, the manufacturing workforce is itself also changing - globally and nationally - comprising of an older, more mobile, more culturally diverse and less specialist / skilled labour pool.
It may not be enough to simply assume that workers will adopt new roles bestowed upon them; to ensure successful worker acceptance and operational performance of a new system it is important to incorporate user requirements into Digital Manufacturing Technologies design. In the past, Human Factors has shaped the tools used in manufacturing, to make people safe, to make work easy, and to make the workforce more efficient. New approaches to capture and predict the impact of the changes that these new types of technologies, such as robotics, rapidly evolvable workspaces, and data-driven systems are required. These approaches consist of embedded sensor technologies for capture of workplace performance, machine learning and data analytics to synthesise and analyse these data, and new methods of visualisation to support decisions made, potentially in real-time, as to how digital manufacturing workplaces should function.
The DigiTOP project will develop the new fundamental knowledge required to reliably and validly capture and predict the performance of a digital manufacturing workplace, integrating the actions and decision of people and technology. It will deliver this knowledge via a Digital Toolkit, which will have three elements:
i) Specification of sensor integration and data analytics for performance capture in Digital Manufacturing
ii) Quantitative analysis of the impact of four industrial Digital Manufacturing use cases
iii) Online interactive tool(s) to support manufacturing decision making for implementation of Digital Manufacturing Technologies
The DigiTOP project brings together a team with expertise in manufacturing, human factors, robotics and human computer interaction, to develop new methods to capture and predict the impact of Digital Manufacturing on future work. This project will work closely with a range of industry partners, including Jaguar Landrover, BAE Systems, Babcock International and the High Value Manufacturing Catapult to co-create industry-specified use cases to examine. The overall goal of DigiTOP is to produce a toolkit, derived from new fundamental engineering and science knowledge, that will enable industry to increase productivity, support Digital Manufacturing Technology adoption and de-risk the implementation of future Digital Manufacturing Technologies through the consideration of human requirements and capabilities.
It may not be enough to simply assume that workers will adopt new roles bestowed upon them; to ensure successful worker acceptance and operational performance of a new system it is important to incorporate user requirements into Digital Manufacturing Technologies design. In the past, Human Factors has shaped the tools used in manufacturing, to make people safe, to make work easy, and to make the workforce more efficient. New approaches to capture and predict the impact of the changes that these new types of technologies, such as robotics, rapidly evolvable workspaces, and data-driven systems are required. These approaches consist of embedded sensor technologies for capture of workplace performance, machine learning and data analytics to synthesise and analyse these data, and new methods of visualisation to support decisions made, potentially in real-time, as to how digital manufacturing workplaces should function.
The DigiTOP project will develop the new fundamental knowledge required to reliably and validly capture and predict the performance of a digital manufacturing workplace, integrating the actions and decision of people and technology. It will deliver this knowledge via a Digital Toolkit, which will have three elements:
i) Specification of sensor integration and data analytics for performance capture in Digital Manufacturing
ii) Quantitative analysis of the impact of four industrial Digital Manufacturing use cases
iii) Online interactive tool(s) to support manufacturing decision making for implementation of Digital Manufacturing Technologies
The DigiTOP project brings together a team with expertise in manufacturing, human factors, robotics and human computer interaction, to develop new methods to capture and predict the impact of Digital Manufacturing on future work. This project will work closely with a range of industry partners, including Jaguar Landrover, BAE Systems, Babcock International and the High Value Manufacturing Catapult to co-create industry-specified use cases to examine. The overall goal of DigiTOP is to produce a toolkit, derived from new fundamental engineering and science knowledge, that will enable industry to increase productivity, support Digital Manufacturing Technology adoption and de-risk the implementation of future Digital Manufacturing Technologies through the consideration of human requirements and capabilities.
Planned Impact
The key beneficiaries of this research will be a) the manufacturing industry, who will have deeper and more robust understanding of the potential benefits of implementation of Digital Manufacturing, and be able to anticipate the changes that this approach will bring to the workplaces and organisations that they deliver; b) companies developing technologies to support Digital Manufacturing, who will be able to tailor their products and guide their implementation in line with the empirically-derived guidance that DigiTOP will deliver; and c) the UK more widely, after the increase in productivity and ability to quantify the impact of digital manufacturing on the industrial workplace.
Our impact will be enabled through
1) creating a Digital Toolkit to catalyse the manufacturing industry as they move towards optimised and safe digital manufacturing technologies,
2) targeted and meaningful engagement with industry collaborators from across the product lifecycle, supply chain and sectors within manufacturing;
3) development of people and expertise, in industry and academia in a domain that is rapidly developing;
4) conducting quantitative analysis of economic and societal impact of our work; and
5) communicating our work to wide audiences, including government, policy makers and the public.
In addition to our project partners, we will utilise our extensive networks of industrial collaborators to ensure that the impact of DigiTOP is as wide-reaching as possible. In particular we will leverage the communities from within the EPSRC Funded Digital Manufacturing Network Plus 'Connected Everything' and the Robotics and Autonomous Systems network, to ensure that our work engages a range of academic and industrial colleagues. We will ensure that the activities from DigiTOP are communicated in an accessible manner and in particular will take advantage of expertise within the consortium of communicating work via the Computerphile YouTube channel.
Our project partners will become early adopters and advocates for our toolkit, the Digital Toolkit itself will be disseminated openly, and example datasets will be shared as open source.
Our impact will be enabled through
1) creating a Digital Toolkit to catalyse the manufacturing industry as they move towards optimised and safe digital manufacturing technologies,
2) targeted and meaningful engagement with industry collaborators from across the product lifecycle, supply chain and sectors within manufacturing;
3) development of people and expertise, in industry and academia in a domain that is rapidly developing;
4) conducting quantitative analysis of economic and societal impact of our work; and
5) communicating our work to wide audiences, including government, policy makers and the public.
In addition to our project partners, we will utilise our extensive networks of industrial collaborators to ensure that the impact of DigiTOP is as wide-reaching as possible. In particular we will leverage the communities from within the EPSRC Funded Digital Manufacturing Network Plus 'Connected Everything' and the Robotics and Autonomous Systems network, to ensure that our work engages a range of academic and industrial colleagues. We will ensure that the activities from DigiTOP are communicated in an accessible manner and in particular will take advantage of expertise within the consortium of communicating work via the Computerphile YouTube channel.
Our project partners will become early adopters and advocates for our toolkit, the Digital Toolkit itself will be disseminated openly, and example datasets will be shared as open source.
Publications
Agrawal S
(2021)
Physiological Data Measurement in Digital Manufacturing
Al-Yacoub A
(2020)
Effective Human-Robot Collaboration Through Wearable Sensors
Al-Yacoub A
(2021)
Improving human robot collaboration through force/torque based learning for object manipulation
in Robotics and Computer Integrated Manufacturing
Al-Yacoub A
(2021)
Data-Driven Modelling of Human-Human Co-Manipulation Using Force and Muscle Surface Electromyogram Activities
in Electronics
Al-Yacoub A
(2021)
Improving human robot collaboration through Force/Torque based learning for object manipulation
in Robotics and Computer-Integrated Manufacturing
Al-Yacoubb A
(2020)
Investigating the Impact of Human in-the-Loop Digital Twin in an Industrial Maintenance Context
in SSRN Electronic Journal
Alsuraykh N
(2019)
How Stress and Mental Workload are Connected
Argyle E
(2021)
Physiological indicators of task demand, fatigue, and cognition in future digital manufacturing environments
in International Journal of Human-Computer Studies
Ariansyah D
(2022)
A head mounted augmented reality design practice for maintenance assembly: Toward meeting perceptual and cognitive needs of AR users.
in Applied ergonomics
Ariansyah D
(2024)
Augmented reality training for improved learnability
in CIRP Journal of Manufacturing Science and Technology
Ariansyaha D
(2020)
Digital Twin Development: A Step by Step Guideline
in SSRN Electronic Journal
Ariansyaha D
(2020)
Towards a Digital Human Representation in an Industrial Digital Twin
in SSRN Electronic Journal
Buerkle A
(2021)
An Adaptive Human Sensor Framework for Human-Robot Collaboration
Buerkle A
(2021)
An adaptive human sensor framework for human-robot collaboration
in The International Journal of Advanced Manufacturing Technology
Buerkle A
(2023)
An Incremental Learning Approach to Detect Muscular Fatigue in Human- Robot Collaboration
in IEEE Transactions on Human-Machine Systems
D'Amico R
(2022)
Cognitive digital twin: An approach to improve the maintenance management
in CIRP Journal of Manufacturing Science and Technology
D'Amico R
(2022)
Detecting failure of a material handling system through a cognitive twin
in IFAC-PapersOnLine
D'Amico R
(2023)
Industrial Insights on Digital Twins in Manufacturing: Application Landscape, Current Practices, and Future Needs
in Big Data and Cognitive Computing
Del Amo I
(2022)
Hybrid recommendations and dynamic authoring for AR knowledge capture and re-use in diagnosis applications
in Knowledge-Based Systems
Eimontaite I
(2020)
Will Operators Work in Close Proximity to Industrial Robots? A Study of Acceptance Using Psychological and Physiological Responses
in SSRN Electronic Journal
Description | The DigiTOP project demonstrated the potential for sensing technologies to be used to monitor and understand human-technology collaborative work in a manufacturing setting. The project delivered a toolkit to raise the profile of considerations of data integration, ethics and design for manufacturing systems. |
Exploitation Route | The tools are freely available for use by others to explain digital manufacturing and show how different sensing technologies can be used to design human-technology collaborative work. |
Sectors | Aerospace Defence and Marine Digital/Communication/Information Technologies (including Software) Manufacturing including Industrial Biotechology |
URL | http://digitop.ac.uk |
Description | Learning from this award has fed into the knowhow of the PI and her work as Chief Scientific Adviser. This has been particularly valuable in informing work relating to monitoring of performance in transport settings, including vehicle control. |
First Year Of Impact | 2023 |
Sector | Transport |
Impact Types | Policy & public services |
Title | A head mounted augmented reality design practise for maintenance assembly |
Description | This is the experimental data used to examine the impact of different AR HMD modalities on task performance, system usability, and user safety |
Type Of Material | Database/Collection of data |
Year Produced | 2021 |
Provided To Others? | Yes |
URL | https://cord.cranfield.ac.uk/articles/dataset/A_head_mounted_augmented_reality_design_practise_for_m... |
Title | Augmented Reality Training for Improved Learnability: data |
Description | Survey used Relevant images from the demonstration |
Type Of Material | Database/Collection of data |
Year Produced | 2024 |
Provided To Others? | Yes |
URL | https://cord.cranfield.ac.uk/articles/dataset/Augmented_Reality_Training_for_Improved_Learnability_d... |
Title | Cognitive data imputation: case study in maintenance cost estimation |
Description | Cost estimation is critical for effective decision making in engineering projects. However, it is often hampered by a lack of sufficient data. For this, data imputation techniques can be used to estimate missing costs based on statistical estimates or analogies with historical data. However, these techniques are often limited because they do not consider the existing knowledge of experts. In this paper, a novel cognitive data imputation technique is proposed for cost estimation that uses explanatory interactive machine learning to integrate and improve human knowledge. Through a case study in maintenance cost estimation the effectiveness of the approach is demonstrated. |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | https://cord.cranfield.ac.uk/articles/dataset/Cognitive_data_imputation_case_study_in_maintenance_co... |
Title | Data: A Design Framework for Adaptive Digital Twins |
Description | This paper develops a new DT design framework that uses ontologies to enable co-evolution with the CES by capturing data in terms of variety, velocity, and volume across the asset life-cycle. The framework has been tested successfully on a helicopter gearbox demonstrator and a mobile robotic system across their life cycles, illustrating DT adaptiveness without the data architecture needing to be modified. The data presented in this portal is related to the data that was generated in the validation process. |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | https://cord.cranfield.ac.uk/articles/dataset/Data_A_Design_Framework_for_Adaptive_Digital_Twins/121... |
Title | Data: Fast Augmented Reality Authoring: Fast Creation of AR step-by-step Procedures for Maintenance Operations |
Description | Augmented Reality (AR) has shown great potential for improving human performance in Maintenance, Repair, and Overhaul (MRO) operations. Whilst most studies are currently being carried out at an academic level, the research is still in its infancy due to limitations in three main aspects: limited hardware capabilities, the robustness of object recognition, and content-related issues. This article focuses on the last point, by proposing a new geometry-based method for creating a step-by-step AR procedure for maintenance activities. The Fast Augmented Reality Authoring (FARA) method assumes that AR can recognise and track all the objects in a maintenance environment when CAD models are available, to knowledge transfer to a non-expert maintainer. The novelty here lies in the fact that FARA is a human-centric method for authoring animation-based procedures with minimal programming skills and the manual effort required. FARA has been demonstrated, as a software unit, in an AR system composed of commercially available solutions and tested with over 30 participants. The results show an average time saving of 34.7% (min 24.7%; max 55.3%) and an error reduction of 68.6% when compared to the utilisation of traditional hard-copy manuals. Comparisons are also drawn from performances of similar AR applications to illustrate the benefits of procedures created utilising FARA. |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | https://cord.cranfield.ac.uk/articles/dataset/Data_Fast_Augmented_Reality_Authoring_Fast_Creation_of... |
Title | Digital Twin Implementation Landscape_final_clean.csv |
Description | Results from an online survey, cleaned from sensitive information. |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
URL | https://cord.cranfield.ac.uk/articles/dataset/Digital_Twin_Implementation_Landscape_final_clean_csv/... |
Title | Human-Human co-manipulation data |
Description | The shared data were filtered and manually cleaned. There are four co-manipulation trials, each one contains the following columns: 'rosbagTimestamp': Timestamp '# of samples'Force/torque signal: 'Fx', 'Fy', 'Fz', 'Tx', 'Ty', 'Tz' Object position: 'x.v', 'y.v', 'z.v', 'Rx.v', 'Ry.v', 'Rz.v', 'w.v' Follower right arm/forearm EMG: 'emg_RFsgl', 'emg_RAsgl', Object displacement (Cartisaian): 'disp_x', 'disp_y', 'disp_z'Time difference between two consequent timestamps 'step_size' The data was collected as follows: Two humans were asked to co-manipulate a load of 10 Kg (together). One human was acting like a leader, and the other one was asked to follow. The only allowed communication between the follower and the leader is through the haptic clues (measured using 6-axis force/torque signal). The follower had EMG muscle activity sensors on the right hand (arm/forearm). Finally, the object position was tracked using an eight-camera VICON system. All sensory data were synchronised using ApproximateTime synchroniser; hence, they have the same timestamp. These datasets were collected through a GitHub tool available at: https://github.com/Intelligent-Automation-Centre/bluebox. The tool is explained in more detail in Al-Yacoub et al. (2020).Reference:Al-Yacoub, A, Buerkle, A, Flanagan, M, Ferreira, P, Hubbard, E, Lohse, N (2020) (Accepted for presentation) Effective Human-Robot Collaboration Through Wearable Sensors. In the 25th IEEE Conference on Emerging Technologies and Factory Automation, Vienna, Austria, 8-11 September. |
Type Of Material | Database/Collection of data |
Year Produced | 2020 |
Provided To Others? | Yes |
URL | https://repository.lboro.ac.uk/articles/dataset/Human-Human_co-manipulation_data/12942122 |
Title | Hybrid recommendations and dynamic authoring for AR knowledge capture and re-use in diagnosis applications |
Description | In Industry 4.0, integrated data management is an important challenge due to heterogeneity and the lack of structure of numerous existing data sources. A relevant research gap involves human knowledge integration, especially in maintenance operations. Augmented Reality (AR) can bridge this gap, but it requires improved augmented content to enable effective and efficient knowledge capture. This paper proposes dynamic authoring and hybrid recommender methods for accurate AR-based reporting. These methods aim to provide maintainers with augmented data input formats and recommended datasets for enhancing the efficiency and effectiveness of their reporting tasks. The proposed contributions have been validated through experiments and surveys in two failure diagnosis reporting scenarios. Experimental results indicated that the proposed reporting solution can reduce reporting errors by 50% and reporting time by 20% compared to alternative recommender and AR tools. Besides, survey results suggested that testers perceived the proposed reporting solution as more effective and satisfactory for reporting tasks than alternative tools. Thus, proving that the proposed methods can improve the effectiveness and efficiency of diagnosis reporting applications. Finally, this paper proposes future works towards a framework for automatic adaptive authoring in AR knowledge transfer and capture applications for human knowledge integration in the context of Industry 4.0. |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
URL | https://cord.cranfield.ac.uk/articles/dataset/Hybrid_recommendations_and_dynamic_authoring_for_AR_kn... |
Title | The Influence of Augmented Reality Interaction Design on Pilot's Perceived Workload and Situation Awareness |
Description | This work explored the potential for Augmented Reality (AR) rendering information superimposed over the flight deck to increase a pilot's situation awareness (SA). This emerging technology introduced novel human-computer interaction paradigms that would have impact on pilot's cognitive demands. |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
URL | https://cord.cranfield.ac.uk/articles/dataset/The_Influence_of_Augmented_Reality_Interaction_Design_... |
Description | A Design Framework for Adaptive Digital Twins |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Postgraduate students |
Results and Impact | Online webinar, presented by an academic from one of the DigiTOP Partner Universities. 30 mins for a presentation and 15 minutes for Q&A to the presenter. |
Year(s) Of Engagement Activity | 2021 |
URL | https://digitop.ac.uk/?page_id=4445 |
Description | Connected Everything 2020 Summer School |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Postgraduate students |
Results and Impact | We were delighted to partner with the Smart Products Beacon at University of Nottingham to deliver our 2020 Summer School. This 3 day event focused on how co-bots could support a post-Covid world. 25 postgraduates joined the online event with support from academics, researchers and industry. The final day saw the students present their co-bot solutions and a team was selected by a panel of judges as the most appropriate response to the brief. |
Year(s) Of Engagement Activity | 2020 |
URL | https://connectedeverything.ac.uk/summer-schools-and-workshops/ |
Description | Defence Sustainability Conference 2020 - 19th and 20th January 2021 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Industry/Business |
Results and Impact | In the first of its kind, the Defence Sustainability Conference will showcase the extensive work currently underway across the MOD, Industry and Academia to optimise operational advantage and support the target of Net Zero 50 (NZ50). It will highlight the need for clear policy developments and provide a platform for innovative ideas and projects being delivered in support of a more sustainable future. This will include presentations from some of the key stakeholders both within the MOD itself, across the supply chain and outside of Defence. The event is designed to spark opportunities for developing best practice and collaboration, as well as creating a pan-sector network of stakeholders to help raise awareness of opportunities to challenge the current ways of working. The keynote speeches for the day will be provided by Lieutenant General Richard Nugee CB CVO CBE Climate Change and Sustainability Strategy Lead and Lieutenant General Richard Wardlaw OBE, Chief Defence Logistics and Support, who will provide his vision on a Sustainable Defence operating model. The conference is being run as a collaboration between Team Defence Information, UK Defence Solution Centre (UK DSC) and the Ministry of Defence, with support from ADS Group and techUK, and will feature a full day of live speakers, virtual trade stands and areas to network and share ideas. John Erkoyuncu delivered the session - Design of digital twins in defence 3-4pm with Babcock |
Year(s) Of Engagement Activity | 2021 |
URL | https://secure.teamdefence.info/community.php?community=1000135 |
Description | Digitop seminar on Human Factors in Industry 4.0 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Industry/Business |
Results and Impact | Public seminar on Human Factors in Industry 4.0. Raised awareness of the pros and cons of virtual reality in industrial applications. |
Year(s) Of Engagement Activity | 2020 |
Description | EHF2020 'The digital footprint at work - human factors challenges and opportunities' by Prof. Sarah Sharples (keynote) |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | The digital footprint at work - human factors challenges and opportunities Professor Sarah Sharples, University of Nottingham, UK As personal and workplace technologies develop, as HFE professionals we now have a vast range of tools available to aid our understanding and analysis of workplace experience and behaviour. However, this data presents challenges, in terms of how it is interpreted, how it is used to inform decisions, how we manage personal privacy and how we balance quantitative and qualitative data sources. This short presentation will provide a short overview of the types of technologies currently being explored, and begin to consider two alternative futures - a utopian one, and a dystopian one. Participants will then be encouraged to raise and discuss questions about the future of 'technology-led' HFE and how we can use these technologies responsibly to influence safer and more effective workplaces, lives and societies. |
Year(s) Of Engagement Activity | 2020 |
URL | https://conference.ergonomics.org.uk/cth_speaker/sarah-sharples/ |
Description | Facial Thermography |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Postgraduate students |
Results and Impact | Online webinar, presented by an academic from one of the DigiTOP Partner Universities. 30 mins for a presentation and 15 minutes for Q&A to the presenter. |
Year(s) Of Engagement Activity | 2021 |
URL | https://digitop.ac.uk/?page_id=4445 |
Description | Lecture on Human Factors in Industry 4.0 on Digital Manufacturing |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Undergraduate students |
Results and Impact | Lecture to students on the Digital Manufacturing Level 4 module at the University of Nottingham, December 2022. Made specific reference to the Digitop toolkit, and to the Sensory Illusions research. Students demonstrated greater engagement with the subject area, as evidenced by their coursework submissions. |
Year(s) Of Engagement Activity | 2022 |
Description | Lecture on Human Factors in Industry 4.0 on the Digital Manufacturing |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Undergraduate students |
Results and Impact | Lecture to students on the Digital Manufacturing Level 4 module at the University of Nottingham, December 2021. Made specific reference to the Digitop toolkit, and to the Sensory Illusions research. Students demonstrated greater engagement with the subject area, as evidenced by their coursework submissions. |
Year(s) Of Engagement Activity | 2021 |
Description | Lecture on Human Factors in Industry 4.0 on the Digital Manufacturing |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Undergraduate students |
Results and Impact | Introduced Human Factors challenges in Digital Manufacturing to students on a Manufacturing UG MEng course, and made specific reference to the Digitop Toolkit & the Sensory Illusions research. Students demonstrated an increased understanding of human factors challenges in digital manufacturing as evidenced by their coursework submissions. |
Year(s) Of Engagement Activity | 2020 |
Description | Manufacturing 2075 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Keynote talk at Manufacturing 2075 event at Cranfield University |
Year(s) Of Engagement Activity | 2018 |
Description | New technologies and measurement of biomechanical risk factors: Keeping employees' digital footprints under control |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Presentation exploring potential for physiological and subjective measures for capturing real time industrial performance and experience |
Year(s) Of Engagement Activity | 2019 |
Description | Queen's Lecture, Technical University of Berlin |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Policymakers/politicians |
Results and Impact | This was a public lecture hosted by the British Ambassador to Germany and the Technical University of Berlin. During the lecture I highlighted some of the work on physiological monitoring that was completed as part of DigiTOP. |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.tu.berlin/en/about/queens-lecture-2022 |
Description | Structured authoring of AR based Communication |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Postgraduate students |
Results and Impact | Online webinar, presented by an academic from one of the DigiTOP Partner Universities. 30 mins for a presentation and 15 minutes for Q&A to the presenter. |
Year(s) Of Engagement Activity | 2020 |
URL | https://digitop.ac.uk/?page_id=4445 |
Description | Wearable Sensors in a Human-Robot Collaboration Context |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Postgraduate students |
Results and Impact | Online webinar, presented by an academic from one of the DigiTOP Partner Universities. 30 mins for a presentation and 15 minutes for Q&A to the presenter. |
Year(s) Of Engagement Activity | 2020 |
URL | https://digitop.ac.uk/?page_id=4445 |
Description | YouTube video "What is Digital Manufacturing?" |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | Members of the DigiTOP project team created an animated video aimed at school children which explains what is digital manufacturing. As of February 2023, the video is showing over 4,000 views. |
Year(s) Of Engagement Activity | 2021,2022,2023 |
URL | https://www.youtube.com/watch?v=ptDJw98Ds9M&t=68s |