Automated surface defect detection framework using machine vision and convolutional neural networks
Author
Abstract
Suggested Citation
DOI: 10.1007/s10845-021-01878-w
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
- Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
- Chung-Feng Jeffrey Kuo & Chun-Han Tsai & Wei-Ren Wang & Han-Cheng Wu, 2019. "Automatic marking point positioning of printed circuit boards based on template matching technique," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 671-685, February.
- Sebastian Meister & Mahdieu A. M. Wermes & Jan Stüve & Roger M. Groves, 2021. "Review of image segmentation techniques for layup defect detection in the Automated Fiber Placement process," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2099-2119, December.
- Durga Prasad Penumuru & Sreekumar Muthuswamy & Premkumar Karumbu, 2020. "Identification and classification of materials using machine vision and machine learning in the context of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1229-1241, June.
- Olatomiwa Badmos & Andreas Kopp & Timo Bernthaler & Gerhard Schneider, 2020. "Image-based defect detection in lithium-ion battery electrode using convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 885-897, April.
- Domen Tabernik & Samo Šela & Jure Skvarč & Danijel Skočaj, 2020. "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 759-776, March.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Feiyang Li & Nian Cai & Xueliang Deng & Jiahao Li & Jianfa Lin & Han Wang, 2022. "Serial number inspection for ceramic membranes via an end-to-end photometric-induced convolutional neural network framework," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1373-1392, June.
- Nhat-To Huynh & Duong-Dong Ho & Hong-Nguyen Nguyen, 2023. "An Approach for Designing an Optimal CNN Model Based on Auto-Tuning GA with 2D Chromosome for Defect Detection and Classification," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
- Shuo Meng & Ruru Pan & Weidong Gao & Jian Zhou & Jingan Wang & Wentao He, 2021. "A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1147-1161, April.
- Zichen Bai & Junfeng Jing, 2024. "Mobile-Deeplab: a lightweight pixel segmentation-based method for fabric defect detection," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3315-3330, October.
- Christian Kubik & Sebastian Michael Knauer & Peter Groche, 2022. "Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 259-282, January.
- Zeqing Yang & Mingxuan Zhang & Yingshu Chen & Ning Hu & Lingxiao Gao & Libing Liu & Enxu Ping & Jung Il Song, 2024. "Surface defect detection method for air rudder based on positive samples," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 95-113, January.
- Chia-Yu Hsu & Ju-Chien Chien, 2022. "Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 831-844, March.
- Yuanyuan Wang & Ling Ma & Lihua Jian & Huiqin Jiang, 2023. "Conductive particle detection via efficient encoder–decoder network," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3563-3577, December.
- Ruiyang Hao & Bingyu Lu & Ying Cheng & Xiu Li & Biqing Huang, 2021. "A steel surface defect inspection approach towards smart industrial monitoring," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1833-1843, October.
- Omid Davtalab & Ali Kazemian & Xiao Yuan & Behrokh Khoshnevis, 2022. "Automated inspection in robotic additive manufacturing using deep learning for layer deformation detection," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 771-784, March.
- Zhenxing Cheng & Hu Wang & Gui-Rong Liu, 2021. "Deep convolutional neural network aided optimization for cold spray 3D simulation based on molecular dynamics," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1009-1023, April.
- Saksham Jain & Gautam Seth & Arpit Paruthi & Umang Soni & Girish Kumar, 2022. "Synthetic data augmentation for surface defect detection and classification using deep learning," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1007-1020, April.
- José M. Navarro-Jiménez & José V. Aguado & Grégoire Bazin & Vicente Albero & Domenico Borzacchiello, 2023. "Reconstruction of 3D surfaces from incomplete digitisations using statistical shape models for manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2345-2358, June.
- Govindan, Kannan & Kannan, Devika & Jørgensen, Thomas Ballegård & Nielsen, Tim Straarup, 2022. "Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
- Tiago Afonso & Anabela C. Alves & Paula Carneiro, 2021. "Lean Thinking, Logistic and Ergonomics: Synergetic Triad to Prepare Shop Floor Work Systems to Face Pandemic Situations," International Journal of Global Business and Competitiveness, Springer, vol. 16(1), pages 62-76, December.
- Shuting Wang & Jie Meng & Yuanlong Xie & Liquan Jiang & Han Ding & Xinyu Shao, 2023. "Reference training system for intelligent manufacturing talent education: platform construction and curriculum development," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1125-1164, March.
- Emmanuel Ekene Okere & Ebrahiema Arendse & Alemayehu Ambaw Tsige & Willem Jacobus Perold & Umezuruike Linus Opara, 2022. "Pomegranate Quality Evaluation Using Non-Destructive Approaches: A Review," Agriculture, MDPI, vol. 12(12), pages 1-25, November.
- Xiaoyu Zhan & Delia Mioara Popescu & Valentin Radu, 2020. "Challenges for Romanian Entrepreneurs in Managing Remote Workers," Book chapters-LUMEN Proceedings, in: Marcin Waldemar STANIEWSKI & Valentina VASILE & Adriana Grigorescu (ed.), International Conference Innovative Business Management & Global Entrepreneurship (IBMAGE 2020), edition 1, volume 14, chapter 49, pages 670-687, Editura Lumen.
- Christoph March & Ina Schieferdecker, 2021.
"Technological Sovereignty as Ability, Not Autarky,"
CESifo Working Paper Series
9139, CESifo.
- Christoph March & Ina Schieferdecker, 2021. "Technological Sovereignty as Ability, not Autarky," Munich Papers in Political Economy 12, Munich School of Politics and Public Policy and the School of Management at the Technical University of Munich.
- Bikash Koli Dey & Hyesung Seok, 2024. "Intelligent inventory management with autonomation and service strategy," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 307-330, January.
More about this item
Keywords
Machine vision; Surface defect detection; Centerless grinding; Deep learning; ResNet-101;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:34:y:2023:i:4:d:10.1007_s10845-021-01878-w. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.