Research on Assembly Line Optimization Based on Machine Learning

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