DIGICOGS:DIGital Twins for Industrial COGnitive Systems through Industry 4.0 and Artificial Intelligence
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Today, in Industry 4.0, Big data analytics – which is used to sort through massive amounts of data and identifies important patterns – have become useful in the advancement of industrial cognitive systems in process industries & are a major theme in current industrial technology development. The challenge is to achieve the advantage of using a data-driven cognitive system by integrating the heterogeneous data from multiple sources that can easily be used in a machine learning model and adjust the algorithms. The objective of DIGICOGS is to provide a digital twin that combines sensor information, AI and machine learning and big data analytics that underpin the new wave of the cognitive system. In DIGICOGS, cutting-edge solutions will be achieved through data-driven analytics, real-time monitoring and intelligent adaptive prediction based on combination of information i.e, sensor data, domain and context. DIGICOGS comprises MDH (a research group), Seco Tools (supplier to process industry and to GKN) & GKN (manufacturing industry). The project tasks will be performed as several work packages (WPs) including different methodologies, such as determining the state of the art, studying the data, defining the use-cases, developing the tools and evaluating the results. The predictive analytic tools developed in the project will assist operational operators, engineers and maintenance staff in fast & efficient process monitoring, predictive maintenance, improving productivity and energy efficiency. Thus, it is believed that the DIGICOGS technologies will strengthen Swedish industrial competence and competitiveness. The results from the project can be re-used and repeated with other Seco Tools customers and companies in process industry.
MDH utvecklar digital tvilling för att stärka svensk industri
MDH develops digital twin to strengthen Swedish industry
| First Name | Last Name | Title |
|---|---|---|
| Mobyen Uddin | Ahmed | Professor |
| Shahina | Begum | Professor |
| Shaibal | Barua | Senior Lecturer |
| Sharmin Sultana | Sheuly | Doctoral student |
Chip Analysis for Tool Wear Monitoring in Machining: A Deep Learning Approach (Aug 2024) Atiq Ur Rehman, Tahira Salwa Rabbi Nishat, Mobyen Uddin Ahmed, Shahina Begum, Abhishek Ranjan Journal of IEEE Access (Access'18)
Combining Ontology and Large Language Models to Identify Recurring Machine Failures in Free-Text Fields (Apr 2024) Marcus Bengtsson, Mobyen Uddin Ahmed, Ricky Stanley D Cruze , Peter Funk, Tomohiko Sakao , Rickard Sohlberg Dr H.C. THE 11th SWEDISH PRODUCTION SYMPOSIUM (SPS24)
Heuristic Approach for Cognitive Digital Twin Technology – A Technical Report (Nov 2023) Atiq Ur Rehman, Mobyen Uddin Ahmed, Shahina Begum
A Case Study on Ontology Development for AI Based Decision Systems in Industry (Jul 2023) Ricky Stanley D Cruze , Mobyen Uddin Ahmed, Marcus Bengtsson, Peter Funk, Rickard Sohlberg Dr H.C., Atiq Ur Rehman 7th International Congress and Workshop on Industrial AI and eMaintenance (IAI)
Artificial Intelligence in Predictive Maintenance: A Systematic Literature Review on Review Papers (Jul 2023) Md Rakibul Islam , Shahina Begum, Mobyen Uddin Ahmed 7th International Congress and Workshop on Industrial AI and eMaintenance (IAI)
Cognitive Digital Twin in Manufacturing: A Heuristic Approach (Jun 2023) Atiq Ur Rehman, Mobyen Uddin Ahmed, Shahina Begum 19th International Conference on Artificial Intelligence Applications and Innovations (AIAI2023)
| Partner | Type |
|---|---|
| GKN Driveline | Industrial |
| Seco Tools | Industrial |
