8 research outputs found
Toward a Generic Mapping Language for Transformations between RDF and Data Interchange Formats
While there exist approaches to integrate heterogeneous data using semantic
models, such semantic models can typically not be used by existing software
tools. Many software tools - especially in engineering - only have options to
import and export data in more established data interchange formats such as XML
or JSON. Thus, if an information which is included in a semantic model needs to
be used in a such a software tool, automatic approaches for mapping semantic
information into an interchange format are needed. We aim to develop a generic
mapping approach that allows users to create transformations of semantic
information into a data interchange format with an arbitrary structure which
can be defined by a user. This mapping approach is currently being elaborated.
In this contribution, we report our initial steps targeted to transformations
from RDF into XML. At first, a mapping language is introduced which allows to
define automated mappings from ontologies to XML. Furthermore, a mapping
algorithm capable of executing mappings defined in this language is presented.
An evaluation is done with a use case in which engineering information needs to
be used in a 3D modeling tool
A Mapping Approach to Convert MTPs into a Capability and Skill Ontology
Being able to quickly integrate new equipment and functions into an existing
plant is a major goal for both discrete and process manufacturing. But
currently, these two industry domains use different approaches to achieve this
goal. While the Module Type Package (MTP) is getting more and more adapted in
practical applications of process manufacturing, so-called skill-based
manufacturing approaches are favored in the context of discrete manufacturing.
The two approaches are incompatible because their models feature different
contents and they use different technologies. This contribution provides a
comparison of the MTP with a skill-based approach as well as an automated
mapping that can be used to transfer the contents of an MTP into a skill
ontology. Through this mapping, an MTP can be semantically lifted in order to
apply functions like querying or reasoning. Furthermore, machines that were
previously described using two incompatible models can now be used in one
production process
Modeling and Executing Production Processes with Capabilities and Skills using Ontologies and BPMN
Current challenges of the manufacturing industry require modular and
changeable manufacturing systems that can be adapted to variable conditions
with little effort. At the same time, production recipes typically represent
important company know-how that should not be directly tied to changing plant
configurations. Thus, there is a need to model general production recipes
independent of specific plant layouts. For execution of such a recipe however,
a binding to then available production resources needs to be made. In this
contribution, select a suitable modeling language to model and execute such
recipes. Furthermore, we present an approach to solve the issue of recipe
modeling and execution in modular plants using semantically modeled
capabilities and skills as well as BPMN. We make use of BPMN to model
\emph{capability processes}, i.e. production processes referencing abstract
descriptions of resource functions. These capability processes are not bound to
a certain plant layout, as there can be multiple resources fulfilling the same
capability. For execution, every capability in a capability process is replaced
by a skill realizing it, effectively creating a \emph{skill process} consisting
of various skill invocations. The presented solution is capable of
orchestrating and executing complex processes that integrate production steps
with typical IT functionalities such as error handling, user interactions and
notifications. Benefits of the approach are demonstrated using a flexible
manufacturing system.Comment: To be submitted to ETFA 202
A Capability and Skill Model for Heterogeneous Autonomous Robots
Teams of heterogeneous autonomous robots become increasingly important due to
their facilitation of various complex tasks. For such heterogeneous robots,
there is currently no consistent way of describing the functions that each
robot provides. In the field of manufacturing, capability modeling is
considered a promising approach to semantically model functions provided by
different machines. This contribution investigates how to apply and extend
capability models from manufacturing to the field of autonomous robots and
presents an approach for such a capability model
A Reference Model for Common Understanding of Capabilities and Skills in Manufacturing
In manufacturing, many use cases of Industry 4.0 require vendor-neutral and
machine-readable information models to describe, implement and execute resource
functions. Such models have been researched under the terms capabilities and
skills. Standardization of such models is required, but currently not
available. This paper presents a reference model developed jointly by members
of various organizations in a working group of the Plattform Industrie 4.0.
This model covers definitions of most important aspects of capabilities and
skills. It can be seen as a basis for further standardization efforts
Model-Based Engineering of CPPS Functions and Code Generation for Skills
Today's production systems are complex networks of cyber-physical systems
which combine mechanical and electronic parts with software and networking
capabilities. To the inherent complexity of such systems additional complexity
arises from the context in which these systems operate. Manufacturing companies
need to be able to adapt their production to ever changing customer demands as
well as decreasing lot sizes. Engineering such systems, which need to be
combined and reconfigured into different networks under changing conditions,
requires engineering methods to carefully design them for possible future uses.
Such engineering methods need to preserve the flexibility of functions into
runtime, so that reconfiguring machines can be done with as little effort as
possible. In this paper we present a model-based approach that is focused on
machine functions and allows to methodically develop system functionalities for
changing system networks. These functions are implemented as so-called skills
using automated code-generation
Automaton-on-Tag: An Approach for an RFID-Driven Production Control with Mealy Machines Stored on an RFID Tag
Part 3: Cyber-Physical (IIoT) Technology Deployments in Smart Manufacturing SystemsInternational audienceIn this paper, we present an approach to how to store production plans directly on an RFID tag in the form of an automaton. Based on a modular manufacturing system, this enables manufacturing systems to become more flexible and changeable and, in addition, reduces the engineering effort for adaptation in an existing plant. The connection between different production modules is implemented via carriers and a Mealy machine that is stored on an RFID tag. This machine’s states represent the production steps of the product on the carrier
A Research Agenda for AI Planning in the Field of Flexible Production Systems
Manufacturing companies face challenges when it comes to quickly adapting
their production control to fluctuating demands or changing requirements.
Control approaches that encapsulate production functions as services have shown
to be promising in order to increase the flexibility of Cyber-Physical
Production Systems. But an existing challenge of such approaches is finding a
production plan based on provided functionalities for a demanded product,
especially when there is no direct (i.e., syntactic) match between demanded and
provided functions. While there is a variety of approaches to production
planning, flexible production poses specific requirements that are not covered
by existing research. In this contribution, we first capture these requirements
for flexible production environments. Afterwards, an overview of current
Artificial Intelligence approaches that can be utilized in order to overcome
the aforementioned challenges is given. For this purpose, we focus on planning
algorithms, but also consider models of production systems that can act as
inputs to these algorithms. Approaches from both symbolic AI planning as well
as approaches based on Machine Learning are discussed and eventually compared
against the requirements. Based on this comparison, a research agenda is
derived