Using artificial intelligence strategies for process-related automated inspection in the production environment

2016 Joint IMEKO TC1-TC7-TC13 Symposium: Metrology Across the Sciences: Wishful Thinking?

Anding, K.^1 ; Kuritcyn, P.^1 ; Garten, D.^2

Ilmenau University of Technology, Gustav-Kirchhoff-Platz 2, Ilmenau

98693, Germany^1

GFE Schmalkalden E.V., Näherstiller Straße 10, Schmalkalden

98574, Germany^2

关键词: Automated inspection;    Automatic visual inspection;    Classifying method;    Color and texture features;    Convolutional Neural Networks (CNN);    Metallic surface;    Network parameters;    Production environments;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/772/1/012026/pdf
DOI  :  10.1088/1742-6596/772/1/012026

来源: IOP

【 摘 要 】

In this paper a new method for the automatic visual inspection of metallic surfaces is proposed by using Convolutional Neural Networks (CNN). The different combinations of network parameters were developed and tested. The obtained results of CNN were analysed and compared with the results of our previous investigations with color and texture features as input parameters for a Support Vector Machine. Advantages and disadvantages of the different classifying methods are explained.