Model-Based Effect-Chain Analysis for Complex Systems

Gräßler, Iris; Wiechel, Dominik; Koch, Anna-Sophie; Preuß, Daniel; Oleff, Christian

doi:10.1017/pds.2022.191

Cited by 11 publications

(4 citation statements)

References 23 publications

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“…The research presented contributes to the application of AI methods in engineering. Further research potential lies in using formalised requirements for dependency analysis as well as importing formalised requirements into system modelling tools for impact analysis of requirement changes (Gräßler et al, 2022;Gräßler and Pöhler, 2020). Moreover, the potential of applying NLP approaches to automatic elicitation of requirements needs to be explored.…”

Section: Discussionmentioning

confidence: 99%

Efficient Formalisation of Technical Requirements for Generative Engineering

et al. 2023

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Currently, engineers need to manually analyse requirement specifications for determining parameters to create geometries in generative engineering. This analysis is time-consuming, error-prone and causes high costs. Generative engineering tools (e.g. Synera) cannot interpret natural language requirements directly. The requirements need to be formalised in a machine-readable format. AI algorithms have the potential to automatically transform natural language requirements into such a formal, machine-readable representation. In this work, a method for formalising requirements for generative engineering is developed and implemented as a prototype in Python. The method is validated in a case example using three products of an automotive engineering service provider. Requirements to be formalised are identified in the specifications of these three products, which are used as a test set to evaluate the performance of the method. The results show that requirements for generative engineering are formalised with high performance (F1 of 86.55 %). By applying the method, efforts and therefore costs for manually analysing requirements regarding parameters for generative engineering are reduced.

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Section: Discussionmentioning

confidence: 99%

Efficient Formalisation of Technical Requirements for Generative Engineering

et al. 2023

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“…MBSE aims to represent the interplay of requirements, functions, logical solution elements and physical solution elements by means of models. Automated dependency analysis as well as proactive change management hold great potential to more extensively include the requirements layer into MBSE approaches (for instance, for effect chain analysis [91]).…”

Section: Discussionmentioning

confidence: 99%

Proactive Management of Requirement Changes in the Development of Complex Technical Systems

2022

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Requirement changes and cascading effects of change propagation are major sources of inefficiencies in product development and increase the risk of project failure. Proactive change management of requirement changes yields the potential to handle such changes efficiently. A systematic approach is required for proactive change management to assess and reduce the risk of a requirement change with appropriate effort in industrial application. Within the paper at hand, a novel method for Proactive Management of Requirement Changes (ProMaRC) is presented. It is developed in close collaboration with industry experts and evaluated based on workshops, pilot users’ feedback, three industrial case studies from the automotive industry and five development projects from research. To limit the application effort, an automated approach for dependency analysis based on the machine learning technique BERT and semi-automated assessment of change likelihood and impact using a modified PageRank algorithm is developed. Applying the method, the risks of requirement changes are assessed systematically and reduced by means of proactive change measures. Evaluation shows high performance of dependency analysis and confirms the applicability and usefulness of the method. This contribution opens up the research space of proactive risk management for requirement changes which is currently almost unexploited. It enables more efficient product development.

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“…In Projekten zur Automobilentwicklung zeigt sich, dass aktuell erheblicher Aufwand nicht in die Ermittlung funktionaler Anforderungen, sondern in die Extrahierung von Anforderungen aus Regularien investiert wird [1,2]. Ein Beispiel ist das einheitliche Regelwerk mit Regularien zur Zertifizierung von Kraftfahrzeugen, das durch die UN/ECE Transport festlegt wird [3].…”

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AI-based extraction of requirements from regulations for automotive engineering

2023

DS 125: Proceedings of the 34th Symposium Design for X (DFX2023)

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Automotive engineering requires compliance with regulations for certification. In specifications, regulations are referenced, which need to be analyzed manually to elicit requirements. This process is time-consuming and leads to high costs. The aim of this research is to evaluate artificial intelligence (AI) models in terms of extracting requirements automatically from regulations. Relevant AI models are identified in a systematic literature analysis and evaluated using success criteria. The most promising AI models are implemented in a pipeline for requirements extraction. The performance of these AI models is assessed in a comparative study using automotive regulations. The results show which AI models are best suited for this task.

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Model-Based Effect-Chain Analysis for Complex Systems

Cited by 11 publications

References 23 publications

Efficient Formalisation of Technical Requirements for Generative Engineering

Efficient Formalisation of Technical Requirements for Generative Engineering

Proactive Management of Requirement Changes in the Development of Complex Technical Systems

AI-based extraction of requirements from regulations for automotive engineering

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