Journal Paper Accepted at the Journal of Intelligent Manufacturing: Measures of reconfigurability and its key characteristics in intelligent manufacturing systems
The LIINES is pleased to announce that the Journal of Intelligent Manufacturing has accepted our paper entitled: “Measures of reconfigurability and its key characteristics in intelligent manufacturing systems”. The paper is authored by Amro M. Farid and was published in October 2014.
Many manufacturing challenges arise with the global trend of increased competition in the marketplace. Production processes must deal with shorter product lifecycles and mass-customization. Consequently, production systems need to be quickly and incrementally adjusted to meet the ever-changing products. Reconfigurable manufacturing systems have been proposed as a solution that facilitates changing production processes for highly automated production facilities.
Much research has been done in the field of reconfigurable manufacturing systems. Topics include: modular machine tools and material handlers, distributed automation, artificially intelligent paradigms, and holonic manufacturing systems. While these technological advances have demonstrated robust operation and been qualitatively successful in achieving reconfigurability, there has been comparatively little attention devoted to quantitative design methodologies of these reconfigurable manufacturing systems and their ultimate industrial adoption remains limited.
Measuring reconfigurability of manufacturing systems quantitatively has been a major challenge in the past, since a quantitative reconfigurability measurement process was non-existent. Earlier work developed a measurement method that extracts measurables from the production shop floor. When this was established, basic measures of reconfiguration potential and reconfiguration ease were developed, based on axiomatic design for large flexible engineering systems and the design structure matrix respectively.
Reconfiguration of a production process can be split up in four steps: Decide which configuration, Decouple, Reorganize, and Recouple. The larger the number of elements in the system, the more configurations are made possible. This is measured using the reconfiguration potential measure, based on axiomatic design for large flexible engineering systems.
Production processes contain multiple interfaces within themselves. Multiple layers of control can be distinguished, that have to work together to coordinate the physical components. These interfaces are the main determinants for the reconfiguration ease measure.
This paper combines these techniques to define a quantitative measure for reconfigurability and its key characteristics of integrability, convertibility and customization. The intention behind this research contribution is that it may be integrated in the future into quantitative design methodologies for reconfigurable manufacturing systems, which may be easily adopted by industrial automation and production companies.
About the author: Wester Schoonenberg completed his B.Sc. in Systems Engineering and Policy Analysis Management at Delft University of Technology in 2014. After his bachelors’ degree, Wester started his M.Sc. at Masdar Institute of Science & Technology. Currently, Wester is working on the integrated operation of electrical grids and production systems with a special interest in the demand side management of industrial facilities.
LIINES Website: http://amfarid.scripts.mit.edu
Duke Energy on Analytics and the Internet of Things
It’s been a long time since 2003 when the concept of the Internet of Things was first proposed by U. of Cambridge Auto-ID Laboratory. At the time, Dr. Amro M. Farid, now head of the Laboratory for Intelligent Integrated Networks of Engineering Systems, was a doctoral student investigating how RFID technology enabled intelligent products within reconfigurable manufacturing systems. The Internet of Things was being applied primarily in the manufacturing and supply chain domain.
Since then, the Internet of Things concept has taken hold not just in manufacturing systems and supply chains but nearly every industrial system domain including energy. Every “thing” or “device” has the potential to be connected via an intelligent sensor so as to make decisions — be they centralized within an operations control center — or distributed amongst artificially intelligent multi-agent systems. The Internet of Things concept has the potential to fundamentally transform industrial systems.
Have a look at Duke Energy’s take on the Internet of Things:
The LIINES is proud to have been working in this area since its inception and continue to do so. More information on our research can be found on the LIINES website.
LIINES Website: http://amfarid.scripts.mit.edu
Journal Paper Accepted at IEEE Transactions on Industrial Electronics: An Enterprise Control Assessment Method for Variable Energy Resource Induced Power System Imbalances. Part 1: Methodology
We are happy to announce that our recent paper entitled: “An Enterprise Control Assessment Method for Variable Energy Resource Induced Power System Imbalances. Part 1: Methodology”, has been accepted to IEEE Transaction on Industrial Electronics. The paper is authored by Aramazd Muzhikyan, Prof. Amro M. Farid and Prof. Youcef Kamal-Toumi.
In recent years, the impact of variable energy resource (VER) integration on power system operations has been studied extensively. While most of the studies agree that VER integration creates a need for additional resources to maintain reliable power system operations, they often fail to give exact assessments due to their methodological limitations. First, a majority of these studies are performed for specific cases and the results obtained cannot be generalized. Moreover, most of these studies are focused on a single control function of power system operations which restricts the scope of the results to that time scale and neglects the coupling between different time scales. Furthermore, most of the results are obtained by statistical calculations, but not validated by numerical simulations. Finally, many of the calculations rely on the experience of system operators which may not necessarily remain valid as the power system continues to evolve.
This newly published paper proposes an enterprise control assessment method for VER integrated power systems. The power system operations are modeled as a three-layer hierarchy. The model integrates resource scheduling, a balancing layer and a regulation layers, which capture most of the balancing operation functionality found in traditional power systems. Such integration allows the study of the coupling between different timescales of power system operations which would be neglected otherwise.
Another important aspect of this methodology is that integration of power system operation layers also guarantees that the obtained results can be generalized for different cases. To achieve this, some modifications of the traditional power system control actions are performed. The validation of the methodology demonstrates that in the absence of these modifications the simulations lead to unreasonable results for some scenarios.