Proceedings of the 2018 International Conference on Industrial Enterprise and System Engineering (IcoIESE 2018)

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

Authors
Muhammad Hanif Ramadhan, Haris Rachmat, Denny Sukma Eka Atmaja, Rasidi Ibrahim
Corresponding Author
Muhammad Hanif Ramadhan
Available Online March 2019.
DOI
https://doi.org/10.2991/icoiese-18.2019.59How to use a DOI?
Keywords
Artificial Neural Network, Industrial Visual Inspection, Principal Component Analysis, Surface Quality
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%.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2018 International Conference on Industrial Enterprise and System Engineering (ICoIESE 2018)
Part of series
Atlantis Highlights in Engineering
Publication Date
March 2019
ISBN
978-94-6252-689-1
ISSN
2589-4943
DOI
https://doi.org/10.2991/icoiese-18.2019.59How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Muhammad Hanif Ramadhan
AU  - Haris Rachmat
AU  - Denny Sukma Eka Atmaja
AU  - Rasidi Ibrahim
PY  - 2019/03
DA  - 2019/03
TI  - Multi-class Classification of Ceramic Tile Surface Quality using Artificial Neural Network and Principal Component Analysis
BT  - 2018 International Conference on Industrial Enterprise and System Engineering (ICoIESE 2018)
PB  - Atlantis Press
SP  - 334
EP  - 338
SN  - 2589-4943
UR  - https://doi.org/10.2991/icoiese-18.2019.59
DO  - https://doi.org/10.2991/icoiese-18.2019.59
ID  - Ramadhan2019/03
ER  -