Multi-Disciplinary Engineering for Industrie 4.0: Semantic Challenges and Needs

Biffl, Stefan; Lüder, Arndt; Winkler, Dietmar

doi:10.1007/978-3-319-41490-4_2

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…During the product creation period, product and production development takes place in the product lifecycle phases of (technical) design and work preparation. The latter represents the link between development and production, which are interdependent in many ways (Biffl et al, 2016;Bremer, 2020). One part of work preparation is work planning (Arndt, 2021).…”

Section: Narrowing Down the Scope And Identifying The State Of The Artmentioning

confidence: 99%

Investigating low data consistency in work planning processes – causes, measures, and opportunities

Dausch,

Langner,

Roth

et al. 2024

Proc. Des. Soc.

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Digital transformation increases the need for interdisciplinary collaboration along the product lifecycle. It is currently hindered by a low data consistency resulting from the use of heterogeneous systems and data models. Especially in work planning, where several data models are combined, this decreases efficiency. Systems Lifecycle management (SysLM) offers a solution to this remedy. However, a sudden switch to SysLM is not possible in brownfields. Thus, it is necessary to examine the challenges and opportunities to derive case-specific measures that enable its adoption in work planning.

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Section: Narrowing Down the Scope And Identifying The State Of The Artmentioning

confidence: 99%

Investigating low data consistency in work planning processes – causes, measures, and opportunities

Dausch,

Langner,

Roth

et al. 2024

Proc. Des. Soc.

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show abstract

“…However, engineers typically use isolated tools with data models that provide only limited capabilities regarding data exchange and synchronization of engineering plans across disciplines and organizations [2]. Such loosely coupled tools and incompatible data models hinder efficient engineering processes, project management, quality assurance, and engineering process improvement [3]. Due to limited collaboration capabilities, engineering projects become risky and error-prone.…”

Section: Introductionmentioning

confidence: 99%

Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies

Winkler¹,

Sabou²,

Biffl³

2017

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

Self Cite

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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 a Flexible and Secure Round-Trip-Engineering Process for Production Systems Engineering with Agile Practices

Winkler

Rinker

Kieseberg

2018

Lecture Notes in Business Information Processing

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Multi-Disciplinary Engineering for Industrie 4.0: Semantic Challenges and Needs

Cited by 8 publications

References 18 publications

Investigating low data consistency in work planning processes – causes, measures, and opportunities

Investigating low data consistency in work planning processes – causes, measures, and opportunities

Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies

Towards a Flexible and Secure Round-Trip-Engineering Process for Production Systems Engineering with Agile Practices

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