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  • 1.
    Bertoni, Alessandro
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering.
    Dasari, Siva Krishna
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Hallstedt, Sophie
    Blekinge Institute of Technology, Faculty of Engineering, Department of Strategic Sustainable Development.
    Petter, Andersson
    GKN Aerospace Systems , SWE.
    Model-based decision support for value and sustainability assessment: Applying machine learning in aerospace product development2018In: DS92: Proceedings of the DESIGN 2018 15th International Design Conference / [ed] Marjanović D., Štorga M., Škec S., Bojčetić N., Pavković N, The Design Society, 2018, Vol. 6, p. 2585-2596Conference paper (Refereed)
    Abstract [en]

    This paper presents a prescriptive approach toward the integration of value and sustainability models in an automated decision support environment enabled by machine learning (ML). The approach allows the concurrent multidimensional analysis of design cases complementing mechanical simulation results with value and sustainability assessment. ML allows to deal with both qualitative and quantitative data and to create surrogate models for quicker design space exploration. The approach has been developed and preliminary implemented in collaboration with a major aerospace sub-system manufacturer.

  • 2.
    Bertoni, Alessandro
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. Blekinge Institute of Technology.
    Hallstedt, Sophie
    Blekinge Institute of Technology, Faculty of Engineering, Department of Strategic Sustainable Development. Blekinge Institute of Technology.
    Dasari, Siva Krishna
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Blekinge Institute of Technology.
    Andersson, Petter
    GKN Aerospace Engine Systems, SWE.
    Integration of Value and Sustainability Assessment in Design Space Exploration by Machine Learning: An Aerospace Application2019In: Design ScienceArticle in journal (Refereed)
  • 3.
    Dasari, Siva Krishna
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Blekinge Institute of Technology.
    Tree Models for Design Space Exploration in Aerospace Engineering2019Licentiate 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.

     

  • 4.
    Dasari, Siva Krishna
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Blekinge Institute of Technology.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Blekinge Institute of Technology.
    Andersson, Petter
    GKN Aerospace Engine Systems, SWE.
    Predictive Modelling to Support Sensitivity Analysis for Robust Design in Aerospace EngineeringIn: Article in journal (Refereed)
  • 5.
    Dasari, Siva Krishna
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Andersson, Petter
    GKN Aerospace Engine Systems, SWE.
    Random Forest Surrogate Models to Support Design Space Exploration in Aerospace Use-case2019In: IFIP Advances in Information and Communication Technology, Springer-Verlag New York, 2019, Vol. 559Conference paper (Refereed)
    Abstract [en]

    In engineering, design analyses of complex products rely on computer simulated experiments. However, high-fidelity simulations can take significant time to compute. It is impractical to explore design space by only conducting simulations because of time constraints. Hence, surrogate modelling is used to approximate the original simulations. Since simulations are expensive to conduct, generally, the sample size is limited in aerospace engineering applications. This limited sample size, and also non-linearity and high dimensionality of data make it difficult to generate accurate and robust surrogate models. The aim of this paper is to explore the applicability of Random Forests (RF) to construct surrogate models to support design space exploration. RF generates meta-models or ensembles of decision trees, and it is capable of fitting highly non-linear data given quite small samples. To investigate the applicability of RF, this paper presents an approach to construct surrogate models using RF. This approach includes hyperparameter tuning to improve the performance of the RF's model, to extract design parameters' importance and \textit{if-then} rules from the RF's models for better understanding of design space. To demonstrate the approach using RF, quantitative experiments are conducted with datasets of Turbine Rear Structure use-case from an aerospace industry and results are presented.

  • 6.
    Dasari, Siva Krishna
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Andersson, Petter
    Engineering Method Development, GKN Aerospace Engine Systems Sweden.
    Persson, Marie
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Tree-Based Response Surface Analysis2015Conference 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.

1 - 6 of 6
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