Manufacturing cost estimation based on a deep-learning method

Ning, Fangwei, Shi, Yan, Cai, Maolin, Xu, Weiqing and Zhang, Xianzhi (2020) Manufacturing cost estimation based on a deep-learning method. Journal of Manufacturing Systems, 54, pp. 186-195. ISSN (print) 0278-6125

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Abstract

In the era of the mass customisation, rapid and accurate estimation of the manufacturing cost of different parts can improve the competitiveness of a product. Owing to the ever-changing functions, complex structure, and unusual complex processing links of the parts, the regression-model cost estimation method has difficulty establishing a complex mapping relationship in manufacturing. As a newly emerging technology, deep-learning methods have the ability to learn complex mapping relationships and high-level data features from a large number of data automatically. In this paper, two-dimensional (2D) and three-dimensional (3D) convolutional neural network (CNN) training images and voxel data methods for a cost estimation of a manufacturing process are proposed. Furthermore, the effects of different voxel resolutions, fine-tuning methods, and data volumes of the training CNN are investigated. It was found that compared to 2D CNN, 3D CNN exhibits excellent performance regarding the regression problem of a cost estimation and achieves a high application value.

Item Type: Article
Uncontrolled Keywords: manufacturing; price quotation; cost estimation; deep learning; CNN
Research Area: Computer science and informatics
Mechanical, aeronautical and manufacturing engineering
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing
Faculty of Science, Engineering and Computing > School of Engineering
Depositing User: Philip Keates
Date Deposited: 15 Jan 2020 15:17
Last Modified: 04 Feb 2020 16:00
DOI: https://doi.org/10.1016/j.jmsy.2019.12.005
URI: http://eprints.kingston.ac.uk/id/eprint/44767

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