Engineering 4.0: Digitization through task automation and reuse
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This document discusses engineering digitalization through task automation and reuse in the development lifecycle. It proposes a knowledge-centric approach to systems engineering using a knowledge management strategy. This includes defining a controlled vocabulary, relating terms through relationships and clusters, representing textual patterns for matching, and combining rules and tasks to infer information. This knowledge graph could then enable capabilities like requirements extraction, model population, quality checking, and reuse of system artifacts. The approach aims to automate tasks, link different artifact types, and leverage semantics and AI/ML to better understand and exploit knowledge embedded in systems artifacts.
Engineering 4.0: Digitization through task automation and reuse
- 1. Engineering digitalization through task automation and reuse in the development lifecycle Jose María Alvarez & Juan Llorens | UC3M & TRC | {josemaria.alvarez, llorens}@uc3m.es
- 3. 3 INCOSE IS 2019 3 COE 2021 MBSE Virtual Workshop Sailing the V: engineering digitalization Lifecycle management: the Future of Systems Engineering Source: https://www.researchgate.net/publication/340649785_AI4SE_and_SE4AI_A_Research_Roadmap
- 4. 4 LOTAR MBSE Workshop 4 COE 2021 MBSE Virtual Workshop Sailing the V: engineering digitalization Mats Berglund (Ericsson) http://www.ices.kth.se/upload/events/13/84404189f85d41a6a7d1cafd0db4e e80.pdf Engineering (and corporate) environment Lifecycle processes ISO 15288:2015 Digitalization of the lifecycle: Internet of Tools Source: https://www.nist.gov/system/files/documents/2019/04/05/14_delp.pdf
- 5. 5 INCOSE IS 2019 5 COE 2021 MBSE Virtual Workshop Source: Boeing Sailing the V: engineering digitalization Lifecycle evolution
- 6. 6 INCOSE IS 2019 6 COE 2021 MBSE Virtual Workshop Sailing the V: engineering digitalization Potential needs to digitalize the V Automation Requirement identification and generation Model population Documentation and compliance Traceability Recovery traces Consistency checking Management MBSE Integration and exchange Link logical (descriptive) physical (analytical) Reuse Simulation Configuration Orchestration Link V&V Quality (CCC) Information sharing with providers Configuration Management Evolution and information sharing
- 8. 8 LOTAR MBSE Workshop 8 COE 2021 MBSE Virtual Workshop Sailing the V: engineering digitalization Concept: a knowledge management strategy
- 9. 9 INCOSE IS 2019 9 COE 2021 MBSE Virtual Workshop Sailing the V: engineering digitalization Sailing V: defining the ground truth 01 Controlled Organizational and Project Vocabulary for a common understanding among stakeholders Vocabulary / Terminology 02 Relate the terms in different way representing semantic relationships: - Relationships between terms (Thesaurus) - Clusters of Terms Terms Relationships 04 Information about how can the text being matched by the patterns be represented using graphs Formalization 03 Represent text structures in a way it is possible to do Pattern Matching within the text Textual Patterns 05 A combination of rules, tasks and groups to infer information from existing text Reasoning Info
- 10. 10 LOTAR MBSE Workshop 10 COE 2021 MBSE Virtual Workshop Sailing the V: engineering digitalization E.g. Support smart artifact authoring (requirements)
- 11. 11 LOTAR MBSE Workshop 11 COE 2021 MBSE Virtual Workshop Sailing the V: engineering digitalization Sailing the V: domain artifacts management (hub & gateway) and exploitation Input artifact/operation (and tool) Tool j Transformation rules System Knowledge Base SRL (engineering knowledge graph) Linking: data, information & knowledge Text SysML Modelica Simulink … Transformation rules Text SysML Modelica Simulink … System Knowledge Base Tool k System Assets Store (Knowledge graph) Output artifact/operation (and tool)
- 12. 12 INCOSE IS 2019 12 COE 2021 MBSE Virtual Workshop Sailing the V: engineering digitalization TRC ecosystem: capabilities and tools within the H2020-AHTOOLs project
- 13. User stories 5 user stories in Action
- 14. “That's one small step for a man, one giant leap for engineering” Requirements Engineering As requirements engineer I want to identify and extract requirements from legacy documents. So that I can automate requirements population. MBSE & Requirements As domain engineer I want to populate models from requirements. So that I can keep consistency over time and make my system artifacts executable. Keep data links alive and consistent. Quality: V&V As domain engineer I want to check quality of my system artifacts: models, requirements, etc. So that I can ensure high- quality artifacts from scratch reaching the CCC objectives. Reuse As domain engineer I want to exchange information between tools, find similar system artifacts (e.g. models) and recover traces. So that I can reuse existing knowledge embedded in system artifacts. Digitalization of Engineering As systems engineer I want to have a human friendly environment for the engineering process. So that I can share all information and data with my colleagues in different disciplines.
- 15. Identify and extract requirements from legacy documents Authoring requirements (and any other artifact) VIDEO-1, VIDEO-1B
- 16. Model generation and exploitation VIDEO-2, VIDEO-2B, VIDEO-2C, VIDEO-2D, VIDEO-2E
- 17. Quality: V & V VIDEO-3, VIDEO-3B & VIDEO-3C
- 18. Reuse: finding models and recovering traces VIDEO-4
- 19. Integration of system artifacts & document generation VIDEO-5
- 20. Closing the stage Conclusions & Future Directions
- 21. 21 LOTAR MBSE Workshop 21 COE 2021 MBSE Virtual Workshop Sailing the V: engineering digitalization Collaborative engineering: unleashing data & knowledge Formal ontologies Main use: • To create a knowledge base of the system: knowledge creation (collaborative) • To perform reasoning processes for knowledge inference How to use: • Local and/or distributed reasoning • Not all ontologies are formal ontologies Warning: • Do NOT use ontologies to perform data validation (consistency checking, etc.)time consuming process • Make ontologies “runnable” not just a document • Avoid transformations from different paradigms but boost cooperation between paradigms • e.g. SysMLTransformation or cooperation?OWL Data Shapes Main use: • Data representation, exchange and consistency. • Lightweight semantics”The Shape” How to use: • Data as a Service: create standard- based APIs (technology is NOT relevant, FOUNDATIONS ARE) • OSLC • Swagger (Open API Specification) • REST architectural style (JSON format) Warning: • Define your URIs and methods properly • Expose both: data and operations • Document the use of the API Swagger a good example
- 22. 22 INCOSE IS 2019 22 COE 2021 MBSE Virtual Workshop Sailing the V: engineering digitalization Technology: main applications of the presented approach • “Shared database” • Common data model (representation) • Federated data & knowledge • Query language • Logical view (graph) vs Physical view (?) • Ready for providing functionalities (e.g. quality, traceability, etc.) Technology as a Data hub Process integration • Connection & access to system artifacts • Common data model (representation) • Transformation • Round-trip between tools • No indexing, storage, etc.gateway • Not only exchange data but functionalities on top of data • Consume functionalities provided by tools to integrate results • Provide new functionalities having a data hub Functionality as a Service Technology as a Data gateway • “Message bus, broker etc.”, “Hub-Spoke” • Collaboration between tools to implement a more complex process • Communication and orchestration architecture • Orchestration (e.g. simulation, verification, etc.)
- 23. 23 LOTAR MBSE Workshop 23 COE 2021 MBSE Virtual Workshop Sailing the V: engineering digitalization Interoperability as a key enabler of the lifecycle management
- 24. 24 LOTAR MBSE Workshop 24 COE 2021 MBSE Virtual Workshop Sailing the V: engineering digitalization Conclusions and Future work Focus on data integration, semantics, AI/ML -Understanding of the knowledge embedded in the system artifacts FUS E Automat e Trace Models Simulatio n & Quality Key Enable rs Focus on innovation -Avoid manual tasks -SMART tools for engineers Focus on linking (knowledge graph) -Recover -Manage -Exploit Focus on integration -Model management & population -Model exchange & execution -Link different types of models -SysML V2 API implementation Focus on reuse and continuous quality: -Link simulations (SysPHS and SSP) -Ensure quality over time -Reuse system artifacts -Standardization (interoperability) -Configuration Management -Tools and APIs (e.g. OpenAPI) -Enhanced engineering methods: AI/ML
- 25. 25 LOTAR MBSE Workshop 25 COE 2021 MBSE Virtual Workshop Sailing the V: engineering digitalization Acknowledgements The research leading to these results has received funding from the H2020-ECSEL Joint Undertaking (JU) under grant agreement No 826452-“Arrowhead Tools for Engineering of Digitalisation Solutions” and from specific national programs and/or funding authorities. Learn more: https://www.amass-ecsel.eu/
- 26. Thank you for your attention! Jose María Álvarez- Rodríguez Josemaria.alvarez@uc3m.es @chema_ar Take a seat and comment with us! Juan Llorens llorens@inf.uc3m.es https://www.reusecompany.com/ http://www.kr.inf.uc3m.es/