Design Selection of In-UVAT Using MATLAB Fuzzy Logic Toolbox

Rachmat, Haris; Mulyana, Tatang; Hasan, Sulaiman; Ibrahim, Mustaffa

doi:10.1007/978-3-319-51281-5_54

Cited by 10 publications

(9 citation statements)

References 11 publications

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“…In order to overcome the issues arise from manual quality inspection, an attempt to automate the visual inspection has been utilized in several studies by using Artificial Intelligence (AI) and Machine Learning techniques [5], [6], [10], [11]. A fuzzy logic system [12] with Gray-Level Co-Occurrence Matrix (GLCM) feature extraction was utilized by Putri et al [6] to detect ceramic tile surface defect. The study was able to correctly classify 12 out of 13 test images and thus giving an accuracy rate of 92.31%.…”

Section: Introductionmentioning

confidence: 99%

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

Ramadhan¹,

Rachmat

Atmaja

et al. 2019

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

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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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Section: Introductionmentioning

confidence: 99%

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

Ramadhan¹,

Rachmat

Atmaja

et al. 2019

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

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“…Development of the SCADA application leverages the Wonderware Information Server with Active Factory and Generic Data Grid in order to obtain reporting system a system that can be accessed online. The researcher has been presented these SCADA such as [2][3][4][5]. The modelization for the control system is made using Simulink graphical model.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Woven Fabric Defect Detection using Image Processing and Fuzzy Logic Method at PT. Buana Intan Gemilang

Safitri¹,

Mulyana²

2019

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

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The development of textile industry which 3rd position in the largest export values in Indonesia prove that the quality of textile must be one of factors that should be considered for all of textile companies. Buana Intan Gemilang is one of the companies that produce woven fabric. This company s produce curtain woven fabric. Quality is the most important factor to reach high level customer satisfaction. To get the best quality of product needs to consider their quality control. According to inspection process in Buana Intan Gemilang, manual inspection for woven fabric defect detection need 19.87 second for average inspection time. Therefore, unbalance of production volume with inspection process cause the bottle neck in inspection process. In this research, proposed designing automated fabric inspection using image processing and Fuzzy Logic Model. This processed uses GLCM as feature extraction with three parameters are cluster shade, cluster prominence, and number of object. The proposed fabric inspection using Fuzzy Logic implemented with MATLAB provides better result in identifying fabric defect and optimizing process time. This research using 120 training data, 80 offline test data, and 80 real time test data. Identification automation defect of woven fabric test data can produce accuracy 97, 5% and averaging process time 1.15 second. This result is better than manually inspection process that took 19.87 second for scanning defect of woven fabric.

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“…The membership value or membership degree or membership function is the main characteristic of reasoning with the fuzzy logic [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Conversely fuzzy logic is a logic that has a value of fuzziness between right and wrong. In fuzzy logic theory, a value can be true or false at the same time but how much truth and error a value depends on the weight of its membership [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Design of Automation System for Ceramic Surface Quality Control Using Fuzzy Logic Method at Balai Besar Keramik (BBK)

2017

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Abstract. Indonesia is the world's top 10 ceramic users. Currently, the process of ceramic quality inspection in Indonesia, particularly in Balai Besar Keramik (BBK) is still done manually by human vision. Therefore, it is necessary to design a visual inspection system for digital image processing of ceramic automation using a Fuzzy Logic method. Fuzzy model is one method that can be used to determine ceramic surface quality control. This study aims to apply the fuzzy model in the design of automation system for ceramic surface quality control and describes its accuracy rate and automatic database. To perform feature extraction using GLCM extraction method to obtain autocorrelation, the sum of square (variance), and a number of the object. The information is used as input for data processing using the fuzzy model in the identification of quality of ceramic surface defect. In this research used Fuzzy model with GLCM extraction feature and process by using MATLAB software. This research used 32 training data can produce the accuracy of 96.87% and based on the real time system of 92.31% used 13 real time test data.

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Design Selection of In-UVAT Using MATLAB Fuzzy Logic Toolbox

Cited by 10 publications

References 11 publications

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

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

Optimizing Woven Fabric Defect Detection using Image Processing and Fuzzy Logic Method at PT. Buana Intan Gemilang

Design of Automation System for Ceramic Surface Quality Control Using Fuzzy Logic Method at Balai Besar Keramik (BBK)

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