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Factory 4.0 Toolkit for Smart Manufacturing Training
Joseph Dennis Cuiffi
, Haifeng Wang
, Josephine Heim
, Brian W. Anthony
, Sangwoon Kim
, David Donghyun Kim
Penn State New Kensington
Research output
:
Contribution to journal
›
Conference article
›
peer-review
3
Link opens in a new tab
Scopus citations
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Dive into the research topics of 'Factory 4.0 Toolkit for Smart Manufacturing Training'. Together they form a unique fingerprint.
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Computer Science
Data Analytics
100%
Software Development Tool
50%
Control Algorithm
50%
Engineering Student
50%
Power Consumption
50%
Learning Experiences
50%
Educational Experience
50%
Process Optimization
50%
Fundamental Concept
50%
Operating Condition
50%
Undergraduate Course
50%
Data Simulation
50%
Training Material
50%
Scrambling
50%
Digital Twin
50%
Feedback Control
50%
Technological Development
50%
Intended Audience
50%
Industrial Internet of Things
50%
Keyphrases
Smart Manufacturing
100%
Data Analytics
33%
Dive
33%
Manufacturing Concepts
33%
Process Optimization
16%
Statistical Analysis
16%
Technology Development
16%
Process Systems
16%
Educational Experience
16%
Production Data
16%
Introductory Overview
16%
Extrusion
16%
Learning Outcomes
16%
Manufacturing Systems
16%
Power Consumption
16%
Data Simulation
16%
Specific Training
16%
All Levels
16%
Undergraduate Engineering Students
16%
Data Management Tools
16%
Optimization Control
16%
Program Analysis
16%
Intended Audience
16%
Analytic Modeling
16%
Feature-rich
16%
Training Workshop
16%
System Optimization
16%
Feedback Control Algorithm
16%
Student Audience
16%
Predictive Modeling
16%
Attitude Survey
16%
Internet Information
16%
Management Benefits
16%
Undergraduate Courses
16%
Manufacturing Workers
16%
Educational System
16%
Training Approaches
16%
Training Materials
16%
Training Content
16%
Production Platforms
16%
Digital Twin
16%
Industrial Internet of Things (IIoT)
16%
Lab Activities
16%
Statistical Analysis Software
16%
Social Sciences
Data Analytics
100%
Technology Development
50%
Energy Consumption
50%
Internet-Of-Things
50%
Digital Twin
50%
Learning Experiences
50%
Production Data
50%
Benefits Management
50%
Economics, Econometrics and Finance
Workforce
100%
Data Analytics
100%
Manufacturing System
50%
Learning Outcome
50%
Engineering
Smart Manufacturing
100%
Process System
16%
Production Data
16%
Control Algorithm
16%
Gas Fuel Manufacture
16%
Engineering Student
16%
Electric Power Utilization
16%
Technological Development
16%
Feedback Control
16%
Industrial Internet
16%
Internet-Of-Things
16%
Production Platforms
16%
Material Science
Production Data
100%