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Conference Paper
Consideration of manufacturing data to apply machine learning methods for predictive manufacturing
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- Authors
- Ji-Hyeong Han, Su-Young Chi
- Issue Date
- 2016-07
- Citation
- International Conference on Ubiquitous and Future Networks (ICUFN) 2016, pp.109-113
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- According to the recent development of internet of things and big data, the serious tries of implementing smart factory have been increased. To realize the smart factory, firstly predictive manufacturing system should be implemented. As a first step of predictive manufacturing, this paper focuses on solving the simple but time consuming and high cost task in the predictive manner. The target problem of this paper is predicting CNC tool wear compensation offset using machine learning methods based on the data. To apply machine learning methods, we should understand the characteristics of the data and find the most suitable method according to the data characteristics. Thus, this paper discusses the characteristics of manufacturing data and compares various cases of applying machine learning methods.
- KSP Keywords
- Big-data, Data characteristics, Machine Learning Methods, Manufacturing data, Manufacturing system, Predictive manufacturing, Smart Factory, Target problem, Tool wear compensation, internet of things(IoT)