Design Theory and Similarity Mechanics

The research area design theory and similarity mechanics at the Institute of Aircraft Design deals with the digitization and automation of complex design tasks.

Design languages for design automation

Graph-based design languages map terms (i.e. the "vocabulary") and assembly knowledge (i.e. the "rules") into recombineable language building blocks and operations. In the information representation of a design language, the nodes of a graph serve as abstract placeholders (i.e. as models) for real objects or processes. Thus a graph representation ("design graph") is machine-processed in place of the product design. This machine processing expands and modifies the graph dynamically at runtime by so-called "model-to-model transformations" (M2M). The abstract product representation allows a simple modularization and scalability and allows the consistent integration of different disciplinary domain models (see figure below). The coupling of the framework to domain-specific software systems is essential in order to integrate the existing knowledge and subprocesses. The couplings are implemented as interfaces that generate domain-specific models in the target format (DSL) from the abstract graph representation ("model-to-text transformations" (M2T)). There are already interfaces for geometry (CAD), multi-body systems (MBS), finite elements (FEM), flow analysis (CFD), etc. (see figure below).

Design analysis and evaluation

Using Buckingham's Pi theorem it is possible to transform a physical relationship of n dimensioned variables into a dimensionless description with only m = n - g dimensionless variables. Where g stands for the number of basic variables used. This knowledge can also be transferred to drafts - which can also be described by dimensioned variables - and reinterpreted as evaluation.

Every minimal description in the sense of the Pi theorem is an evaluation.

Schema der Entwurfsanalyse und -bewertung

Digital factory

In addition to a multitude of boundary conditions, a product has a significant influence on the associated (digital) factory. The information already provided by a product created with graph-based design languages can be further processed automatically and used to generate suitable production sites. The aim is to be able to automatically design an optimal factory (in terms of costs, energy, time, etc.). This concerns the resources used as well as the manufacturing processes. In addition, the "Pi Group" is also concerned with the question of the optimal factory layout.

Photo: ZAFH Project DiP

Automated cable routing and piping

An essential step in the design of complex products is cabling and piping. The "Pi-Group" researches algorithms for automated cable and pipe routing, as well as the physical simulation of cables and pipes for installation simulations and other applications.

Photo: IILS mbH
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© IILS mbH

Projects

The Center for Applied Research "Digital Product Lifecycle" (DiP) transfers the extremely powerful approaches of hardware and software development (e.g. the Unified Modellig Language UML) and adapts them to wide areas of mechanical engineering and automotive engineering. The aim is to integrate the entire product life cycle with all relevant product, process and resource-related data into a digital overall model and to provide the necessary processes, methods, tools and libraries.

Digital modelling of the product life cycle of a car bonnet

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© ZaFH Project "Digital Product Lifecycle (DiP)"

Homepage Digital Product Lifecycle

The overall IDEaliSM aim can be divided into three main-objectives:

  • An advanced integration framework for distributed multidisciplinary design and optimization enabling Competence Centres to offer and share engineering services and to collaborate in Distributed Development Teams.
  • An Engineering Language Workbench: a set of domain specific and high-level modelling languages, ontologies and data standards to enable flexible configuration of engineering workflows and services and enable straightforward integration into distributed the advanced integration framework.
  • A methodology for service-oriented development processes to redefine the product development process and information architecture to enable collaboration between service-oriented Competence Centers in Distributed Development Teams.

The resulting development framework will support European industries to enhance their level of integration and flexibility in product development to reduce the effort, cost and time-to-market in designing innovative aircraft and automotive structures and systems. As such, IDEaliSM fits well within the ‘Engineering Technology’ field identified in the ITEA2 research roadmap and the challenges within the Systems Engineering and Software Engineering domains.

Homepage Project IDEaliSM

Publications

All publications by year of publication

  1. 2023

    1. Voss, C., Petzold, F., & Rudolph, S. (2023). Graph transformation in engineering design : an overview of the last decade. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 37, 1–17. https://doi.org/10.1017/S089006042200018X
    2. Dinkelacker, J., Kaiser, D., Panzeri, M., Parmentier, P., Neumaier, M., Tonhäuser, C., & Rudolph, S. (2023). System integration based on packing, piping and harness routing automation using graph-based design languages. CEAS Aeronautical Journal, 14, 591–606. https://doi.org/10.1007/s13272-023-00642-5

  2. 2022

    1. Elwert, M., Ramsaier, M., Eisenbart, B., Stetter, R., Till, M., & Rudolph, S. (2022). Digital Function Modeling in Graph-Based Design Languages. Applied Sciences, 12(11), Article 11. https://doi.org/10.3390/app12115301
    2. Neumaier, M., Kranemann, S., Kazmeier, B., & Rudolph, S. (2022). Automated Piping in an Airbus A320 Landing Gear Bay Using Graph-Based Design Languages. Aerospace, 9(3), Article 3. https://doi.org/10.3390/aerospace9030140
    3. Schneider, T., Qiu, C., Kloft, M., Aspandi-Latif, D., Staab, S., Mandt, S., & Rudolph, M. (2022). Detecting Anomalies within Time Series using Local Neural Transformations. CoRR, abs/2202.03944. http://dblp.uni-trier.de/db/journals/corr/corr2202.html#abs-2202-03944

  3. 2021

    1. Qiu, C., Mandt, S., & Rudolph, M. (2021). History Marginalization Improves Forecasting in Variational Recurrent Neural Networks. Entropy, 23(12), Article 12. http://dblp.uni-trier.de/db/journals/entropy/entropy23.html#QiuMR21
    2. Schopper, D., Kübler, K., Rudolph, S., & Riedel, O. (2021). EIPPM : The Executable Integrative Product-Production Model. Computers, 10(6), Article 6. https://doi.org/10.3390/computers10060072
    3. Holder, K., Schumacher, S., Friedrich, M., Till, M., Stetter, R., Fichter, W., & Rudolph, S. (2021). Digital Development Process for the Drive System of a Balanced Two-Wheel Scooter. Vehicles, 3(1), Article 1. https://doi.org/10.3390/vehicles3010003
    4. Schopper, D., Kübler, K., Rudolph, S., & Riedel, O. (2021). EIPPM—The Executable Integrative Product-Production Model. Computers, 10(6), Article 6. https://doi.org/10.3390/computers10060072

  4. 2020

    1. Qiu, C., Mandt, S., & Rudolph, M. (2020). Variational Dynamic Mixtures. CoRR, abs/2010.10403. http://dblp.uni-trier.de/db/journals/corr/corr2010.html#abs-2010-10403
    2. Borowski, J., Stetter, R., & Rudolph, S. (2020). Design, Dimensioning and Simulation of Inerters for the Reduction of Vehicle Wheel Vibrations : Case Studies. Vehicles, 2(3), Article 3. https://doi.org/10.3390/vehicles2030023
    3. Kübler, K., Schopper, D., Riedel, O., & Rudolph, S. (2020). Towards an Automated Product-Production System Design - Combining Simulation-based Engineering and Graph-based Design Languages. Procedia Manufacturing, 52, 258--265. https://doi.org/10.1016/j.promfg.2020.11.043
    4. Kübler, K., Schopper, D., Riedel, O., & Rudolph, S. (2020). Towards an Automated Product-Production System Design - Combining Simulation-based Engineering and Graph-based Design Languages. Procedia Manufacturing, 52, 258--265. https://doi.org/10.1016/j.promfg.2020.11.043

  5. 2019

    1. Walter, B., Martin, J., Schmidt, J., Dettki, H., & Rudolph, S. (2019). Executable State Machines Derived from Structured Textual Requirements - Connecting Requirements and Formal System Design. In S. Hammoudi, L. F. Pires, & B. Selic (Eds.), MODELSWARD (pp. 193–200). SciTePress. http://dblp.uni-trier.de/db/conf/modelsward/modelsward2019.html#WalterMSDR19
    2. Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2019). Bi-objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm. In K. Deb, E. D. Goodman, C. A. C. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, & P. Reed (Eds.), EMO (Vol. 11411, pp. 516–528). Springer. http://dblp.uni-trier.de/db/conf/emo/emo2019.html#BossekGMRT19
    3. Walter, B., Kaiser, D., & Rudolph, S. (2019). Machine-Executable Model-Based Systems Engineering with Graph-Based Design Languages. In E. Bonjour, D. Krob, L. Palladino, & F. Stephan (Eds.), Complex Systems Design & Management (p. 239). Springer. https://doi.org/10.1007/978-3-030-04209-7_25
    4. Holder, K., Rudolph, S., Stetter, R., & Salander, C. (2019). Automated requirements-driven design synthesis of gearboxes with graph-based design languages using state of the art tools. Best of Gears 2019, 83, 3, Article 83, 3. https://doi.org/10.1007/s10010-019-00322-z
    5. Walter, B., Kaiser, D., & Rudolph, S. (2019). From Manual to Machine-executable Model-based Systems Engineering via Graph-based Design Languages. In S. Hammoudi, L. F. Pires, & B. Selic (Eds.), MODELSWARD (pp. 201–208). SciTePress. http://dblp.uni-trier.de/db/conf/modelsward/modelsward2019.html#WalterKR19
    6. Zech, A., Stetter, R., Holder, K., Rudolph, S., & Till, M. (2019). Novel approach for a holistic and completely digital represented product development process by using graph-based design languages. In R. Teti (Ed.), 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 18-20 July 2018, Gulf of Naples, Italy (79; Issue 79, pp. 568–573). Elsevier. https://doi.org/10.1016/j.procir.2019.02.102

  6. 2018

    1. Vogel, S., & Rudolph, S. (2018). Complex System Design with Design Languages: Method, Applications and Design Principles. CoRR, abs/1805.09111. http://dblp.uni-trier.de/db/journals/corr/corr1805.html#abs-1805-09111
    2. Walter, B., Schilling, M., Piechotta, M., & Rudolph, S. (2018). Improving Test Execution Efficiency Through Clustering and Reordering of Independent Test Steps. 2018 IEEE 11th International Conference on Software Testing, Verification and Validation (ICST), 363–373. https://doi.org/10.1109/ICST.2018.00043
    3. Walter, B., Kaiser, D., & Rudolph, S. (2018). Machine-Executable Model-Based Systems Engineering with Graph-Based Design Languages. In E. Bonjour, D. Krob, L. Palladino, & F. Stephan (Eds.), CSDM (p. 239). Springer. http://dblp.uni-trier.de/db/conf/csdm/csdm2018.html#WalterKR18
    4. Schopper, D., & Rudolph, S. (2018). From Model-Driven Architecture and Model-Based Systems Engineering via Formal Concept Analysis to Graph-Based Design Languages and Back: A Scientific Discourse. ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2018, 51739, Article 51739. https://doi.org/10.1115/DETC2018-86392
    5. Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2018). Local search effects in bi-objective orienteering. In H. E. Aguirre & K. Takadama (Eds.), GECCO (pp. 585–592). ACM. http://dblp.uni-trier.de/db/conf/gecco/gecco2018.html#BossekGMRT18
    6. Wiedorn, M. O., Oberthür, D., Bean, R., Schubert, R., Werner, N., Abbey, B., Aepfelbacher, M., Adriano, L., Allahgholi, A., Al-Qudami, N., Andreasson, J., Aplin, S., Awel, S., Ayyer, K., Bajt, S., Barák, I., Bari, S., Bielecki, J., Botha, S., … Barty, A. (2018). Megahertz serial crystallography. Nature Communications, 9(1), Article 1. https://doi.org/10.1038/s41467-018-06156-7

  7. 2017

    1. Walter, B., Hammes, J., Piechotta, M., & Rudolph, S. (2017). A Formalization Method to Process Structured Natural Language to Logic Expressions to Detect Redundant Specification and Test Statements. In A. Moreira, J. Araújo, J. Hayes, & B. Paech (Eds.), RE (pp. 263–272). IEEE Computer Society. http://dblp.uni-trier.de/db/conf/re/re2017.html#WalterHPR17
    2. Holder, K., Zech, A., Ramsaier, M., Stetter, R., Niedermeier, H.-P., Rudolph, S., & Till, M. (2017). Model-Based Requirements Management in Gear Systems Design Based On Graph-Based Design Languages. Applied Sciences, 7(11), Article 11. https://doi.org/10.3390/app7111112

  8. 2016

    1. Gross, J., & Rudolph, S. (2016). Modeling graph-based satellite design languages. Aerospace Science and Technology, 49, 63–72. https://doi.org/10.1016/j.ast.2015.11.026
    2. Schmidt, J., & Rudolph, S. (2016). Graph-Based Design Languages: A Lingua Franca for Product Design Including Abstract Geometry. IEEE Computer Graphics and Applications, 36(5), Article 5. https://doi.org/10.1109/MCG.2016.89
    3. Gross, J., & Rudolph, S. (2016). Rule-based spacecraft design space exploration and sensitivity analysis. Aerospace Science and Technology, 59, 162–171. https://doi.org/10.1016/j.ast.2016.10.007
    4. Gross, J., & Rudolph, S. (2016). Geometry and simulation modeling in design languages. Aerospace Science and Technology, 54, 183–191. https://doi.org/10.1016/j.ast.2016.03.003
    5. Rudolph, M., Ruiz, F. J. R., Mandt, S., & Blei, D. M. (2016). Exponential Family Embeddings. In D. D. Lee, M. Sugiyama, U. von Luxburg, I. Guyon, & R. Garnett (Eds.), NIPS (pp. 478–486). http://dblp.uni-trier.de/db/conf/nips/nips2016.html#RudolphRMB16

  9. 2015

    1. Grimme, C., Meisel, S., Trautmann, H., Rudolph, G., & Wölck, M. (2015). Multi-objective Analysis of Approaches to Dynamic Routing of a Vehicle. In J. Becker, J. vom Brocke, & M. de Marco (Eds.), ECIS. http://dblp.uni-trier.de/db/conf/ecis/ecis2015.html#GrimmeMTRW15
    2. Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., & Trautmann, H. (2015). Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. In S. Silva & A. I. Esparcia-Alcázar (Eds.), GECCO (pp. 425–432). ACM. http://dblp.uni-trier.de/db/conf/gecco/gecco2015.html#MeiselGBWRT15

  10. 2014

    1. Beilstein, L. T., & Rudolph, S. (2014). Optimum function for minimum weight evaluation of structural joints. In E. Plödereder, L. Grunske, E. Schneider, & D. Ull (Eds.), GI-Jahrestagung: Vol. P-232 (pp. 1961–1965). GI. http://dblp.uni-trier.de/db/conf/gi/gi2014.html#BeilsteinR14

  11. 2013

    1. Furgale, P. T., Schwesinger, U., Rufli, M., Derendarz, W., Grimmett, H., Mühlfellner, P., Wonneberger, S., Timpner, J., Rottmann, S., Li, B., Schmidt, B., Nguyen, T.-N., Cardarelli, E., Cattani, S., Bruning, S., Horstmann, S., Stellmacher, M., Mielenz, H., Köser, K., … Siegwart, R. (2013). Toward automated driving in cities using close-to-market sensors: An overview of the V-Charge Project. Intelligent Vehicles Symposium, 809–816. http://dblp.uni-trier.de/db/conf/ivs/ivs2013.html#FurgaleSRDGMWTRLSNCCBHSMKBHHLFITPNWPBEPS13

  12. 2012

    1. Rudolph, S., Heisserman, J., & Culley, S. (2012). Design Computing and Cognition (DCC’10). AI EDAM, 26(2), Article 2. http://dblp.uni-trier.de/db/journals/aiedam/aiedam26.html#RudolphHC12
    2. Congote, J., Novo, E., Kabongo, L., Ginsburg, D., Gerhard, S., Pienaar, R., & Ruiz, O. E. (2012). Real-time Volume Rendering and Tractography Visualization on the Web. Journal of WSCG, 20(2), Article 2. http://dblp.uni-trier.de/db/journals/jwscg/jwscg20.html#CongoteNKGGPR12
    3. Groß, J., & Rudolph, S. (2012). Generating simulation models from UML - a FireSat example. In G. A. Wainer & P. J. Mosterman (Eds.), SpringSim (TMS-DEVS) (p. 25). SCS/ACM. http://dblp.uni-trier.de/db/conf/springsim/springsim2012-4.html#GrossR12

  13. 2011

    1. Mehdi, A., Rudolph, S., & Grimm, S. (2011). Epistemic Querying of OWL Knowledge Bases. In G. Antoniou, M. Grobelnik, E. P. B. Simperl, B. Parsia, D. Plexousakis, P. D. Leenheer, & J. Z. Pan (Eds.), ESWC (1) (Vol. 6643, pp. 397–409). Springer. http://dblp.uni-trier.de/db/conf/esws/eswc2011-1.html#MehdiRG11
    2. Reuter, C., Dadam, P., Rudolph, S., Deiters, W., & Trillsch, S. (2011). Guarded Process Spaces (GPS): A Navigation System towards Creation and Dynamic Change of Healthcare Processes from the End-User’s Perspective. In F. Daniel, K. Barkaoui, & S. Dustdar (Eds.), Business Process Management Workshops (2) (Vol. 100, pp. 237–248). Springer. http://dblp.uni-trier.de/db/conf/bpm/bpmw2011-2.html#ReuterDRDT11

  14. 2010

    1. Otto, S., & Bannenberg, T. (2010). Decentralized Evolutionary Agents Streamlining Logistic Network Design. In R. Schaefer, C. Cotta, J. Kolodziej, & G. Rudolph (Eds.), PPSN (2) (Vol. 6239, pp. 240–249). Springer. http://dblp.uni-trier.de/db/conf/ppsn/ppsn2010-2.html#OttoB10

  15. 2007

    1. Haq, M., & Rudolph, S. (2007). A design language for generic space-frame structure design. IJCAT, 30(1/2), Article 1/2. http://dblp.uni-trier.de/db/journals/ijcat/ijcat30.html#HaqR07

  16. 2005

    1. Rusdorf, S., & Brunnett, G. (2005). Real time tracking of high speed movements in the context of a table tennis application. In G. Singh, R. W. H. Lau, Y. Chrysanthou, & R. P. Darken (Eds.), VRST (pp. 192–200). ACM. http://dblp.uni-trier.de/db/conf/vrst/vrst2005.html#RusdorfB05

  17. 2003

    1. Brückner, S., & Rudolph, S. (2003). Knowledge discovery in engineering dynamic system analysis. In B. V. Dasarathy (Ed.), Data Mining and Knowledge Discovery: Theory, Tools, and Technology (Vol. 5098, pp. 185–192). SPIE. http://dblp.uni-trier.de/db/conf/dmkdttt/dmkdttt2003.html#BrucknerR03

  18. 2002

    1. Brückner, S., & Rudolph, S. (2002). Aspects of knowledge discovery in technical data. In B. V. Dasarathy (Ed.), Data Mining and Knowledge Discovery: Theory, Tools, and Technology (Vol. 4730, pp. 109–117). SPIE. http://dblp.uni-trier.de/db/conf/dmkdttt/dmkdttt2002.html#BrucknerR02
    2. Barrios, L. J., & Rudolph, S. (2002). Knowledge discovery process for scientific and engineering data. In B. V. Dasarathy (Ed.), Data Mining and Knowledge Discovery: Theory, Tools, and Technology (Vol. 4730, pp. 118–125). SPIE. http://dblp.uni-trier.de/db/conf/dmkdttt/dmkdttt2002.html#BarriosR02

  19. 2001

    1. Brückner, S., & Rudolph, S. (2001). Knowledge discovery in scientific data using hierarchical modeling in dimensional analysis. In B. V. Dasarathy (Ed.), Data Mining and Knowledge Discovery: Theory, Tools, and Technology (Vol. 4384, pp. 208–217). SPIE. http://dblp.uni-trier.de/db/conf/dmkdttt/dmkdttt2001.html#BrucknerR01

  20. 2000

    1. Rudolph, S. (2000). Knowledge discovery in scientific data. In B. V. Dasarathy (Ed.), Data Mining and Knowledge Discovery: Theory, Tools, and Technology (Vol. 4057, pp. 250–258). SPIE. http://dblp.uni-trier.de/db/conf/dmkdttt/dmkdttt2000.html#Rudolph00
    2. Hertkorn, P., & Rudolph, S. (2000). Systematic method to identify patterns in engineering data. In B. V. Dasarathy (Ed.), Data Mining and Knowledge Discovery: Theory, Tools, and Technology (Vol. 4057, pp. 273–280). SPIE. http://dblp.uni-trier.de/db/conf/dmkdttt/dmkdttt2000.html#HertkornR00

  21. 1999

    1. Hertkorn, P., & Rudolph, S. (1999). From data to models: synergies of a joint data mining and similarity theory approach. In B. V. Dasarathy (Ed.), Data Mining and Knowledge Discovery: Theory, Tools, and Technology (Vol. 3695, pp. 120–127). SPIE. http://dblp.uni-trier.de/db/conf/dmkdttt/dmkdttt1999.html#HertkornR99

  22. 1997

    1. Rudolph, S. (1997). On topology, size and generalization of non-linear feed-forward neural networks. Neurocomputing, 16(1), Article 1. http://dblp.uni-trier.de/db/journals/ijon/ijon16.html#Rudolph97
    2. Lagerstrom, R. N., & Gipp, S. K. (1997). PScheD: Political Scheduling on the CRAY T3E. In D. G. Feitelson & L. Rudolph (Eds.), JSSPP (Vol. 1291, pp. 117–138). Springer. http://dblp.uni-trier.de/db/conf/jsspp/jsspp1997.html#LagerstromG97

Group members

This image shows Stephan Rudolph

Stephan Rudolph

PD Dr.-Ing.

Head of research group "Design Theory and Similarity Mechanics"

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