Forecast of Production Quantity of General-Purpose Parts Based on Customized Production

DOI

10.2991/mmsta-19.2019.24How to use a DOI?

Keywords

association rules; FP-growth; equality regression;clustering; K-means

Abstract

With the development of the Internet, informatization has penetrated into all walks of life. The general-purpose parts manufacturing enterprise is no exception, and its production mode is also undergoing tremendous changes. The main performance is the development of customized production to the network. The production line of the enterprise has shifted from large-scale mass production to small-volume customized production. In this transformation process, a large amount of product quality related data is generated. Traditional quality data analysis methods have been difficult to unearth the full value of data. Therefore, this paper applies data mining technology to the quality-related data of general-purpose parts manufacturing enterprises. Through the prediction algorithm, it can accurately estimate the quantity of materials before production, which can reduce the inventory of enterprises or avoid secondary production. Due to the characteristics of the manufacturing process of the common parts, there are many reasons for the defects that may occur in the production, and even the same product, the total number of defects and the specific defects that are generated by different production batches are not the same. In the face of such a scenario, the usual prediction algorithm cannot be directly applied, because there is no feature vector as the algorithm input, so this paper proposes a comprehensive prediction algorithm based on regression-association-cluster to solve this problem. The algorithm only needs the product coding and the number of materials to be fed, so it can predict the number of scraps of the production task through historical data, and combine the production line experience to improve the accuracy of the prediction. Experiments show that the prediction effect of the algorithm is in line with expectations. The research work of this paper is supported by Sichuan Science & Technology Program under Grant No. 2018GZ0118.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Honglin Wang
AU  - Xiaohong Li
AU  - Yongfeng Xi
AU  - Tao Hong
PY  - 2019/12
DA  - 2019/12
TI  - Forecast of Production Quantity of General-Purpose Parts Based on Customized Production
BT  - Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019)
PB  - Atlantis Press
SP  - 114
EP  - 119
SN  - 2352-538X
UR  - https://doi.org/10.2991/mmsta-19.2019.24
DO  - 10.2991/mmsta-19.2019.24
ID  - Wang2019/12
ER  -