Title:A Convolution Neural Network-based Approach for Metal Surface
Roughness Evaluation
Volume: 17
Issue: 2
Author(s): Zengren Pan, Yanhui Liu*, Zhiwei Li, Qiwen Xun and Ying Wu
Affiliation:
- School of Materials Engineering, Shanghai University of Engineering Science, Shanghai, China
Keywords:
Product quality control, surface roughness evaluation, convolutional neural network, deep learning, data augmentation, transfer learning approach.
Abstract:
Background: Metal surface roughness detection is an essential step of quality control
in the metal processing industry. Due to the high manual involvement and poor efficiency of traditional
roughness testing, rapid automated vision detection has received increasing attention in
product quality control. Many methods have focused on extracting features related to roughness
from images by means of mathematical statistics. However, these methods often rely on extensive
experiments and complex calculations, while being sensitive to external environmental disturbances.
Methods: In this paper, a convolution neural network-based approach for metal surface roughness
evaluation has been proposed. The convolutional neural network was initialized using a transfer
learning strategy, and the data augmentation technique was applied to the benchmark dataset for
sample expansion.
Results: To evaluate this approach, samples of 4 types of roughness classes were prepared. The
samples were divided into a training set, validation set, and test set in the ratio of 7:2:1. The accuracy
of the neural network on the test set was found to be above 86%.
Conclusion: The effectiveness of the proposed approach and its superiority over manual detection
have been demonstrated in the experiments.
