Pipeline for ontology-based modeling and automated deployment of digital twins for planning and control of manufacturing systems

Abstract

The demand for individualized products drives modern manufacturing systems towards greater adaptability and flexibility. This increases the focus on data-driven digital twins enabling swift adaptations. Within the framework of cyber-physical systems, the digital twin is a digital model that is fully connected to the physical and digital assets. A digital model must follow a standardization for interoperable data exchange. Established ontologies and meta-models offer a basis in the definition of a schema, which is the first phase of creating a digital twin. The next phase is the standardized and structured modeling with static use-case specific data. The final phase is the deployment of digital twins into operation with a full connection of the digital model with the remaining cyber-physical system. In this deployment phase communication standards and protocols provide a standardized data exchange. A survey on the state-of-the-art of these three digital twin phases reveals the lack of a consistent workflow from ontology-driven definition to standardized modeling. Therefore, one goal of this paper is the design of an end-to-end digital twin pipeline to lower the threshold of creating and deploying digital twins. As the task of establishing a communication connection is highly repetitive, an automation concept by providing structured protocol data is the second goal. The planning and control of a line-less assembly system with manual stations and a mobile robot as resources and an industrial dog as the product serve as exemplary digital twin applications. Along this use-case the digital twin pipeline is transparently explained.

Ontology-based task allocation for heterogeneous resources in Line-less Mobile Assembly Systems

“…For building the domain ontology, the broadly accepted seven-step procedure according to NOY et al [12] was followed due to its application-oriented structure. Moreover, the first two steps (definition phase: developing an ontology and modeling phase: modeling of a use-case) of the digital twin pipeline of GÖPPERT et al were applied [13]. Finally, validation and verification are carried out following SARGENT et al and GÓMEZ-PÉREZ through application ontology and performance testing [14,15].…”

“…An application ontology was implemented to validate the framework within its intended scope and determine whether its output behavior provides an acceptable accuracy [14]. Use-case-specific instances are modeled according to the conceptual ontology, creating a knowledge base/description model on an operational level, consistent with [8], [13] and [12]. The process of truck chassis assembly is used as an application scenario: The parts 'cross member', 'front member' and 'rear member' have to be transported from storage to the chassis and have to be screwed onto 'chassis'.…”

Online capability-based resource allocation for on-site construction operations utilizing digital twin models

“…Based on the approaches from Sect. 2.1, the approach from Göppert et al (2021), who proposed a Digital Twin Pipeline for automated deployment of a DT, will be applied in the following. Their methodology is used because they provide a holistic approach to DT development (from ontology to deployment) that the other authors cited in Sect.…”

“…Kousi et al (2019) automatically deployed a DT in the Robot Operating System (ROS) framework with a focus on 3D environment representation (Kousi et al 2019). Based on these approaches, Göppert et al (2021) proposed a Digital Twin Pipeline for automated deployment of a DT (Göppert et al 2021).…”

Ontology-based task allocation for heterogeneous resources in Line-less Mobile Assembly Systems

“…In the following the first phase of ontology-based modeling, the definition phase, according to GÖPPERT et al [13], is described and the conceptual CAPabILity-based resource AllocatioN Ontology (CAPILANO) is presented. The broadly applied concept of dividing assembly systems into product, process and resource of MARTIN et al was followed.…”