Hostname: page-component-7bb8b95d7b-fmk2r Total loading time: 0 Render date: 2024-09-16T13:48:07.284Z Has data issue: false hasContentIssue false
  • English
  • Français

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

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.

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

References

Abdelali, Z., Mustapha, H., Abdelwahed, N. (2019) “Investigating the use of random forest in software effort estimation”, Procedia Computer Science, Vol. 148, pp. 343–352. https://doi.org/10.1016/j.procs.2019.01.042CrossRefGoogle Scholar
Arundacahawat, A., Roy, R., Al-Ashaab, A. (2013), “An analogy based estimation framework for design rework efforts” Journal of Intelligent Manufacturing, Vol 24, pp. 625–639. https://doi.org/10.1007/s10845-011-0605-6Google Scholar
Bertoni, A., Bertoni, M. (2018), “PSS cost engineering: A model-based approach for concept design”, CIRP Journal of Manufacturing Science and Technology, Vol. 29, Part B, pp. 176–190. https://doi.org/10.1016/j.cirpj.2018.08.001CrossRefGoogle Scholar
Bertoni, A., Hallstedt, S. I., Dasari, S. K., Andersson, P. (2020), “Integration of value and sustainability assessment in design space exploration by machine learning: an aerospace application”, Design Science, Vol. 6. https://doi.org/10.1017/dsj.2019.29CrossRefGoogle Scholar
Bilal, M., Oyedele, L.O. (2020), “Guidelines for applied machine learning in construction industry—A case of profit margins estimation”, Advanced Engineering Informatics, Vol. 43, p. 101013. https://doi.org/10.1016/j.aei.2019.101013CrossRefGoogle Scholar
Boothroyd, G., Dewhurst, P., Knight, W.A. (2011), Product Design For Manufacture and Assembly 3rd Edition, CRC Press.Google Scholar
Breiman, L. (2001), “Random forests”, Machine Learning, Vol. 45, pp. 5–32. https://doi.org/10.1023/A:1010933404324CrossRefGoogle Scholar
Cavalieri, S., Maccarrone, P., 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://doi.org/10.1016/j.ijpe.2003.08.005CrossRefGoogle Scholar
Chen, X., Huang, J., Yi, M. (2020), “Cost estimation for general aviation aircrafts using regression models and variable importance in projection analysis”, Journal of Cleaner Production, Vol. 256, pp. 120648. https://doi.org/10.1016/j.jclepro.2020.120648CrossRefGoogle Scholar
Chou, J.S., Tai, Y., Chang, L.J. (2010), “Predicting the development cost of TFT-LCD manufacturing equipment with artificial intelligence models”. International Journal of Production Economics, Vol. 128 No.1, pp. 339–350. https://doi.org/10.1016/j.ijpe.2010.07.031CrossRefGoogle Scholar
Cook, R.D., Weisberg, S., (1982), Residuals and Influence in Regression, New York: Chapman and HallGoogle Scholar
Dogan, A., Birant, D., (2021), “Machine learning and data mining in manufacturing”, Expert Systems With Applications, Vol. 166, p. 114060. https://doi.org/10.1016/j.eswa.2020.114060CrossRefGoogle Scholar
Duran, O., Maciel, J., Rodriguez, N. (2012), “Comparisons between two types of neural networks for manufacturing cost estimation of piping elements”, Expert Systems with Applications, Vol. 39 No. 9, pp. 7788–7795. https://doi.org/10.1016/j.eswa.2012.01.095CrossRefGoogle Scholar
Elmousalami, H.H. (2019), “Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction”, Computer Science, Mathematics, Engineering, Vol. abs/1909.11637. https://doi.org/10.1109/tem.2020.2972078CrossRefGoogle Scholar
Favi, C., Germani, M., Mandolini, M., (2016), “Design for Manufacturing and Assembly vs. Design to Cost: Toward a Multi-objective Approach for Decision-making Strategies During Conceptual Design of Complex Products”, Procedia CIRP, Vol. 50, pp. 275–280. https://doi.org/10.1016/j.procir.2016.04.190.CrossRefGoogle Scholar
Green, S.B., (1991), “How many subjects does it take to do a regression analysis?” Multivariate behavioral research. Vol. 26 No. 3, pp. 499–510. https://doi.org/10.1207/s15327906mbr2603_7CrossRefGoogle Scholar
Heiat, A. (2002), “Comparison of artificial neural network and regression models for estimating software development effort”, Information and Software Technology, Vol. 44 No. 15, pp. 911–922. https://doi.org/10.1016/S0950-5849(02)00128-3CrossRefGoogle Scholar
Hihn, J., Menzies, T. (2015), “ Data Mining Methods and Cost Estimation Models. Why is it so hard to infuse new ideas?”, 30th IEEE/ACM International Conference on Automated Software Engineering Workshop, pp. 5–9, https://doi.org/10.1109/ASEW.2015.27CrossRefGoogle Scholar
Isıklı, E., Aydın, N., Bilgili, L., Toprak, A. (2020), “Estimating fuel consumption in maritime transport”, Journal of Cleaner Production, Vol. 275, p. 124142. https://doi.org/10.1016/j.jclepro.2020.124142CrossRefGoogle Scholar
Langmaak, S., Wiseall, S., Bru, C., Adkins, R., Scanlan, J., Sobester, A. (2012), “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://doi.org/10.1016/j.ijpe.2012.09.020CrossRefGoogle Scholar
Li, Y., Zou, C., Berecibar, M., Nanini-Maury, E., Chan, J.C. W., van den Bossche, P., Van Mierlo, J., Omar, N. (2018), “Random forest regression for online capacity estimation of lithium-ion batteries”, Applied Energy, Vol. 32, pp. 197–210. https://doi.org/10.1016/j.apenergy.2018.09.182Google Scholar
Loyer, J.A., Henriques, H., Fontul, M., Wiseall, S. (2016), “Comparison of Machine Learning methods applied to the estimation of manufacturing cost of jet engine components”. International Journal of Production Economics, Vol. 178, pp. 109–119. https://doi.org/10.1016/j.ijpe.2016.05.006CrossRefGoogle Scholar
Mandolini, M., Campi, F., Favi, C., Germani, M., Raffaeli, R. (2020), “A framework for analytical cost estimation of mechanical components based on manufacturing knowledge representation” International Journal of Advanced Manufacturing Technology, Vol. 107(3-4), pp. 1131–1151.10.1007/s00170-020-05068-5CrossRefGoogle Scholar
Martinelli, I., Campi, F., Checcacci, E., Lo Presti, G.M., Pescatori, F., Pumo, A., Germani, M., (2019), “Cost Estimation Method for Gas Turbine in Conceptual Design Phase”, Procedia CIRP, Vol. 84, pp. 650–655. https://doi.org/10.1016/j.procir.2019.04.311CrossRefGoogle Scholar
Masel, D.T., Dowler, J.D., Judd, R.D (2010), “Adapting Bottoms-up Cost Estimating Relationships to New Systems”, ISPA/SCEA Joint Annual Conference and Training Workshop.Google Scholar
Niazi, A, Dai, J.S., Balabani, S., Seneviratne, L. (2005), “Product cost estimation: technique classification and methodology review”, Journal of Manufacturing Science and Engineering, Vol. 128 No. 2, pp. 563–575. https://doi.org/10.1115/1.2137750CrossRefGoogle Scholar
Ning, F., Shi, Y., Cai, M., Xu, W., Zhang, X. (2020), “Manufacturing cost estimation based on the machining process and deep learning method”, Journal of Manufacturing Systems, Vol. 56, pp. 11–22. https://doi.org/10.1016/j.jmsy.2020.04.011CrossRefGoogle Scholar
Özcan, B., Fığlalı, A. (2014), “Artificial neural networks for the cost estimation of stamping dies”, Neural Computing and Applications, Vol. 25, pp.717–726. https://doi.org/10.1007/s00521-014-1546-8CrossRefGoogle Scholar
Rockwell, R.C., (1975), “Assessment of multicollinearity: The Haitovsky test of the determinant.” Sociological Methods & Research, Vol. 3 No. 3, pp. 308–320. https://doi.org/10.1177/004912417500300304.CrossRefGoogle Scholar
Sala, R., Zambetti, M., Pirola, F., Pinto, R. (2018), “How to select a suitable machine learning algorithm: A feature-based, scope-oriented selection framework”, 23rd Summer School “Francesco Turco”-Industrial Systems Engineering 2018, Vol. 2018, pp. 87–93.Google Scholar
Tan, H., Wang, H., Chen, L., Shi, F. (2011), “Dummy Variable Model Analysis With Law Factors on Safety Production in Chinese Coal Mine Industry”, Procedia Engineering, Vol. 26, pp. 2383–2390. https://doi.org/10.1016/j.proeng.2011.11.2449Google Scholar
Ulrich, K., Eppinger, S.A. (2011), Product Design and Development, McGraw-Hill Education, New York.Google Scholar
Verlinden, B., Duflou, J.R., Collin, P., Cattrysse, D. (2007), “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://doi.org/10.1016/j.ijpe.2007.02.004CrossRefGoogle Scholar
Wang, H.S., Wang, Y.N., Wang, Y.C. (2013), “Cost estimation of plastic injection molding parts through integration of PSO and BP neural network”, Expert Systems with Applications, Vol. 40 No. 2, pp. 418–428. https://doi.org/10.1016/j.eswa.2012.01.166CrossRefGoogle Scholar
Weichert, D., Link, P., Stoll, A., Ruping, S., Ihlenfeldt, S., Wrobel, S. (2019), “A review of machine learning for the optimization of production processes”, The International Journal of Advanced Manufacturing Technology, Vol. 104, pp. 1889–1902. https://doi.org/10.1007/s00170-019-03988-5CrossRefGoogle Scholar