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학술대회 Applications of Machine Learning Algorithms to Predictive Manufacturing: Trends and Application of Tool Wear Compensation Parameter Recommendation
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저자
한지형, 김록원, 지수영
발행일
201510
출처
International Conference on Big Data Applications and Services (BigDAS) 2015, pp.1-7
DOI
https://dx.doi.org/10.1145/2837060.2837066
협약과제
14PC3900, 중소 제조산업의 4M (Man, Machine, Materiel, Method) 데이터 통합 분석을 활용한 프리틱디브 매뉴펙춰링 시스템 개발 , 지수영
초록
The manufacturing industry has become more competitive because of globalization and fast change in the industry. To survive from the global market, manufacturing enterprises should reduce the product cost and increase the productivity. The most promising way is applying the information communication technology especially machine learning algorithms to the traditional manufacturing system. This paper presents recent trends of applying machine learning techniques to manufacturing system and briey explains each kind of applications. As a representative application of machine learning algorithms to manufacturing system, a generalized tool wear compensation parameter recommendation framework using regression algorithms and preliminary results using real data gathered from local and small manufacturing are also presented.
키워드
Machine learning, Predictive manufacturing, Tool wear compensation parameter recommendation
KSP 제안 키워드
Applications of Machine Learning, Global market, Information and communication technology(ICT), Machine Learning Algorithms, Machine Learning technique(MLT), Manufacturing system, Predictive manufacturing, Product cost, Real data, Recent Trends, Regression algorithm