Research on Assembly Line Optimization Based on Machine Learning

DOI

10.2991/pntim-19.2019.37How to use a DOI?

Keywords

K-means Algorithm; Text processing; job standardization; Non-value-added Operations

Abstract

How to conduct a wide range of problem analysis on production line work in a short time and standardize, its work has become a major difficulty for enterprises to improve work efficiency. This paper takes M company's Rail car assembly as an example to conduct the job optimization. It uses the k-means algorithm in machine learning to conduct clustering analysis on job data，Identify common and unusual factors in the assignment. Establishing different text dictionaries aims to normalize the expression of texts and help identify as well as remove non - value-added work efficiently. Application of machine learning makes it easy for management personnel to identify the operational bottlenecks of the entire production line, achieve standardized operations, and improve production efficiency.

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  - Zhang Peng
AU  - Fang Yadong
AU  - Liang Xiaowei
AU  - Chen Jingwen
PY  - 2019/11
DA  - 2019/11
TI  - Research on Assembly Line Optimization Based on Machine Learning
BT  - Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturing (PNTIM 2019)
PB  - Atlantis Press
SP  - 180
EP  - 183
SN  - 2589-4943
UR  - https://doi.org/10.2991/pntim-19.2019.37
DO  - 10.2991/pntim-19.2019.37
ID  - Peng2019/11
ER  -