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2024 | OriginalPaper | Buchkapitel

Machine Learning for Costing Gas-Turbine Components

verfasst von : Marco Mandolini, Luca Manuguerra, Mikhailo Sartini, Francesco Pescatori, Giulio Marcello Lo Presti, Michele Germani

Erschienen in: Design Tools and Methods in Industrial Engineering III

Verlag: Springer Nature Switzerland

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Abstract

Machine Learning (ML), part of Artificial Intelligence, is one of the enabling technologies of Industry 4.0. ML appears to be an effective, affordable, accurate and scalable technique to cost mechanical parts in the early stage of the design process. Despite the cost estimation methods proposed in the literature, their application in specific real industrial contexts (e.g., engineered-to-order products) is minimal.

This paper presents an innovative method for developing ML-based parametric cost models. The training data set is generated thanks to an analytical and automatic software tool for cost estimation. The data is subsequently processed using the Cross Industry Standard Process for Data Mining – CRISP-DM method. CRISP-DM is a process model for data science and representation. It provides an overview of the data mining life cycle. Its flexibility and easy customisation allow the creation of a data mining model that fits the goal of this work.

The proposed method was employed to develop two cost models (semi-finishing and finishing phases) for components (disks) of a gas turbine. Gradient Boosted Trees turned out to be the best-performing prediction algorithm. Design engineers successfully used the generated cost models while configuring the gas-turbine cross-section.

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Bertoni, A., Hallstedt, S.I., Dasari, S.K., Andersson, P.: Integration of value and sustainability assessment in design space exploration by machine learning: an aerospace application. Design Science. 6, e2 (2020). https://doi.org/10.1017/dsj.2019.29CrossRef

Verlinden, B., Duflou, J.R., Collin, P., Cattrysse, D.: Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study. Int. J. Prod. Econ. 111, 484–492 (2008). https://doi.org/10.1016/j.ijpe.2007.02.004CrossRef

Ning, F., Shi, Y., Cai, M., Xu, W., Zhang, X.: Manufacturing cost estimation based on the machining process and deep-learning method. J. Manuf. Syst. 56, 11–22 (2020). https://doi.org/10.1016/j.jmsy.2020.04.011CrossRef

Chen, X., Huang, J., Yi, M.: Development cost prediction of general aviation aircraft using combined estimation technique. Chin. J. Aeronaut. 34, 32–41 (2021). https://doi.org/10.1016/j.cja.2020.07.024CrossRef

Campi, F., Mandolini, M., Santucci, F., Favi, C., Germani, M.: Parametric cost modelling of components for turbomachines: preliminary study. Proc. Design Soc. 1, 2379–2388 (2021). https://doi.org/10.1017/pds.2021.499CrossRef

Loyer, J.-L., Henriques, E., Fontul, M., Wiseall, S.: Comparison of machine learning methods applied to the estimation of manufacturing cost of jet engine components. Int. J. Prod. Econ. 178, 109–119 (2016). https://doi.org/10.1016/j.ijpe.2016.05.006CrossRef

Yoo, S., Kang, N.: Explainable artificial intelligence for manufacturing cost estimation and machining feature visualization. Expert Syst. Appl. 183, 115430 (2021). https://doi.org/10.1016/j.eswa.2021.115430CrossRef

Ning, F., Shi, Y., Cai, M., Xu, W., Zhang, X.: Manufacturing cost estimation based on a deep-learning method. J. Manuf. Syst. 54, 186–195 (2020). https://doi.org/10.1016/j.jmsy.2019.12.005CrossRef

Seo, K.-K., Park, J.-H., Jang, D.-S., Wallace, D.: Approximate Estimation of the Product Life Cycle Cost Using Artificial Neural Networks in Conceptual Design. Inter. J. Adv. Manufact. Technol. 19, 461–471. https://doi.org/10.1007/s001700200049

10.

EasyKost; AI Based Product Cost Management Software. https://www.easykost.com/, (Accessed 23 May 2023)

11.

Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507–2517 (2007). https://doi.org/10.1093/bioinformatics/btm344CrossRef

Titel: Machine Learning for Costing Gas-Turbine Components
verfasst von: Marco Mandolini
Luca Manuguerra
Mikhailo Sartini
Francesco Pescatori
Giulio Marcello Lo Presti
Michele Germani
Verlag: Springer Nature Switzerland
Buch: Design Tools and Methods in Industrial Engineering III
Print ISBN: 978-3-031-58093-2

Electronic ISBN: 978-3-031-58094-9

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-58094-9_8

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