An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products

Habibpour, Maryam; Gharoun, Hassan; Tajally, AmirReza; Shamsi, Afshar; Asgharnezhad, Hamzeh; Khosravi, Abbas; Nahavandi, Saeid

doi:10.48550/arxiv.2107.11643

Cited by 1 publication

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“…In the study [4], where four powerful CNN-based models were used, the accuracy achieved is not explicitly stated, but it's clear that the proposed hybrid model's performance likely surpasses any achieved in that study.…”

Section: Discussionmentioning

confidence: 86%

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Transfer Learning for Detection of Casting Defects Model In Scope of Industrial 4.0

TANYILDIZ,

BATUR ŞAHİN

2023

Türk Doğa Ve Fen Dergisi

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Casting represents a production process where a liquid material is poured into a mold with a hollow cavity, usually of the intended shape, following which its solidification is allowed. Numerous defect types are available, including blow holes, pin holes, burrs, mold material defects, shrinkage defects, metallurgical defects, casting metal defects, etc. All industries have quality control departments to eliminate the occurrence of this defective product. But the main problem is that this inspection process is done manually. This is a very time consuming process and due to human sensitivity this is not 100% accurate. In this study, we will verify whether the "manual inspection" bottleneck can be eliminated by automating the inspection process with transfer learning in the manufacturing process of casting products. In this study, we will verify whether the "manual inspection" bottleneck can be eliminated by automating the inspection process with transfer learning in the manufacturing process of casting products. In this study, the casting images were divided into two separate classes, and the classification process was carried out by applying deep learning architectures. The benefits of this proposed approach are discussed and proposed as a more efficient way to control the quality of final products under Industry 4.0.

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Section: Discussionmentioning

confidence: 86%

“…At the end of the study, they achieved 99% accuracy. In the study [4] four powerful CNN-based models (VGG16, ResNet50, DenseNet121, and InceptionResNetV2) were applied to the dataset and produced the feature maps. The extracted features were classified into various classifiers.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for Detection of Casting Defects Model In Scope of Industrial 4.0

TANYILDIZ,

BATUR ŞAHİN

2023

Türk Doğa Ve Fen Dergisi

View full text Add to dashboard Cite

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An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products

Cited by 1 publication

References 0 publications

Transfer Learning for Detection of Casting Defects Model In Scope of Industrial 4.0

Transfer Learning for Detection of Casting Defects Model In Scope of Industrial 4.0

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