68,717 research outputs found
Incremental Consistency Checking in Delta-oriented UML-Models for Automation Systems
Automation systems exist in many variants and may evolve over time in order
to deal with different environment contexts or to fulfill changing customer
requirements. This induces an increased complexity during design-time as well
as tedious maintenance efforts. We already proposed a multi-perspective
modeling approach to improve the development of such systems. It operates on
different levels of abstraction by using well-known UML-models with activity,
composite structure and state chart models. Each perspective was enriched with
delta modeling to manage variability and evolution. As an extension, we now
focus on the development of an efficient consistency checking method at several
levels to ensure valid variants of the automation system. Consistency checking
must be provided for each perspective in isolation, in-between the perspectives
as well as after the application of a delta.Comment: In Proceedings FMSPLE 2016, arXiv:1603.0857
Realising the open virtual commissioning of modular automation systems
To address the challenges in the automotive industry posed by the need to rapidly manufacture more
product variants, and the resultant need for more adaptable production systems, radical changes are
now required in the way in which such systems are developed and implemented. In this context, two
enabling approaches for achieving more agile manufacturing, namely modular automation systems
and virtual commissioning, are briefly reviewed in this contribution. Ongoing research conducted at
Loughborough University which aims to provide a modular approach to automation systems design
coupled with a virtual engineering toolset for the (re)configuration of such manufacturing
automation systems is reported. The problems faced in the virtual commissioning of modular
automation systems are outlined. AutomationML - an emerging neutral data format which has
potential to address integration problems is discussed. The paper proposes and illustrates a
collaborative framework in which AutomationML is adopted for the data exchange and data
representation of related models to enable efficient open virtual prototype construction and virtual
commissioning of modular automation systems. A case study is provided to show how to create the
data model based on AutomationML for describing a modular automation system
Evaluation of Cognitive Architectures for Cyber-Physical Production Systems
Cyber-physical production systems (CPPS) integrate physical and computational
resources due to increasingly available sensors and processing power. This
enables the usage of data, to create additional benefit, such as condition
monitoring or optimization. These capabilities can lead to cognition, such that
the system is able to adapt independently to changing circumstances by learning
from additional sensors information. Developing a reference architecture for
the design of CPPS and standardization of machines and software interfaces is
crucial to enable compatibility of data usage between different machine models
and vendors. This paper analysis existing reference architecture regarding
their cognitive abilities, based on requirements that are derived from three
different use cases. The results from the evaluation of the reference
architectures, which include two instances that stem from the field of
cognitive science, reveal a gap in the applicability of the architectures
regarding the generalizability and the level of abstraction. While reference
architectures from the field of automation are suitable to address use case
specific requirements, and do not address the general requirements, especially
w.r.t. adaptability, the examples from the field of cognitive science are well
usable to reach a high level of adaption and cognition. It is desirable to
merge advantages of both classes of architectures to address challenges in the
field of CPPS in Industrie 4.0
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