Skip to main navigation
Skip to search
Skip to main content
Penn State Home
Help & FAQ
Link opens in a new tab
Search content at Penn State
Home
Researchers
Research output
Research units
Equipment
Grants & Projects
Prizes
Activities
Cloud-Based Parallel Machine Learning for Tool Wear Prediction
Dazhong Wu
, Connor Jennings
, Janis Terpenny
,
Soundar Kumara
, Robert X. Gao
Harold and Inge Marcus Department of Industrial and Manufacturing Engineering
Institute for Computational and Data Sciences (ICDS)
Center for Interdisciplinary Mathematics
Research output
:
Contribution to journal
›
Article
›
peer-review
67
Link opens in a new tab
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Cloud-Based Parallel Machine Learning for Tool Wear Prediction'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Computer Science
Random Decision Forest
100%
Parallel Machine
100%
Learning System
100%
Machine Learning
100%
Predictive Model
66%
Map-Reduce
66%
Machine Learning Technique
33%
Real-Time Application
33%
Computational Efficiency
33%
Condition Monitoring
33%
Training Data
33%
Machine Learning Algorithm
33%
Prediction Accuracy
33%
Processing Speed
33%
Monitoring Data
33%
Cloud Computing System
33%
Cloud Computing
33%
Industrial Internet of Things
33%
Mechanical Component
33%
Engineering
Random Forest
100%
Learning System
100%
Cloud Computing
66%
Smart Manufacturing
66%
MapReduce
66%
Limitations
33%
Progression
33%
Condition Monitoring
33%
Mean-Squared-Error
33%
Accurate Prediction
33%
Mechanical Component
33%
Computational Efficiency
33%
Machine Learning Technique
33%
Machine Learning Algorithm
33%
Monitoring Data
33%
Industrial Internet
33%
Internet-Of-Things
33%
Keyphrases
Tool Wear Prediction
100%
Smart Manufacturing
66%
Model-based Prognostics
33%
Sensor-generated Data
33%
Condition Monitoring Data
33%
Fault Development
33%
Parallel Random Forest
33%
Milling Tests
33%
Chemical Engineering
Learning System
100%
Condition Monitoring
33%
Material Science
Data Processing
100%