General Objectives:
In this project, techniques and technologies will be investigated and developed, based on the theoretical and practical foundations that reside at the intersection of Systems Engineering and Automation, Computer Science, and Artificial Intelligence, to advance the state of science and technology in:
- Monitoring systems for device status through digital twins, supported by computational intelligence and data integration.
- Decision-making through highly interpretable systems by humans with a high degree of precision of the trained models, based on evolving fuzzy systems in multilayer architectures.
- Self-reconfiguration strategies based on optimization, through multi-objective optimization heuristic methods for the optimal re-parameterization of devices and production reprogramming.
The general scientific-technical objective of the project is the design and development of two self-reconfiguration methods based on digital twins and transparent and explainable decision-making, and their validation in an Industry 4.0 pilot line: one focused on the automatic reprogramming of production and the other aimed at offering intelligent reconfiguration recommendations with an operator and technologist-centered approach.
Specific Objectives:
To achieve the general objective of the project, the following specific objectives are defined:
O1. Design and develop digital twins at the local level (nodes/devices) and global level (production line).
O2. Design and develop an optimization-based reconfiguration method using digital twins, with transparent and explainable decision-making, focused on automatic production reprogramming.
O3. Design and develop a method based on digital twins with transparent and explainable decision-making.
O4. Implement and evaluate the proposed methods in O1, O2, and O3.
O5. Integrate and evaluate the developed methods in an Industry 4.0 pilot line.
These objectives are achievable within 3 years, considering that the working team is expert in the subject, some previous results are available, and the number of researchers is aligned with the number of proposed objectives.
It is important to highlight the research group’s experience in the project’s topics. The GAMHE group has a long history in the field of intelligent monitoring, control, and supervision systems applied to manufacturing processes, which is demonstrated in two transitions in the evolution of research activity. The first transition ranges from research focused on physical systems (e.g., DPI DPI2008-01978 COGNETCON), through network-connected physical systems (e.g., DPI DPI2011-24447 CONMICRO), to cyber-physical systems and IoT (e.g., DPI2017-86915-C3-1-R COGDRIVE). The second transition, from the perspective of the developed methods and techniques, spans from the research and development of methods based on Fuzzy Logic and Artificial Neural Networks for modeling, control, and supervision to artificial cognitive control, with applications in manufacturing and contributions related to the objectives/scope of this project.
Duration of the project: 2021-2025.
This work was funded by Ministerio de Ciencia, Innovacion y Universidades (MICIU) of Spain trough the project «Self-reconfiguration for Industrial Cyber-Physical Systems based on digital twins and Artificial Intelligence. Methods and application in Industry 4.0 pilot line (SELFRECO)», grant ID PID2021–127763OB-100”.
