Multi-class Classification of Ceramic Tile Surface Quality using Artificial Neural Network and Principal Component Analysis

Abstract

The visual inspection of ceramic tile surface is an important factor which may influence the perceived surface quality of the product. While manual labor offers an alternative in the task of visual inspection, human limitation related problem such as fatigue and safety may pose an undesirable inspection performance when applied in mass production industry. This study attempted to automate the process of ceramic quality inspection through computerized image classification. Specifically, a dimensionality reduction technique called Principal Component Analysis and classification technique Artificial Neural Network were incorporated in the study to classify five categories of surface quality: normal, crack, chip-off, scratch and dry spots. Given 400 principal components as the input layer and three hidden layers consisting 150 hidden units each, the model was trained under 19,696 training images by using Adam Optimization. By performing prediction on the test set consisting of 4,256 images, the trained model was able to achieve the classification accuracy of 90.13%.

Predicting Dimensional Deviation of Ceramic Tiles using Machine Learning Methods

“…1 Neural Network Artificial 2 Support Vector Machine (SVM) 3 K-Nearest Neighbor (KNN) 4 Laser Triangulation Method (LTM) 5 Morphological Operations 6 Discrete Wavelet Transform (DWT) 7 Gray-Level Co-Occurrence Matrix (GLCM) 8 Correlation Method 9 Random Forest 10 Logistic Regression 11 Posterior Probability 12 Forward Feature Selection…”