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PARAMETRIC COST MODELLING OF COMPONENTS FOR TURBOMACHINES: PRELIMINARY STUDY

Published online by Cambridge University Press:  27 July 2021

Federico Campi ,
Marco Mandolini ,
Federica Santucci ,
Claudio Favi  and
Michele Germani
Federico Campi*
Affiliation:
Università Politecnica delle Marche;
Marco Mandolini
Affiliation:
Università Politecnica delle Marche;
Federica Santucci
Affiliation:
Università Politecnica delle Marche;
Claudio Favi
Affiliation:
Università degli studi di Parma
Michele Germani
Affiliation:
Università Politecnica delle Marche;
*
Campi, Federico, Università Politecnica delle Marche, DIISM, Italy, [email protected]

Abstract

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The ever-increasing competitiveness, due to the market globalisation, has forced the industries to modify their design and production strategies. Hence, it is crucial to estimate and optimise costs as early as possible since any following changes will negatively impact the redesign effort and lead time.

This paper aims to compare different parametric cost estimation methods that can be used for analysing mechanical components. The current work presents a cost estimation methodology which uses non-historical data for the database population. The database is settled using should cost data obtained from analytical cost models implemented in a cost estimation software. Then, the paper compares different parametric cost modelling techniques (artificial neural networks, deep learning, random forest and linear regression) to define the best one for industrial components.

Such methods have been tested on 9 axial compressor discs, different in dimensions. Then, by considering other materials and batch sizes, it was possible to reach a training dataset of 90 records. From the analysis carried out in this work, it is possible to conclude that the machine learning techniques are a valid alternative to the traditional linear regression ones.

Keywords

Design costingMachine learningEarly design phasesConceptual design
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 2379 - 2388
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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