Towards Effective Management of Inconsistencies in Model-Based Engineering of Automated Production Systems

Feldmann, Stefan; Herzig, Sebastian J. I.; Kernschmidt, Konstantin; Wolfenstetter, Thomas; Kammerl, Daniel; Qamar, Ahsan; Lindemann, Udo; Krcmar, Helmut; Paredis, Christiaan J. J.; Vogel‐Heuser, Birgit

doi:10.1016/j.ifacol.2015.06.200

Cited by 63 publications

(17 citation statements)

References 11 publications

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“…Herzig et al proposed an approach to identify inconsistencies within the context of MBSE where all models are represented by graphs and inconsistencies are identified by means of pattern matching. Feldmann et al: [916‐923] describes the challenges related to managing inconsistencies and proposed a conceptual approach that uses semantic web technologies for identifying inconsistencies in models. These approaches work well in model inconsistency management.…”

Section: Related Workmentioning

confidence: 99%

Model verification for system design of complex mechatronic products

Chen

Liu

Zhao

et al. 2018

Systems Engineering

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Increasing complexity of mechatronic products leads to more challenging designs. As the most important stage, system design determines the overall product architecture. Defects in this stage are difficult to rectify in future stages, thus requiring greater model accuracy. With the development of model-based systems engineering (MBSE), model verification can detect design defects, reduce development costs, improve development efficiency, and ensure system reliability. Currently, conventional verifications are performed only after design processes, which makes it difficult to make systematic modifications. To solve this problem, a model verification method implemented in the design stage is proposed in this study. Using this method, designers are able to detect defects earlier, thereby reducing modification costs. The proposed method is as follows:first, a language named Relation-based Modeling for Static Properties (RMSP) is proposed to formalize the static properties of requirements; second, the representation of "Conceptual Graphs" is extended to meet the needs of model verification of mechatronic products; finally, requirements in RMSP and system design in Systems Modeling Language (SysML) are transformed into the Extension of Conceptual Graphs. In this manner, the incorrectness and inconsistencies between requirements and system design can be found through projection in the system design stage. The omitted or improperly designed requirement items would be found and redesigned to fulfill the product requirements. K E Y W O R D Sconceptual graph, mechatronic product, model verification, SysML, system design 156

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Section: Related Workmentioning

confidence: 99%

Model verification for system design of complex mechatronic products

Chen

Liu

Zhao

et al. 2018

Systems Engineering

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“…• The systems modeling language (SysML) [62] and its variants, such as SysML4Mechatronics [63] and SysML4Modelica [64];…”

Section: Modeling Techniques Applied To Industry 40mentioning

confidence: 99%

A Systematic Mapping Study on Modeling for Industry 4.0

Wortmann

Combemale

Barais

2017

2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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Industry 4.0 is a vision of manufacturing in which smart, interconnected production systems optimize the complete value-added chain to reduce cost and time-to-market. At the core of Industry 4.0 is the smart factory of the future, whose successful deployment requires solving challenges from many domains. Model-based systems engineering (MBSE) is a key enabler for such complex systems of systems as can be seen by the increased number of related publications in key conferences and journals. This paper aims to characterize the state of the art of MBSE for the smart factory through a systematic mapping study on this topic. Adopting a detailed search strategy, 1466 papers were initially identified. Of these, 222 papers were selected and categorized using a particular classification scheme. Hence, we present the concerns addressed by the modeling community for Industry 4.0, how these are investigated, where these are published, and by whom. The resulting research landscape can help to understand, guide, and compare research in this field. In particular, this report identifies the Industry 4.0 challenges addressed by the modeling community, but also the challenges that seem to be less investigated.

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“…Semantic Web Technologies (SWTs) were used in several works to support consistency management across engineering models, including defect detection. Feldmann et al [5,25] focus on identifying inconsistencies that may arise among diverse engineering plans and engineering models created during the ASE process. Such inconsistency detection contributes to the increased productivity of the engineering process as it supports the detection of potentially severe defects early in the engineering process.…”

Section: Semantic Web Technologies For Defect Detectionmentioning

confidence: 99%

“…Performed research revealed that there is very limited support for defect detection in heterogeneous environments. The latter include but are not limited to identifying inconsistencies between variables of mechanical and software (control) engineering artifacts because of the technical heterogeneity of tools and the semantic heterogeneity of data models [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies

Winkler¹,

Sabou²,

Biffl³

2017

Quality Control and Assurance - An Ancient Greek Term Re-Mastered

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In multidisciplinary engineering (MDE) projects, for example, automation systems or manufacturing systems, stakeholders from various disciplines, for example, electrics, mechanics and software, have to collaborate. In industry practice, engineers apply individual and highly specialized tools with strong limitation regarding defect detection in early engineering phases. Experts typically execute reviews with limited tool support which make engineering projects defective and risky. Semantic Web Technologies (SWTs) can help to bridge the gap between heterogeneous sources as foundation for efficient and effective defect detection. Main questions focus on (a) how to bridge gaps between loosely coupled tools and incompatible data models and (b) how SWTs can help to support efficient and effective defect detection in context of engineering process improvement. This chapter describes success-critical requirements for defect detection in MDE and shows how SWTs can provide the foundation for early and efficient defect detection with an adapted review approach. The proposed defect detection framework (DDF) suggests different levels of SWT contributions as a roadmap for engineering process improvement. Two selected industry-related real-life cases show different levels of SWT involvement. Although SWTs have been successfully applied in real-life use cases, SWT applications can be risky if applied without good understanding of success factors and limitations.

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Towards Effective Management of Inconsistencies in Model-Based Engineering of Automated Production Systems

Cited by 63 publications

References 11 publications

Model verification for system design of complex mechatronic products

Model verification for system design of complex mechatronic products

A Systematic Mapping Study on Modeling for Industry 4.0

Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies

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