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MACHINE LEARNING FOR PARAMETRIC COST ESTIMATION OF AXISYMMETRIC COMPONENTS

Published online by Cambridge University Press:  19 June 2023

Luca Manuguerra ,
Marco Mandolini ,
Michele Germani  and
Mikhailo Sartini
Luca Manuguerra*
Affiliation:
UNIVPM Università Politecnica delle Marche
Marco Mandolini
Affiliation:
UNIVPM Università Politecnica delle Marche
Michele Germani
Affiliation:
UNIVPM Università Politecnica delle Marche
Mikhailo Sartini
Affiliation:
UNIVPM Università Politecnica delle Marche
*
Manuguerra, Luca, UNIVPM Università Politecnica delle Marche, Italy, [email protected]

Abstract

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Machine learning (ML) is a well-established research topic in Industry 4.0 is boosting its adoption. ML is also used for manufacturing cost estimation during design. Such approaches are commonly used to estimate the cost of mass-produced parts. Many consolidated historical data are available for training the regression models. Unfortunately, very often, such a database of data is not available.

The paper defines an ML approach for parametric cost estimation of axisymmetric components. The data for training the ML model derives from automatic software for analytically estimating the manufacturing cost. With a proper set of simulations, the tool can generate a large amount of data for training. The paper presents the steps for developing a parametric cost model using ML. The approach is based on CRoss Industry Standard Process for Data Mining method. The proposed method was used to develop one cost model (to estimate the total cost that considered raw material and manufacturing cost). The obtained Relative Error is 23.52% ± 1.37%, coherent with E2516 − 11, Standard Classification for Cost Estimate Classification System.

Keywords

Design costingMachine learningConceptual designBig data
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2485 - 2494
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), 2023. Published by Cambridge University Press

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