An Intelligent System for Grinding Wheel Condition Monitoring Based on Machining Sound and Deep Learning

Immediate monitoring of the conditions of the grinding wheel during the grinding process is important because it directly affects the surface accuracy of the workpiece. Because the variation in machining sound during the grinding process is very important for the field operator to judge whether the...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.58279-58289
Main Authors: Lee, Cheng-Hsiung, Jwo, Jung-Sing, Hsieh, Han-Yi, Lin, Ching-Sheng
Format: Article
Language:English
Subjects:
Artificial intelligence
Audio signals
Deep learning
Feature extraction
Grinding machines
Grinding wheel wear
Grinding wheels
intelligent system
Intelligent systems
Machinery condition monitoring
Machining
machining sound
Monitoring
Sensors
Signal processing
Sound
Spectrum analysis
Wheels
Workpieces
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Immediate monitoring of the conditions of the grinding wheel during the grinding process is important because it directly affects the surface accuracy of the workpiece. Because the variation in machining sound during the grinding process is very important for the field operator to judge whether the grinding wheel is worn or not, this study applies artificial intelligence technology to attempt to learn the experiences of auditory recognition of experienced operators. Therefore, we propose an intelligent system based on machining sound and deep learning to recognize the grinding wheel condition. This study uses a microphone embedded in the grinding machine to collect audio signals during the grinding process, and extracts the most discriminated feature from spectrum analysis. The features will be input the designed CNNs architecture to create a training model based on deep learning for distinguishing different conditions of the grinding wheel. Experimental results show that the proposed system can achieve an accuracy of 97.44%, a precision of 98.26% and a recall of 96.59% from 820 testing samples.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2982800