Abstract
This work presents an optical inspection-guiding system for electronic board manufacturing. The system monitors in real time the mounting process of electronic components performed by an operator. It visually guides the operator through the mounting process while checking the correctness of its actions. As a consequence, mounting errors are reduced while operator comfort is enhanced. This work also introduces a novel method to generate virtual images from a few real images in order to generate enough data for model training. The proposed method is tested using 7 different descriptor combinations and 4 different classifiers. We have also collected, generated, and evaluated a component dataset of 20 different components, called ECAD. The solution was tested with 16 real scenarios, different electronic boards which are empty or full with components. Finally, a usability test was carried out with 21 different people comparing the original and proposed solutions. The propose system is advantageous since it enhances operator’s comfort and satisfaction, increases mounting speed, and reduces error ratio.
Similar content being viewed by others
Notes
References
Acciani G, Brunetti G, Fornarelli G (2006) A multiple neural network system to classify solder joints on integrated circuits. International Journal of Computational Intelligence Research 2(4):337–348
Aravand A, Sobhi J (2017) The implementation of automated optical inspection in printed circuit boards. International Journal of Computer Science and Network Security 17(6):137–146
Bhardwaj S.C. (2016) Detection and verification of missing components in SMD using AOI techniques. International Journal of Computer Graphics 7(2):13–22
Brooke J (1995) Sus: A quick and dirty usability scale. Usability Eval. Ind. 189
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE Computer society conference on computer vision and pattern recognition (CVPR’05), vol 1, pp 886–893
Garrido-Jurado S, Muñoz-Salinas R., Madrid-Cuevas F J, Marín-jiménez M.J. (2014) Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recogn. 47(6):2280–2292
Hagi H, Iwahori Y, Fukui S, Adachi Y, Bhuyan M.K. (2014) Defect classification of electronic circuit board using SVM based on random sampling. Procedia - Procedia Computer Science 35:1210–1218
Ho T K (1995) Random decision forests. In: Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1, ICDAR ’95, pp. 278. IEEE Computer Society, Washington
Hu M K (1962) Visual pattern recognition by moment invariants. IRE Transactions on Information Theory 8(2):179–187
Janóczki M, Becker Á, Jakab L, Gróf R, Takács T (2013) Automatic optical inspection of soldering. In: Materials Science-Advanced Topics. Intech
Karger D R (1993) Global min-cuts in rnc, and other ramifications of a simple min-out algorithm. In: Proceedings of the Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ’93. Society for Industrial and Applied Mathematics, Philadelphia, pp 21–30
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (7):971–987
Quinlan JR. Induction of decision trees. Tech. rep.http://hunch.net/~coms-4771/quinlan.pdf
Rother C, Kolmogorov V, Blake A (2004) Grabcut -interactive foreground extraction using iterated graph cuts ACM Transactions on Graphics (SIGGRAPH)
Sural S, Qian G, Pramanik S (2002) Segmentation and histogram generation using the hsv color space for image retrieval. In: ICIP
Takada Y, Shiina T, Usami H, Iwahori Y (2017) Defect detection and classification of electronic circuit boards using keypoint extraction and CNN features. The Ninth International Conferences on Pervasive Patterns and Applications Defect 2017, pp. 113–116
Wang J, Chen Q, Chen Y (2004) Rbf kernel based support vector machine with universal approximation and its application. In: Yin F. L., Wang J., Guo C. (eds) Advances in neural networks–ISNN 2004. Springer, Berlin, pp 512–517
Funding
This work was supported by the Basque Industry 4.0 under Grant BI-00023/2017, in collaboration with IKORFootnote 1 company.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Dataset available at https://www.kaggle.com/mrojer/electronic-components-for-automatic-detection
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Ojer, M., Serrano, I., Saiz, F. et al. Real-time automatic optical system to assist operators in the assembling of electronic components. Int J Adv Manuf Technol 107, 2261–2275 (2020). https://doi.org/10.1007/s00170-020-05125-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-020-05125-z