Abstract
For decades, aluminum extrusion has been successfully applied in the manufacturing of profiles for the applications ranging from locomotives to skyscrapers. In recent years however, increasing profile complexity and the need for rapid production have lead to greater challenges for manufactures seeking rapid and robust production procedures. As a consequence, the occurrence of defects in extruded profile surfaces continues to create difficulties often requiring disposal of entire components. Hence, quality inspection of the profiles must be performed prior to packing in order to identify and appropriately manage defect-containing extrusions. Up until now, quality control in extrusion factories is primarily performed by the human eye due to its high performance in discriminating defect varieties. But human performance is cost intensive and furthermore prone to failure, especially when applied in high-throughput environments. On that account this paper proposes an approach in surface defect classification and detection, whereby a simple camera records the extruded profiles during production and a neural network architecture distinguishes between immaculate surfaces and surfaces containing a variety of common defects (surface defect classification). Furthermore, a neural network is employed to point out the defects in the video frames (surface defect detection). In this work, we show that methods from artificial intelligence are highly compatible with industrial applications such as quality control even under common industry constraints such as very limited data set sizes for training a neural network. Data augmentation as well as transfer learning are the key ingredients for training networks that meet the high requirements of modern production facilities in detecting surface defects, particularly when access to training sets is limited. Accuracies of 0.98 in the classification and mean average precisions of 0.47 in the detection setting are achieved whilst training on a data set containing as little as 813 images. Real-time classification and detection codes are implemented, and the networks perform reliably despite changes in lighting conditions and camera orientation.
Similar content being viewed by others
References
Qamar SZ, Arif AFM, Sheikh AK (2004) Analysis of product defects in a typical aluminum extrusion facility. Mater Manuf Process 19(3):391–405
Chondronasios A, Popov I, Jordanov I (2016) Feature selection for surface defect classification of extruded aluminum profiles. Int J Adv Manuf Technol 83(1–4):33–41
Gonzalez-Adrados JR, Pereira H (1996) Classification of defects in cork planks using image analysis. Wood Sci Technol 30(3):207–215
Bishop CM (2006) Pattern recognition and machine learning. Information science and statistics. Springer
Lopes F, Pereira H, Natale FGB, De Tintrup F, Giusto DD, Vernazza G (1995) Cork pores and defects detection by morphological image analysis. Wood Science and Technology
Georgieva A, Jordanov I (2007) Image processing techniques for cork tiles classification. In: 2007 IEEE international conference on signal processing and communications, pp 576–579
Di L, Liang L-Q, Zhang W-J (2014) Defect inspection and extraction of the mobile phone cover glass based on the principal component analysis. Int J Adv Manuf Technol 73(9–12):1605–1614
Shlens J (2005) A tutorial on principal component analysis. ar**v:1404.1100
Engelhardt M, Behne D, Grittner N, Neumann A, Reimche W, Klose C (2015) Non-destructive testing of longitudinal and charge weld seams in extruded aluminum and magnesium profiles, vol 2
Garbacz P, Giesko T, Mazurkiewicz A (2015) Inspection method of aluminium extrusion process. Arch Civil Mech Eng 15(3):631–638
Zhang X-w, Ding Y-q, Lv Y-y, Shi A-y, Liang R-y (2011) A vision inspection system for the surface defects of strongly reflected metal based on multi-class svm. Expert Syst Appl 38(5):5930–5939
Park J-K, Kwon B-K, Park J-H, Kang D-J (2016) Machine learning-based imaging system for surface defect inspection. Int J Precis Eng Manuf Green Technol 3(3):303–310
Ciora RA, Simion CM (2014) Industrial applications of image processing. Acta Universitatis Cibiniensis–Technical Series 64(1):17–21
Tzutalin (2015) Labelimg. https://github.com/tzutalin/labelImg. Git code
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. ar**v:1409.1556
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778
Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE Conference on computer vision and pattern recognition (CVPR), pp 1–9
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) (* = equal contribution). Imagenet large scale visual recognition challenge IJCV. http://www.image-net.org/challenges/LSVRC/
Demant C, Streicher-Abel B, Garnica C (2013) Industrial image processing: visual quality control in manufacturing, 2 edn. Springer
Yao Y, Rosasco L, Caponnetto A (2007) On early stop** in gradient descent learning. Constr Approx 26(2):289–315
Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems, vol 25. Curran Associates, Inc., pp 1097–1105
Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, Lecun Y (2014) Overfeat: integrated recognition, localization and detection using convolutional networks. In: International conference on learning representations (ICLR2014), CBLS
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: ECCV
Diederik PK, Ba J (2014) Adam: a method for stochastic optimization. ar**v:1412.6980
Ren S, He K, Girshick RB, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39:1137–1149
Girshick RB, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on computer vision and pattern recognition, pp 580–587
Girshick RB (2015) Fast R-CNN. In: 2015 IEEE international conference on computer vision (ICCV), pp 1440–1448
Huang J, Rathod V, Sun C, Zhu M, Korattikara A, Fathi A, Fischer I, Wojna Z, Song Y, Guadarrama S, Murphy K (2016) Speed/accuracy trade-offs for modern convolutional object detectors. ar**v:1611.10012
Ruder S (2016) An overview of gradient descent optimization algorithms. ar**v:1609.04747
Acknowledgements
We thank Aluminium Laufen for the support of this project.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interests
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Neuhauser, F.M., Bachmann, G. & Hora, P. Surface defect classification and detection on extruded aluminum profiles using convolutional neural networks. Int J Mater Form 13, 591–603 (2020). https://doi.org/10.1007/s12289-019-01496-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12289-019-01496-1