A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

Wu, Dazhong; Jennings, Connor; Terpenny, Janis; Gao, Robert X.; Kumara, Soundar

doi:10.1115/1.4036350

articleJournal of Manufacturing Science and EngineeringMar 28, 2017BRONZE OA

A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

DWDazhong Wu CJConnor Jennings JTJanis Terpenny RXRobert X. Gao SKSoundar Kumara

Pennsylvania State University · U.S. National Science Foundation · +3 more institutions

Indexed incrossref

Abstract

Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closed-form mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with…

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Topics

Keywords

Prognostics
Random forest
Artificial neural network
Support vector machine
Machine learning
Artificial intelligence
Predictive modelling
Computational intelligence

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