상세정보
학술대회
Consideration of Manufacturing Data to Apply Machine Learning Methods for Predictive
- 발행일
- 201607
- 출처
- International Conference on Ubiquitous and Future Networks (ICUFN) 2016, pp.109-113
- 협약과제
- 15PC5200, 중소 제조산업의 4M (Man, Machine, Materiel, Method) 데이터 통합 분석을 활용한 프리틱디브 매뉴펙춰링 시스템 개발 , 지수영
- 초록
- 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 제안 키워드
- Big Data, Data characteristics, Internet of thing(IoT), Machine Learning Methods, Manufacturing data, Manufacturing system, Predictive manufacturing, Smart Factory, Target problem, Tool wear compensation