Prediction of tool wear using machine vision approach
Tool wear prediction plays a crucial role in the machining industry for proper planning and optimization of cutting conditions. Nevertheless, tool wear assessment method using sensor signals has its drawbacks in the industry application. The objective of this study is to apply Artificial Neura...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/7148/ http://eprints.uthm.edu.my/7148/1/J14148_72aaaa163aaaa2172e5b3a0bf92821e7.pdf |
| Summary: | Tool wear prediction plays a crucial role in the machining industry for proper
planning and optimization of cutting conditions. Nevertheless, tool wear assessment method
using sensor signals has its drawbacks in the industry application. The objective of this study is
to apply Artificial Neural Network (ANN) prediction model and machine vision system to
predict flank wear in turning operation based on the texture images of machined surface
captured by complementary metal oxide semiconductor (CMOS) camera in-cycle. The image
pre-processing technique was utilized to enhance the quality of surface texture images acquired
from the experiment and the texture descriptors were extracted from the processed images using
gray-level co-occurrence matrix (GLCM). Three ANN prediction models with different input
variables were developed using MATLAB software. The findings showed that the ANN
prediction model with input variables of contrast, entropy, cutting speed, and feed rate
outperformed the other ANN prediction model. The prediction accuracy of this model in
estimating flank wear reached up to 93.18%. A very good fit and the relationship could be
found in this model with R2
of 0.9863 for flank wear. |
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