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Compilation of training datasets for use of convolutional neural networks supporting automatic inspection processes in industry 4.0 based electronic manufacturing

Schwebig, Alida Ilse Maria; Tutsch, Rainer ORCID

Ensuring the highest quality standards at competitive prices is one of the greatest challenges in the manufacture of electronic products. The identification of flaws has the uppermost priority in the field of automotive electronics, particularly as a failure within this field can result in damages and fatalities. During assembling and soldering of printed circuit boards (PCBs) the circuit carriers can be subject to errors. Hence, automatic optical inspection (AOI) systems are used for real-time detection of visible flaws and defects in production. This article introduces an application strategy for combining a deep learning concept with an optical inspection system based on image processing. Above all, the target is to reduce the risk of error slip through a second inspection. The concept is to have the inspection results additionally evaluated by a convolutional neural network. For this purpose, different training datasets for the deep learning procedures are examined and their effects on the classification accuracy for defect identification are assessed. Furthermore, a suitable compilation of image datasets is elaborated, which ensures the best possible error identification on solder joints of electrical assemblies. With the help of the results, convolutional neural networks can achieve a good recognition performance, so that these can support the automatic optical inspection in a profitable manner. Further research aims at integrating the concept in a fully automated way into the production process in order to decide on the product quality autonomously without human interference.

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Schwebig, A.I.M., Tutsch, R., 2020. Compilation of training datasets for use of convolutional neural networks supporting automatic inspection processes in industry 4.0 based electronic manufacturing. https://doi.org/10.5194/jsss-9-167-2020
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