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Tree-Based Response Surface Analysis
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-3311-2530
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Engineering Method Development, GKN Aerospace Engine Systems Sweden.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Computer-simulated experiments have become a cost effective way for engineers to replace real experiments in the area of product development. However, one single computer-simulated experiment can still take a significant amount of time. Hence, in order to minimize the amount of simulations needed to investigate a certain design space, different approaches within the design of experiments area are used. One of the used approaches is to minimize the time consumption and simulations for design space exploration through response surface modeling. The traditional methods used for this purpose are linear regression, quadratic curve fitting and support vector machines. This paper analyses and compares the performance of four machine learning methods for the regression problem of response surface modeling. The four methods are linear regression, support vector machines, M5P and random forests. Experiments are conducted to compare the performance of tree models (M5P and random forests) with the performance of non-tree models (support vector machines and linear regression) on data that is typical for concept evaluation within the aerospace industry. The main finding is that comprehensible models (the tree models) perform at least as well as or better than traditional black-box models (the non-tree models). The first observation of this study is that engineers understand the functional behavior, and the relationship between inputs and outputs, for the concept selection tasks by using comprehensible models. The second observation is that engineers can also increase their knowledge about design concepts, and they can reduce the time for planning and conducting future experiments.

Place, publisher, year, edition, pages
Springer, 2015. Vol. 9432, p. 12p. 118-129
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9432
Keywords [en]
Machine learning, Regression, Surrogate model, Response surface model
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-11442DOI: 10.1007/978-3-319-27926-8_11ISBN: 978-3-319-27925-1 (print)OAI: oai:DiVA.org:bth-11442DiVA, id: diva2:895539
Conference
The International Workshop on Machine learning, Optimization and big Data (MOD 2015), Taormina - Sicily, Italy
Funder
Knowledge FoundationAvailable from: 2016-01-19 Created: 2016-01-19 Last updated: 2019-04-25Bibliographically approved
In thesis
1. Tree Models for Design Space Exploration in Aerospace Engineering
Open this publication in new window or tab >>Tree Models for Design Space Exploration in Aerospace Engineering
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

A crucial issue in the design of aircraft components is the evaluation of a larger number of potential design alternatives. This evaluation involves too expensive procedures, consequently, it slows down the search for optimal design samples. As a result, scarce or small number of design samples with high dimensional parameter space and high non-linearity pose issues in learning of surrogate models. Furthermore, surrogate models have more issues in handling qualitative data (discrete) than in handling quantitative data (continuous). These issues bring the need for investigations of methods of surrogate modelling for the most effective use of available data. 

 The thesis goal is to support engineers in the early design phase of development of new aircraft engines, specifically, a component of the engine known as Turbine Rear Structure (TRS). For this, tree-based approaches are explored for surrogate modelling for the purpose of exploration of larger search spaces and for speeding up the evaluations of design alternatives. First, we have investigated the performance of tree models on the design concepts of TRS. Second, we have presented an approach to explore design space using tree models, Random Forests. This approach includes hyperparameter tuning, extraction of parameters importance and if-then rules from surrogate models for a better understanding of the design problem. With this presented approach, we have shown that the performance of tree models improved by hyperparameter tuning when using design concepts data of TRS. Third, we performed sensitivity analysis to study the thermal variations on TRS and hence support robust design using tree models. Furthermore, the performance of tree models has been evaluated on mathematical linear and non-linear functions. The results of this study have shown that tree models fit well on non-linear functions. Last, we have shown how tree models support integration of value and sustainability parameters data (quantitative and qualitative data) together with TRS design concepts data in order to assess these parameters impact on the product life cycle in the early design phase.

 

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2019. p. 149
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 8
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17852 (URN)978-91-7295-377-2 (ISBN)
Presentation
2019-06-03, 13:00 (English)
Opponent
Supervisors
Available from: 2019-04-26 Created: 2019-04-25 Last updated: 2019-06-11Bibliographically approved

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fulltext(860 kB)349 downloads
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Publisher's full texthttp://link.springer.com/chapter/10.1007/978-3-319-27926-8_11

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Dasari, Siva KrishnaLavesson, NiklasPersson, Marie

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