Skill-based Propagation of "Plug&Produce"-Devices in Reconfigurable Production Systems by AML

Acronym:

SkillPro

Funding:

EC - FP7

Duration:

10/2012 – 09/2015

Abstract

It is increasingly evident that the era of mass production is being replaced by the era of market niches. The key to creating products that can meet the demands of a diversified customer base is a short development cycle yielding low cost, high quality goods in sufficient quantity to meet demand. This makes flexibility an increasingly important attribute to manufacturing.

In response the reconfigurable manufacturing system (RMS) concept has emerged in the last few years as an attempt to achieve the required manufacturing flexibility. A RMS proposes a manufacturing system where machine components, machines, cells, or material handling units can be added, removed, modified, or interchanged as needed to respond quickly to changing requirements. Compared to flexible manufacturing systems (FMS), this approach is believed to have the potential to offer a cheaper solution, in the long run as it can increase the life and utility of a manufacturing system.

A lot of research has been conducted towards establishing such a production system, the most prominent of those approaches being the "Plug&Produce" paradigm - in analogy to the "Plug&Play" paradigm of the Computer Industry. 'Plug-and-Produce' allows the automatic configuration and seamless integration of heterogeneous devices into a system.

However, while the "Plug&Produce" paradigm has contributed greatly towards reducing commissioning effort and ramp-up time for new components, it has been too focussed on solving interoperability issues by means of standardized interfaces and service or agent-oriented ICT-architectures.

Enhancing these approaches by taking into account the knowledge about the capabilities/skills of the new devices and their impact on the overall capabilities/skills of the production system in terms of operational procedures and economical KPI would increase the versatility and efficiency of production sites.

Further, AutoPnP will explore the utilization of service-oriented architecture (SOA) and semantic descriptions of services, components and interfaces in automation technology as to enable the autonomous interaction and collaboration of several service robots, e.g. in a production line.

Key Words:

model-based control, Asset Management, Manufacturing Execution System, learning of sequences, control loops, prediction models, meta cognitive loop, autonomous planning and control, active logic, Bayesian net, self-awareness, self-reflection, AutomationML, information fusion

Homepage:

Coordinator:

Prof. Björn Hein
Karlsruher Institut für Technologie
 send mail to bjoern.hein@kit.edu bjoern.hein@kit.edu

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