Inconsistency management in heterogeneous engineering data in intralogistics based on coupled metamodels

Abstract

During the development of intralogistics systems (ILS), heterogeneous models are created, which represent discipline-specific views, e.g., control software developed by automation engineers or discrete-event simulation models created by simulation engineers. These models represent discipline-specific views on the system but contain overlapping information. Thereby, keeping the information in different development models consistent is challenging and currently requires high manual effort, which highly depends on the developers’ experience. To overcome this challenge, an approach to link heterogeneous model data and identify potential information inconsistencies within and between models automatically is proposed. The concept is evaluated with a use case containing three typical inconsistencies from five representative engineering models applied in ILS development.

Ontology Versioning for Managing Inconsistencies in Engineering Models Arising From Model Changes in the Design of Intralogistics Systems

“…In their approach, different metamodels were coupled with consistency rules, represented as links, to build a virtual metamodel that automatically links models in the software domain. Building upon this work, our previous research [8] integrated the V-SUMM approach with semantic web technologies to automatically connect and identify inconsistencies in heterogeneous engineering models used for ILS development. The simplified framework is displayed in Fig.…”

“…In our previous research [8], the approach for automatic coupling of discipline-specific model data during ILS development does not include the detection of model changes, thus not fulfilling of R1. Moreover, although various inconsistencies are detectable and traceable to multiple source models in that work, their traceability to different model versions remained unaddressed, leading to the partial fulfillment of R4.…”

“…Moreover, although various inconsistencies are detectable and traceable to multiple source models in that work, their traceability to different model versions remained unaddressed, leading to the partial fulfillment of R4. Additionally, the approach in [8] does not consider model versioning (R2) and the analysis of the model change effects (R3). Building upon the model integration and inconsistency checking concept introduced in our prior research [8], this paper aims to address the identified research gaps in the current state of the art and fulfill the requirements outlined in Section I.…”

“…Additionally, the approach in [8] does not consider model versioning (R2) and the analysis of the model change effects (R3). Building upon the model integration and inconsistency checking concept introduced in our prior research [8], this paper aims to address the identified research gaps in the current state of the art and fulfill the requirements outlined in Section I. The main contribution of the paper is the introduction of an ontology-versioning approach that enables the detection and recording of changes in multiple versions of heterogeneous model data and automatically identifies cross-model change effects related to the introduced inconsistencies.…”

“…1. Framework for automatic inconsistency checking based on coupled metamodels (simplified from [8]).…”