Software Defined Manufacturing Extends Cloud-Based Control

Author(s):  
Armin Lechler ◽  
Alexander Verl

Nowadays, the key goal in manufacturing is being very efficient within changing markets and under turbulent conditions. Therefore, production plants with their machines logistics and all the other involved components have to be adaptable to changing conditions. For this reason, reconfigurable manufacturing systems are needed, which allow a fast adaption to new requirements of the product to be manufactured. Today, reconfiguration in manufacturing is mostly limited due to missing reconfigurability of the control software in combination with the underlying hardware. The coupling is that strong that in manufacturing control software is always bound to special hardware. Until now, flexibility is only possible by changing application or part programs that are interpreted by a fixed control kernel. The adaption of any core functionality is impossible, and any other changes require high manual effort for redesigning software systems and parametrizing their functionalities. For better adaptability in manufacturing this coupling has to be dissolved. Other disciplines and industries have similar requirements like the information and communication technology (ICT). In the area of ICT, there are more and more concepts of Software Defined Anything (SDX) like Software Defined Networking (SDN) or Software Defined Radio (SDR). Flexible, adaptive and really reconfigurable manufacturing should be improved by a new concept of Software Defined Manufacturing (SDM). SDM allows freely defined functionalities within the physical limitations of the mechanical and electrical components of a machine. But current manufacturing equipment with its control architecture does not offer the technical basis for such a concept. Existing concepts of cloud-based control architectures show indeed a virtualization of the control algorithms. Due to the fact that the software is running remotely, the software is decoupled from its hardware. However, the local control algorithms with hard real-time requirements still have a very strong coupling with the hardware. The local control software could not be defined freely according to the requirements of the product to be manufactured. In this paper, a new control architecture for manufacturing that combines cloud-based control as a service (CaaS) and Software Defined Manufacturing is presented. As a result, an architecture of an operating system for manufacturing equipment is shown, which is freely programmable. This paper deals with Software Defined Manufacturing for local control software, communication and cloud-based control systems. SDM allows defining the behavior of the entire manufacturing process based on design description of a product to be manufactured. In addition, methods are described, which allow the automatic configuration and optimization of such an architecture by using simulation technics and collected process data.

2016 ◽  
Vol 49 ◽  
pp. 87-100 ◽  
Author(s):  
Robson Marinho da Silva ◽  
Fabrício Junqueira ◽  
Diolino J. Santos Filho ◽  
Paulo E. Miyagi

2000 ◽  
Author(s):  
Ming-Chyuan Lu ◽  
Elijah Kannatey-Asibu

Abstract Ramp-up is a major step in the implementation of manufacturing systems, and is even more critical in reconfigurable manufacturing systems. For a successful reduction in ramp-up time, it is essential to analyze and monitor both the overall manufacturing system and the individual machine tools/processes that comprise the system. Towards this end, we have addressed the issue of monitoring tool wear using audible sound to enable faulty conditions associated with wear to be identified during the process before the part quality gets out of specification. Audible sound generated from the cutting process is analyzed as a source for monitoring tool wear during turning, assuming adhesive wear as the predominant wear mechanism. The analysis incorporates the dynamics of the cutting process. In modeling the interaction on the flank surface, the asperities on the surfaces are represented as a trapezoidal series function with normal distribution. The effect of changing asperity height, size, spacing, and the stiffness of the asperity interaction is investigated and compared with experimental data.


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