Hostname: page-component-7bb8b95d7b-495rp Total loading time: 0 Render date: 2024-09-15T12:57:02.107Z Has data issue: false hasContentIssue false
  • English
  • Français

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, l.manuguerra@pm.univpm.it

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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

References

Alstad, J.P. (2019), “Development of COSYSMO 3.0: An Extended, Unified Cost Estimating Model for Systems Engineering”, Procedia Computer Science, Elsevier B.V., Vol. 153, pp. 55–62, https://dx.doi.org/10.1016/j.procs.2019.05.055.CrossRefGoogle Scholar
Bertoni, A. and Bertoni, M. (2020), “PSS cost engineering: A model-based approach for concept design”, CIRP Journal of Manufacturing Science and Technology, Elsevier Ltd, Vol. 29, pp. 176–190, https://dx.doi.org/10.1016/j.cirpj.2018.08.001.CrossRefGoogle Scholar
Boothroyd, G. and Reynolds, C. (1989), “Approximate cost estimates for typical turned parts”, Journal of Manufacturing Systems, Vol. 8 No. 3, pp. 185–193, https://dx.doi.org/10.1016/0278-6125(89)90040-X.CrossRefGoogle Scholar
Campi, F., Mandolini, M., Santucci, F., Favi, C. and Germani, M. (2021), “PARAMETRIC COST MODELLING OF COMPONENTS FOR TURBOMACHINES: PRELIMINARY STUDY”, Proceedings of the Design Society, Cambridge University Press, Vol. 1, pp. 2379–2388, https://dx.doi.org/10.1017/pds.2021.499.CrossRefGoogle Scholar
Cavalieri, S., Maccarrone, P. and Pinto, R. (2004), “Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry”, International Journal of Production Economics, Vol. 91 No. 2, pp. 165–177, https://dx.doi.org/10.1016/j.ijpe.2003.08.005.CrossRefGoogle Scholar
X, CHEN., J, HUANG. and M, YI. (2021), “Development cost prediction of general aviation aircraft using combined estimation technique”, Chinese Journal of Aeronautics, Chinese Journal of Aeronautics, Vol. 34 No. 4, pp. 32–41, https://dx.doi.org/10.1016/j.cja.2020.07.024.Google Scholar
Elmousalami, H.H. (2021), “Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction: A Case Study and Comparative Analysis”, IEEE Transactions on Engineering Management, Vol. 68 No. 1, pp. 183–196, https://dx.doi.org/10.1109/TEM.2020.2972078.CrossRefGoogle Scholar
Hennebold, C., Klöpfer, K., Lettenbauer, P. and Huber, M. (2022), “Machine Learning based Cost Prediction for Product Development in Mechanical Engineering”, Procedia CIRP, Elsevier B.V., Vol. 107, pp. 264–269, https://dx.doi.org/10.1016/j.procir.2022.04.043.CrossRefGoogle Scholar
Hihn, J. and Menzies, T. (2015), “Data Mining Methods and Cost Estimation Models: Why is it So Hard to Infuse New Ideas?”, 2015 30th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW), IEEE, pp. 5–9, https://dx.doi.org/10.1109/ASEW.2015.27.CrossRefGoogle Scholar
Kadir, A.Z.A., Yusof, Y. and Wahab, M.S. (2020), “Additive manufacturing cost estimation models—a classification review”, The International Journal of Advanced Manufacturing Technology, Springer, Vol. 107 No. 9–10, pp. 4033–4053, https://dx.doi.org/10.1007/s00170-020-05262-5.CrossRefGoogle Scholar
Kamps, T., Lutter-Guenther, M., Seidel, C., Gutowski, T. and Reinhart, G. (2018), “Cost- and energy-efficient manufacture of gears by laser beam melting”, CIRP Journal of Manufacturing Science and Technology, Elsevier Ltd, Vol. 21, pp. 47–60, https://dx.doi.org/10.1016/j.cirpj.2018.01.002.CrossRefGoogle Scholar
Langmaak, S., Wiseall, S., Bru, C., Adkins, R., Scanlan, J. and Sóbester, A. (2013), “An activity-based-parametric hybrid cost model to estimate the unit cost of a novel gas turbine component”, International Journal of Production Economics, Vol. 142 No. 1, pp. 74–88, https://dx.doi.org/10.1016/j.ijpe.2012.09.020.CrossRefGoogle Scholar
Loyer, J.L., Henriques, E., Fontul, M. and Wiseall, S. (2016), “Comparison of Machine Learning methods applied to the estimation of manufacturing cost of jet engine components”, International Journal of Production Economics, Elsevier B.V., Vol. 178, pp. 109–119, https://dx.doi.org/10.1016/j.ijpe.2016.05.006.CrossRefGoogle Scholar
Masel, D.T., Dowler, J.D. and Judd, R.D. (2010), “Adapting Bottoms-up Cost Estimating Relationships to New Systems”, SPA/SCEA Joint Annual Conference and Training Workshop.Google Scholar
Niazi, A., Dai, J.S., Balabani, S. and Seneviratne, L. (2006), “Product Cost Estimation: Technique Classification and Methodology Review”, Journal of Manufacturing Science and Engineering, American Society of Mechanical Engineers(ASME), Vol. 128 No. 2, pp. 563–575, https://dx.doi.org/10.1115/1.2137750.CrossRefGoogle Scholar
Ning, F., Shi, Y., Cai, M., Xu, W. and Zhang, X. (2020a), “Manufacturing cost estimation based on the machining process and deep-learning method”, Journal of Manufacturing Systems, Elsevier B.V., Vol. 56, pp. 11–22, https://dx.doi.org/10.1016/j.jmsy.2020.04.011.CrossRefGoogle Scholar
Ning, F., Shi, Y., Cai, M., Xu, W. and Zhang, X. (2020b), “Manufacturing cost estimation based on a deep-learning method”, Journal of Manufacturing Systems, Elsevier B.V., Vol. 54, pp. 186–195, https://dx.doi.org/10.1016/j.jmsy.2019.12.005.CrossRefGoogle Scholar
Documentation, RapidMiner. (2023), “https://docs.rapidminer.com/9.3/studio/auto-model/”.Google Scholar
Seo, K.-K., Park, J.-H., Jang, D.-S. and Wallace, D. (2002), “Approximate Estimation of the Product Life Cycle Cost Using Artificial Neural Networks in Conceptual Design”, The International Journal of Advanced Manufacturing Technology, Vol. 19 No. 6, pp. 461–471, https://dx.doi.org/10.1007/s001700200049.CrossRefGoogle Scholar
Verlinden, B., Duflou, J.R., Collin, P. and Cattrysse, D. (2008), “Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study”, International Journal of Production Economics, Vol. 111 No. 2, pp. 484–492, https://dx.doi.org/10.1016/j.ijpe.2007.02.004.CrossRefGoogle Scholar
Yoo, S. and Kang, N. (2021), “Explainable artificial intelligence for manufacturing cost estimation and machining feature visualization”, Expert Systems with Applications, Elsevier Ltd, Vol. 183, https://dx.doi.org/10.1016/j.eswa.2021.115430.Google Scholar