Training data for parts quotation

Training data for parts quotation

Abstract: 

To obtain the prices of parts from the manufacturing characteristics and other manufacturing processes, feature quantity expression is innovatively applied. By identifying manufacturing features and calculating the feature quantities, the feature quantities are described in the form of assignments as data. To obtain the prices of parts intelligently, the most widely used and mature deep-learning method is adopted to realize the accurate quotation of parts

Instructions: 

The deep-learning method was used to realize the quotation of parts. A CNN was used to learn a large amount of CSV data with characteristic quantities

Dataset Files

No Data files have been uploaded.

Embed this dataset on another website

Copy and paste the HTML code below to embed your dataset:

Share via email or social media

Click the buttons below:

facebooktwittermailshare
[1] Fangwei Ning, "Training data for parts quotation", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/1tq7-y065. Accessed: Jun. 20, 2019.
@data{1tq7-y065-19,
doi = {10.21227/1tq7-y065},
url = {http://dx.doi.org/10.21227/1tq7-y065},
author = {Fangwei Ning },
publisher = {IEEE Dataport},
title = {Training data for parts quotation},
year = {2019} }
TY - DATA
T1 - Training data for parts quotation
AU - Fangwei Ning
PY - 2019
PB - IEEE Dataport
UR - 10.21227/1tq7-y065
ER -
Fangwei Ning. (2019). Training data for parts quotation. IEEE Dataport. http://dx.doi.org/10.21227/1tq7-y065
Fangwei Ning, 2019. Training data for parts quotation. Available at: http://dx.doi.org/10.21227/1tq7-y065.
Fangwei Ning. (2019). "Training data for parts quotation." Web.
1. Fangwei Ning. Training data for parts quotation [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/1tq7-y065
Fangwei Ning. "Training data for parts quotation." doi: 10.21227/1tq7-y065