Measures of reconfigurability and its key characteristics in intelligent manufacturing systems
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- 作者：Amro M. Farid
- 关键词：Reconfigurability ; Axiomatic design for large flexible systems ; Design structure matrix ; Reconfigurable manufacturing systems ; Multi ; agent systems ; Holonic manufacturing systems
- 刊名：Journal of Intelligent Manufacturing
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：28
- 期：2
- 页码：353-369
- 全文大小：
- 刊物类别：Business and Economics
- 刊物主题：Production; Manufacturing, Machines, Tools; Control, Robotics, Mechatronics;
- 出版者：Springer US
- ISSN：1572-8145
- 卷排序：28
文摘
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.