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Data-driven and Real-time Prediction Models for Highly Iterative Product Development Processes
Jönköping University, School of Engineering, JTH, Industrial Product Development, Production and Design, JTH, Product design and development (PDD).ORCID iD: 0000-0002-7894-7734
Jönköping University, School of Engineering, JTH, Industrial Product Development, Production and Design, JTH, Production development. Jönköping University, School of Engineering, JTH, Industrial Product Development, Production and Design, JTH, Product design and development (PDD).ORCID iD: 0000-0002-6761-597X
Jönköping University, School of Engineering, JTH, Industrial Product Development, Production and Design, JTH, Product design and development (PDD). Jönköping University, School of Engineering, JTH, Industrial Product Development, Production and Design, JTH, Production development.ORCID iD: 0000-0001-6278-2499
2022 (English)In: Transdisciplinarity and the Future of Engineering / [ed] B. R. Moser, P. Koomsap, J. Stjepandić, IOS Press, 2022, p. 463-472Conference paper, Published paper (Refereed)
Abstract [en]

Some high-level technical products are associated with transdisciplinary simulation-driven design processes. Therefore, their design process involves many stakeholders and is prone to frequent changes, leading to a highly iterative process with a long lead time. Despite the decades of statistical approximations and metamodeling techniques on prediction models, companies are still striving toachieve fully automated real-time predictions in early design phases. The literature study shows a gap in existing methods such as not being fully real-time or suffering from high dimensionality. This paper presents a generic model for the development process of such described products and motivation for such modeling through a series of semi-structured interviews with an automotive sub-supplier company. The proposed process model points to the digital verification in every design loop as the bottleneck which is then confirmed by interviewees. As alternative solutions to overcome the problems, a method for data-driven and real-time prediction models is presented to enable the designer to foresee the consequence of their decision in the design phase. To evaluate the method, two examples of such real-time metamodeling techniques, developed in an ongoing research project are discussed. The proposed examples confirm that the framework can reduce lead time spent on digital verification and therefore accelerate the design process in such products.

Place, publisher, year, edition, pages
IOS Press, 2022. p. 463-472
Series
Advances in Transdisciplinary Engineering ; 28
Keywords [en]
Data-driven, Prediction models, Design automation, Simulation-driven design, CAD/CAE
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-56575DOI: 10.3233/ATDE220676Scopus ID: 2-s2.0-85142195146ISBN: 978-1-64368-338-6 (print)ISBN: 978-1-64368-339-3 (electronic)OAI: oai:DiVA.org:hj-56575DiVA, id: diva2:1661182
Conference
29th ISTE International Conference on Transdisciplinary Engineering, 2022, MI, MA, USA
Available from: 2022-05-25 Created: 2022-05-25 Last updated: 2022-11-30Bibliographically approved
In thesis
1. Data-driven and real-time prediction models for iterative and simulation-driven design processes
Open this publication in new window or tab >>Data-driven and real-time prediction models for iterative and simulation-driven design processes
2022 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The development of more complex products has increased dependency on virtual/digital models and emphasized the role of simulations as a means of validation before production. This level of dependency on digital models and simulation togetherwith the customization level and continuous requirement change leads to a large number of iterations in each stage of the product development process. This research, studies such group of products that have multidisciplinary, highly iterative, and simulation-driven design processes. It is shown that these high-level technical products, which are commonly outsourced to suppliers, commonly suffer from a long development lead time. The literature points to several research tracks including design automation and data-driven design with possible support. After studying the advantages and disadvantages of each track, a data-driven approachis chosen and studied through two case studies leading to two supporting tools that are expected to improve the development lead time in associated design processes. Feature extraction in CAD as a way to facilitate metamodeling is proposed as the first solution. This support uses the concept of the medial axis to find highly correlated features that can be used in regression models. As for the second supporting tool, an automated CAD script is used to produce a library of images associated with design variants. Dynamic relaxation is used to label each variant with its finite element solution output. Finally, the library is used to train a convolutions neural network that maps screenshots of CAD as input to finite element field answers as output. Both supporting tools can be used to create real-time prediction models in the early conceptual phases of the product development process to explore design space faster and reduce lead time and cost.

Abstract [sv]

Utvecklingen av mer komplexa produkter har ökat beroendet av virtuella/digitala modeller och ökat betydelsen av simuleringar för att validera en produkt inför produktion. Ett stort beroende av digitala modeller och simulering tillsammans med den individuella anpassningen och kontinuerliga kravförändringar leder till ett stort antal iterationer i varje steg i produktutvecklingsprocessen. Forskningen som presenteras i denna avhandling studerar denna typ av produkter som har multidisciplinära, mycket iterativa och simuleringsdrivna designprocesser. Det har visat sig att dessa tekniska produkter på hög nivå, som vanligtvis tillhandahålls av underleverantörer, vanligtvis har en lång ledtid för utveckling. Litteraturstudien pekar på flera forskningsspår, exempelvis designautomation och datadriven design, eventuellt med stöd. Efter att ha studerat fördelarna och nackdelarna med varje spår, väljs det datadrivna tillvägagångssättet och studeras genom två fallstudier som leder till att två stödjande verktyg tas fram. De förväntas förbättra utvecklingsledtiden i tillhörande designprocesser. Feature extraktion i CAD som ett sätt att underlätta metamodellering föreslås som det första verktyget. Detta stöd använder medial axis för att hitta korrelerade features som kan användas i regressionsmodeller. När det gäller det andra stödjande verktyget används ett automatiserat CAD-skript för att producera ett stort bibliotek med bilder som är associerade olika designvarianter. Dynamisk relaxation används för att märka varje variant med dess finita elementlösning. Slutligen används detta bibliotek för att träna ett konvolutionerande neuralt nätverk som kartlägger skärmdumpar av CAD som indata till finita elementfältsvar som utdata. Båda stödverktygen kan användas för att skapa modeller för förutsägelser i realtid i de tidiga konceptuella faserna av produktutvecklingsprocessen för att utforska designrymden snabbare och minska ledtid och kostnader.

Place, publisher, year, edition, pages
Jönköping: Jönköping University, School of Engineering, 2022. p. 59
Series
JTH Dissertation Series ; 071
Keywords
Development lead time; Iterative design; Simulation-driven design; Design automation; Data-driven design; Artificial Intelligence
National Category
Computer Sciences
Identifiers
urn:nbn:se:hj:diva-56577 (URN)978-91-87289-77-4 (ISBN)
Presentation
2022-06-14, E1405, Tekniska Högskolan, Jönköping University, Jönköping, 10:00 (Swedish)
Opponent
Supervisors
Funder
Knowledge Foundation
Available from: 2022-05-25 Created: 2022-05-25 Last updated: 2022-05-25Bibliographically approved

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Arjomandi Rad, MohammadRaudberget, DagStolt, Roland

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