.png){kind=icon}
返回
Manufacturing cost estimation based on a deep-learning method
.png){kind=icon}
OA
AI
DOI：10.1016/j.jmsy.2019.12.005.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Manufacturing
Price quotation
Cost estimation
Deep learning
CNN
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
14.2
论文数:
2.7K
被引数:
1.6W
机构
引用论文
.png){kind=icon}
分享收藏Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks从没有面部标志的单个图像对真实年龄和表观年龄的深刻期望
PREAI
分享收藏
查看更多内容