Name
Abstract | ||
|---|---|---|
This paper outlines the base concepts, materials and methods used to develop an Industry 4.0 architecture focused on predictive maintenance, while relying on low-cost principles to be affordable by Small Manufacturing Enterprises. The result of this research work was a low-cost, easy-to-develop cyber-physical system architecture that measures the temperature and vibration variables of a machining process in a Haas CNC turning centre, while storing such data in the cloud where Recursive Partitioning and Regression Tree model technique is run for predicting the rejection of machined parts based on a quality threshold. Machining quality is predicted based on temperature and/or vibration machining data and evaluated against average surface roughness of each machined part, demonstrating promising predictive accuracy. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICE.2018.8436307 | 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) |
Keywords | Field | DocType |
e-Maintenance,Predictive Maintenance,Condition Based Maintenance,Industry 4.0,Smart Manufacturing,Machine Learning,Small Manufacturing Enterprise,Low Cost,Open Source | Decision tree,Data modeling,Computer science,Machining,Recursive partitioning,Systems architecture,Predictive maintenance,Industry 4.0,Reliability engineering,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-5386-1470-9 | 1 | 0.41 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erim Sezer | 1 | 1 | 0.41 |
| david romero | 2 | 69 | 10.57 |
| Federico Guedea | 3 | 1 | 0.41 |
| Marco Macchi | 4 | 36 | 10.56 |
| Christos Emmanouilidis | 5 | 62 | 9.96 |