Measures of reconfigurability and its key characteristics in intelligent manufacturing systems

Abstract

In recent years, the fields of reconfigurable manufacturing systems, holonic manufacturing systems, and multi-agent systems have made technological advances to support the ready reconfiguration of automated manufacturing systems. While these technological advances have demonstrated robust operation and been qualitatively successful in achieving reconfigurability, limited effort has been devoted to the measurement of reconfigurability in the resultant systems. Hence, it is not clear (1) to which degree these designs have achieved their intended level of reconfigurability, (2) which systems are indeed quantitatively more reconfigurable and (3) how these designs may overcome their design limitations to achieve greater reconfigurability in subsequent design iterations. Recently, a reconfigurability measurement process based upon axiomatic design knowledge base and the design structure matrix has been developed. Together, they provide quantitative measures of reconfiguration potential and ease. This paper now builds upon these works to provide a set of composite reconfigurability measures. Among these are measures for the key characteristics of reconfigurability: integrability, convertibility, and customization, which have driven the qualitative and intuitive design of these technological advances. These measures are then demonstrated on an illustrative example followed by a discussion of how they adhere to requirements for reconfigurability measurement in automated and intelligent manufacturing systems.

Dynamic Service Reconfiguration with Multi-agent Systems

“…Given by the maximum number of services to process a unit of time where γ stands for the complete request and t for the unit time requirements do not support the dimension of the reconfiguration effort index. In the literature, the structure matrix analysis investigates the capability to reconfigure [14]; however the process relies on centralized decisions. This indicates a lack of research in measuring the reconfigurability effort and its impact on the decentralized way.…”

Multi-Agent System Design Principles for Resilient Coordination & Control of Future Power Systems

“…While the full description of resilience measures is not feasible here, the underlying Axiomatic Design model [27][28][29][30] for LFES is included in order to provide a common foundation upon which the remainder of the discussion is based. The interested reader is referred to previous works [21,[23][24][25][27][28][29][30]63,91] for further discussion and illustrative examples of how this Axiomatic Design model has been applied to resilience and reconfigurability measurement. The second half of this section serves to ground the Axiomatic Design model, as a concise description of system structure, to traditional power systems models as descriptions of power system behavior.…”

“…As has been discussed extensively in the literature, life cycle properties such as reconfigurability and resilience depend primarily on a complete description of system structure rather than system behavior [25,[27][28][29]. Therefore, the discussion presented in the previous subsection is sufficient to address the cyber-layer and distill the MAS design principles for resilience in "MAS Design Principles for Resilience in Power Systems" section.…”

“…Principle 8 ensures that when a reconfiguration process occurs (i.e., addition, modification or removal of a structural degree of freedom), it does so simultaneously on the physical resource as well as on the corresponding agent. Previous reconfigurability measurement work has shown that in many cases misaligned informatic entities such as centralized controllers lead to greater coupling of structural degrees of freedom [22,25]; thus hindering ease of reconfiguration. Recent work in power system state estimation has recognized the challenge of gathering geographically dispersed measurements from a variable power grid topology; thus motivating recent developments in distributed state estimation [38].…”

Transitioning From Standard Automation Solutions to Cyber-Physical Production Systems: An Assessment of Critical Conceptual and Technical Challenges

“…Most of these contributions have, however, never made it out of the academia or were applied with important deviations in real operation. Among the main adoption barriers one may mention: the low maturity of the related technologies [15] (particularly considering the cyber-physical interface [16]); limitations with the verification and validation procedures [17] and the lack of integrated architectures that could inform the development, from conceptual stages down to the hardware-on-the-loop implementation level, in a quantitative way [18], [19]. Collectively, these contributions embed the main principles of what would/could be a Cyber-Physical Production System (CPPS) from architectural and technical perspectives but they have never been considered or established in the much required aggregated format.…”