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

Putri, Anggie Prahas; Rachmat, Haris; Atmaja, Denny Sukma Eka

doi:10.1051/matecconf/201713500053

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…Song et al [17] successfully identified wood-grain defects using histogram features, achieving a recognition rate of 99.8%. Additionally, Putri et al [18] detected defects on ceramic surfaces using the gray-level co-occurrence matrix technique, with an accuracy of 92.31%. Today, image segmentation techniques have been overshadowed by the widespread use of deep learning models, which offer improved segmentation results [19].…”

Section: Related Workmentioning

confidence: 99%

DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection

Huang,

Jin

2024

Applied Sciences

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Surface defect detection is a critical task in the manufacturing industry to ensure product quality and machining efficiency. Image-based precise defect detection faces significant challenges due to defects lacking fixed shapes and the detection being heavily influenced by lighting conditions. Addressing the efficiency demands of defect detection algorithms, often deployed on embedded devices, and the highly imbalanced pixel ratio between foreground and background images, this paper introduces a multi-scale fusion staged U-shaped convolutional neural network (DEU-Net). The network provides segmentation results for defect anomalies while indicating the probability of defect presence. It enables the model to train with fewer parameters, a crucial requirement for practical applications. The proposed model achieves an MIoU of 66.94 and an F1 score of 74.89 with lower Params (36.675) and Flops (19.714). Comparative analysis with FCN, U-Net, Deeplab v3+, U-Net++, Attention U-Net, and Trans U-Net demonstrates the superiority of the proposed approach in surface defect detection.

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Section: Related Workmentioning

confidence: 99%

DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection

Huang,

Jin

2024

Applied Sciences

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“…Industri keramik ini punya kontribusi yang signifikan terhadap penyediaan kebutuhan domestik, perolehan devisa dan penyerapan tenaga kerja [4]. Dilihat dari data ekspor dan impor yang didapatkan dari [12] bahwa volume impor keramik di Indonesia sendiri masih tinggi dibandingkan volume ekspor. Salah satu faktor yang diyakini oleh Asosiasi Aneka Industri dan Keramik Indonesia (Asaki) mengapa adanya ketimpangan antar ekspor dan impor keramik dalam negeri adalah kualitas yang dimiliki.…”

Section: Pendahuluanunclassified

“…Seperti yang dilakukan [3] yang menggunakan teknik pengolahan citra digital dalam mengidentifikasi cacat pada jenis ubin polos dan bertekstur. Selain itu dalam penelitian [12] yang menggunakan teknik yang sama untuk mendeteksi cacat pada permukaan keramik. Serta yang dilakukan oleh [2] yang menggunakan teknik pengolahan citra digital pada keramik untuk mengetahui luas keramik dan cacat keramik untuk dry spot .…”

Section: Landasan Teori a Image Processingunclassified

Optimasi Parameter Pengukuran Dimensi dan Defect Ubin Keramik dengan Metode Taguchi

Atmaja¹,

Herliansyah²

2021

JSC

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The ceramics industry in Indonesia has a large contribution to the growth of various aspects in Indonesia. But in reality, there is still an imbalance between exports and imports for ceramic products. One way is to improve the quality of the ceramic industry in Indonesia. In fact, the ceramic quality inspection process in the ceramic industry is still done manually which can make mistakes in identifying defects. In this study, the design of variable identification system of ceramics was carried out specifically in the area of ceramics and dry spot defects on ceramic surfaces using image processing. Whereas to get a low error rate against the applicable variables, a design of experiment with the Taguchi approach is carried out. The results show that 50 cm distance, 300 lux light, 1x resize and 0.06 threshold can produce an image that has the smallest error value when identifying ceramic area and dry spot defects on the ceramic surface.

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“…In traditional surface defect detection methods, researchers have focused on various aspects of texture, color, and shape features. For texture features, Putri et al [3] utilized the gray level co-occurrence matrix (GLCM) extraction method to capture the features of ceramic surface quality defects. The extracted data, including autocorrelation, variance, and object count, were processed and inputted into a fuzzy model, achieving a defect recognition accuracy of 96.87%.…”

mentioning

confidence: 99%

Research on Defect Detection of Electronic Components in Facility Greenhouse Based on Improved YOLOv5

Qi,

Yang,

Liang

et al. 2023

IEEE Access

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To address challenges in manual detection of electronic component defects in facility greenhouses, this paper presents an electronic component defect detection method using the Improved YOLOv5 recognition algorithm. By introducing the Convolutional Block Attention Module into the YOLOv5 backbone network, the model's identification and classification of defect types are enhanced, the network's receptive field is improved, and recognition accuracy is increased. The proposed method also utilizes the α-CIoU loss function to expedite network regression. The effectiveness of the Improved YOLOv5 model was evaluated on a self-made electronic component dataset. Experimental results revealed an average accuracy of 91.9% and a detection time of 2.1 seconds per frame. Compared to the original YOLOv5 model, the average precision value increased by 1.5%, and the single-image detection speed improved by 0.2 seconds per frame. These improvements meet the accurate and efficient requirements for electronic component defect detection within the greenhouse equipment such as roller shutters and ventilators. This study provides valuable theoretical and technical support for defect detection in electronic components, contributing to the performance optimization of electrical equipment in facility greenhouses. The proposed method shows great potential for further development and application in real-world scenarios.

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

Cited by 8 publications

References 5 publications

DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection

DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection

Optimasi Parameter Pengukuran Dimensi dan Defect Ubin Keramik dengan Metode Taguchi

Research on Defect Detection of Electronic Components in Facility Greenhouse Based on Improved YOLOv5

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