Open AccessK-means clustering on quality of radial run out tires
Author(s) -
Bambang Biantoro,
- Hernadewita
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1034/1/012122
Subject(s) - cluster analysis , quality (philosophy) , radial tire , product (mathematics) , computer science , tread , data mining , engineering , artificial intelligence , mathematics , materials science , natural rubber , physics , composite material , geometry , quantum mechanics
The tire industry entered the era of industrial revolution (IR) 4.0. The process of tire production with electronic transactions produces big data that can be exploited in solving quality problems. Stratification techniques commonly used in big data conditions are less effective in mapping product quality conditions. Data handling and subjectivity in product quality classification are inhibitory factors in exploiting data. K means easy grouping of calculations and implementation can be used to map product quality. Radial runs out tire quality using K-mean clustering and elbow method validation shows that the optimum conditions for clustering the radial quality of exhaust tires are three groups. Each group is related to the construction of tires, i.e. radial outlets located at tread splices, sidewall splices and BEC splices. The priority to improve the quality of radial depletion can be suggested through quality mapping based on the results of K-mean cluster analysis, especially on BEC components. The results of the repairs show that the radial value of running out of tires decreased by 12.5%.