IoT Based CNC Machine Condition Monitoring System Using Machine Learning Techniques









Abstract

We developed a CNC machine's condition monitoring system based on Artificial Neural Network (ANN) and correlate the same with the real-time CNC machine data. The classification of the condition of a CNC machine was done by deciding whether it is a fresh machine or worn machine using machine learning techniques. In consideration of real time data loaded from a CNC machine we built a database and then modelled an ANN. Based on this approach of machine learning which implements pattern recognition and probabilistic modelling of the CNC machine data, classification of the condition of a CNC machine was done successfully. Finally, performance analysis of this machine model prevail in terms of Machine Error Rate (MER) upholds the impressive fact that modeling using the ANN yields better results over another alternative modeling techniques and can be used for developing Automatic CNC machine condition monitoring and recognition system.


Modules


Algorithms


Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL