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Parametric cost estimation models of civil aircraft for the preliminary aircraft design phase

Published online by Cambridge University Press:  20 May 2022

O. Al-Shamma*
Affiliation:
University of Information Technology and Communications, Baghdad, Iraq
R. Ali
Affiliation:
Coventry University, Coventry, UK
*
*Corresponding author. Email: o.al_shamma@uoitc.edu.iq

Abstract

In the preliminary design phase of aircraft design, estimating the production cost accurately is a challenging task. At this stage, many design parameters that affect the overall cost are still undefined. This paper establishes cost-estimation models for civil, commercial aircraft using a parametric cost analysis (PCA) approach. Aircraft are characterised based on their size, ranging from a wide body to executive jets, into four categories. Key design parameters, such as maximum take-off weight, number of passengers, range, wing area, span, fuselage length, to name a few, are likely to be available in the preliminary design stage and significantly impact the aircraft design. These variables either directly or indirectly affect the overall production cost or performance. The PCA approach includes both correlation and multiple linear regression techniques. The empirical models thus developed were able to predict the aircraft cost with an error of less than ±4% for all aircraft categories considered. Two aircraft in each defined category were not part of the PCA models and were used to verify the models. The proposed models provide the ability to estimate the aircraft cost quickly in the early stages of the preliminary design phase and provide the possibility of performing parametric studies involving the key variables to determine the cost sensitivity to the main design parameters.

Type
Research Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Chen, X., Huang, J., Yi, M. and Pan, Y. Prediction of the development cost of general aviation aircraft, Aircraft Eng. Aerospace Technol., 2019, 91, (4), pp 567574. DOI: 10.1108/AEAT-09-2018-0248 CrossRefGoogle Scholar
Xie, N.-m., Yin, S.-M. and Hu, C.-Z. Estimating a civil aircraft’s development cost with a GM (1, N) model and an MLP neural network, Grey Syst. Theory Appl., 2017, 7, (1), pp 218. DOI: 10.1108/GS-11-2016-0049 CrossRefGoogle Scholar
Asiedu, Y. and Gu, P. Product life cycle cost analysis: State of the art review, Int. J. Prod. Res., 1998, 36, (4), pp 883908. DOI: 10.1080/002075498193444 CrossRefGoogle Scholar
Tirovolis, N.L. and Serghides, V.C. Unit cost estimation methodology for commercial aircraft, J. Aircraft, 2005, 42, (6), pp 13771386. DOI: 10.2514/1.12491 CrossRefGoogle Scholar
Wu, H., Liu, Y., Ding, Y. and Liu, J. Methods to reduce direct maintenance costs for commercial aircraft, Aircraft Eng. Aerospace Technol., 2004, 76, (1), pp 1518. DOI: 10.1108/00022660410514964 CrossRefGoogle Scholar
Ross, T.E. and Crossley, W.A. Method to assess commercial aircraft technologies, J. Aircraft, 2000, 37, (37), pp 570579. DOI: 10.2514/2.2668 CrossRefGoogle Scholar
Castagne, S., Curran, R., Rothwell, A., Price, M., Benard, E. and Raghunathan, S. A generic tool for cost estimating in aircraft design, Res. Eng. Des., 2008, 18, (4), pp 149162. DOI: 10.1007/s00163-007-0042-x CrossRefGoogle Scholar
Feng, S. and Song, E. A manufacturing process information model for design and process planning integration, J. Manuf. Syst., 2003, 22, (1), pp 115. DOI: 10.1016/S0278-6125(03)90001-X CrossRefGoogle Scholar
Dean, E.B. Parametric cost deployment, Proceedings of the 7th symposium on quality function deployment, MI, USA, November 1995, pp 2734.Google Scholar
Roy, R., Bendall, D., Taylor, J., Jones, P., Madariaga, A., Crossland, J., Hamel, J. and Taylor, I. Development of airframe engineering CER’s for aerostructures, Proceedings of the Second World Manufacturing Congress (WMC’99), Durham (UK), 1999, pp 838844. DOI: 10.1007/978-1-4612-2936-0_12 CrossRefGoogle Scholar
Dunk, A.S. Product life cycle cost analysis: The impact of customer profiling, competitive advantage, and quality of IS information, Manag. Account. Res., 2004, 15, (4), pp 401414. DOI: 10.1016/j.mar.2004.04.001 CrossRefGoogle Scholar
Cai, W. and Fang, W. Combination forecasting method for the development cost of aircraft, Syst. Eng. Electron., 2014, 36, (8), pp 15731579. DOI: 10.3969/j.issn.1001-506X.2014.08.20 Google Scholar
Ambrule, V.R. and Bhirud, A.N. Use of artificial neural network for pre-design cost estimation of building projects, Int. J. Recent Innovation Trends Comput. Commun., 2017, 5, (2), pp 173176. DOI: 10.6106/jcepm.2014.4.4.009 Google Scholar
Mahalakshmi, G. and Rajasekaran, C. Early cost estimation of highway projects in India using artificial neural network, in Sustainable Construction and Building Materials, Springer, 2019, Singapore, vol. 25, pp 659672. DOI: 10.1007/978-981-13-3317-0_59 CrossRefGoogle Scholar
Chen, X., Huang, J. and Yi, M. Development cost prediction of general aviation aircraft projects with parametric modelling, Chin. J. Aeronaut., 2019, 32, (6), pp 14651471. DOI: 10.1016/j.cja.2019.03.024 CrossRefGoogle Scholar
Raymer, D. Aircraft Design: A Conceptual Approach, 6th ed, AIAA, 2018, Reston, USA. DOI: 10.2514/4.104909 CrossRefGoogle Scholar
Kundu, A.K., Price, M.A. and Riordan, D. Conceptual Aircraft Design: An Industrial Approach, 1st ed, John Wiley & Sons, 2019, Hoboken, NJ.Google Scholar
Chen, X., Yi, M. and Huang, J. Application of a PCA-ANN based cost prediction model for general aviation aircraft, IEEE Access, 2020, 8, pp 130124130135. DOI: 10.1109/ACCESS.2020.3008442 CrossRefGoogle Scholar
Al-Shamma, O. Development of interactive aircraft design software for use in problem-based learning. PhD dissertation, University of Hertfordshire, UK, 2013. DOI: 10.18745/th.12108 CrossRefGoogle Scholar