Manufacturing cost estimation based on a deep-learning method

Fangwei Ning, Yan Shi, Maolin Cai, Weiqing Xu, Xianzhi Zhang

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)186-195
Number of pages10
JournalJournal of Manufacturing Systems
Volume54
Early online date24 Dec 2019
DOIs
Publication statusPublished - 1 Jan 2020
Externally publishedYes

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