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

By Dazhong Wu, Connor Jennings, Janis P. Terpenny, Robert X. Gao, Soundar Kumara *. 2017

609 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/5913cbd4d209311df06c7fdab7ba34b082098853

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In Electrical and Electronic Engineering

1. D. Huisingh

Citations in the corpus

• Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach (2021, neutral): “Afterextracting someinformativefeatures,theconventionalmachinelearning algorithms,suchasartificialneuralnetwork[12],[13],ex-tremelearningmachine(ELM)[14],SVM[15],[16],neural networks[17],andrandomforest(RF)[18],canbeemployed topredicttheRUL.”
• Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects (2019, background): “Next, predictive maintenance based on heterogeneous data, such as cutting forces, vibration signals and acoustic emissions, from milling machinery was the main focus in Reference [65].”
• A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions (2019, background): “In the SM environment, a smart spare parts inventory management system was proposed to establish transparency between manufacturers and suppliers and to reduce the inventory costs (Zheng and Wu, 2017).”