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Anomaly detection in formed sheet metals using convolutional autoencoders

Research output: Journal contributions › Conference article in journal › Research › peer-review

Standard

Anomaly detection in formed sheet metals using convolutional autoencoders. / Heger, Jens; Desai, Gururaj; Zein El Abdine, Mazhar.
In: Procedia CIRP, Vol. 93, 01.01.2020, p. 1281-1285.

Research output: Journal contributions › Conference article in journal › Research › peer-review

Harvard

Heger, J, Desai, G & Zein El Abdine, M 2020, 'Anomaly detection in formed sheet metals using convolutional autoencoders', Procedia CIRP, vol. 93, pp. 1281-1285. https://doi.org/10.1016/j.procir.2020.04.106

APA

Heger, J., Desai, G., & Zein El Abdine, M. (2020). Anomaly detection in formed sheet metals using convolutional autoencoders. Procedia CIRP, 93, 1281-1285. https://doi.org/10.1016/j.procir.2020.04.106

Vancouver

Heger J, Desai G, Zein El Abdine M. Anomaly detection in formed sheet metals using convolutional autoencoders. Procedia CIRP. 2020 Jan 1;93:1281-1285. doi: 10.1016/j.procir.2020.04.106

Bibtex

@article{340e356df41b4efe8ddd8afb4ddb78ef,
title = "Anomaly detection in formed sheet metals using convolutional autoencoders",
abstract = "Autoencoders are used to compress data and learn how to reliably reconstruct it. Through comparing the reconstruction error with a set threshold, they are able to detect anomalies in unseen datasets where the data does not quite match the reconstructed input samples. In this work, we attempt to investigate the use of convolutional autoencoders in the field of visual quality inspection, where images of formed sheet metals from a real production line are inspected for the occurrence of cracks and wrinkle formation. This approach tackles the problem of needing enough defective samples to attain reliable detection accuracies",
keywords = "Engineering, Artifical intelligence, Deep autoencoders, Process monitoring, Surface quality inspection",
author = "Jens Heger and Gururaj Desai and {Zein El Abdine}, Mazhar",
note = "The authors gratefully acknowledge the financial support given by the Federal Ministry for Economic Affairs and Energy (BMWi) under the Central Innovation Programme for SMEs IM)(Zorfe thopjrectartSPm“”ress on the basis of a decision of the German Bundestag (grant number: ZF4084503GR7).; 53rd CIRP Conference on Manufacturing Systems - CMS 2020, CMS 2020 ; Conference date: 01-07-2020 Through 03-07-2020",
year = "2020",
month = jan,
day = "1",
doi = "10.1016/j.procir.2020.04.106",
language = "English",
volume = "93",
pages = "1281--1285",
journal = "Procedia CIRP",
issn = "2212-8271",
publisher = "Elsevier B.V.",

}

RIS

TY - JOUR

T1 - Anomaly detection in formed sheet metals using convolutional autoencoders

AU - Heger, Jens

AU - Desai, Gururaj

AU - Zein El Abdine, Mazhar

N1 - Conference code: 53

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Autoencoders are used to compress data and learn how to reliably reconstruct it. Through comparing the reconstruction error with a set threshold, they are able to detect anomalies in unseen datasets where the data does not quite match the reconstructed input samples. In this work, we attempt to investigate the use of convolutional autoencoders in the field of visual quality inspection, where images of formed sheet metals from a real production line are inspected for the occurrence of cracks and wrinkle formation. This approach tackles the problem of needing enough defective samples to attain reliable detection accuracies

AB - Autoencoders are used to compress data and learn how to reliably reconstruct it. Through comparing the reconstruction error with a set threshold, they are able to detect anomalies in unseen datasets where the data does not quite match the reconstructed input samples. In this work, we attempt to investigate the use of convolutional autoencoders in the field of visual quality inspection, where images of formed sheet metals from a real production line are inspected for the occurrence of cracks and wrinkle formation. This approach tackles the problem of needing enough defective samples to attain reliable detection accuracies

KW - Engineering

KW - Artifical intelligence

KW - Deep autoencoders

KW - Process monitoring

KW - Surface quality inspection

UR - http://www.scopus.com/inward/record.url?scp=85092439400&partnerID=8YFLogxK

U2 - 10.1016/j.procir.2020.04.106

DO - 10.1016/j.procir.2020.04.106

M3 - Conference article in journal

AN - SCOPUS:85092439400

VL - 93

SP - 1281

EP - 1285

JO - Procedia CIRP

JF - Procedia CIRP

SN - 2212-8271

T2 - 53rd CIRP Conference on Manufacturing Systems - CMS 2020

Y2 - 1 July 2020 through 3 July 2020

ER -

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