Engineering Applications of Artificial Intelligence
A non-conventional quality control system to detect surface faults in mechanical front seals
Section snippets
Introduction and methodology
This study aims to develop a quality control system to detect some particular faults, currently not recognized by the deterministic system already implemented by the Italian manufacturer “Meccanotecnica Umbra S.p.A”, that affect mechanical frontal seals. Consequently success relative to the detection of these faults means a direct improvement as compared with the methodology already implemented.
Therefore, the analysis is focused on the three particular faults currently not recognized and listed
Input parameters
As can be noticed from the images shown in Fig. 2, the surface area to analyze is dimensioned to be comparable with the dimensions of common faults, so that the surface imperfection meaningfully affects the statistical parameters extrapolated from such a zone.
Therefore, the images are acquired as RGB (1600×1200 pixels) and then converted to gray scale. On the basis of these data, it was noted that the presence of typical faults on the surfaces taken in consideration causes a sensible variation
Container bottom surface analysis
In this case the training set collects 20 images of good exemplars and 20 images of seals with scratches on the container's bottom surface. The unsuitable seals were placed in front of the camera so that the scratches were all along the analysis area. The testing set collects 20 images of good seals and 10 images of scratched ones. This set was used to test the performances of the trained neural system.
As already said, the input array collects 25 elements per image; consequently 25 neurons make
Container lateral surface analysis
The system implemented to analyze the bottom metal surface of the container was adapted to control the quality of the lateral one. As already said, it is necessary to identify the lack of seal on this surface. Even in this case a training set and a testing one were collected.
In reference to the ANN architecture of Fig. A.1 in Appendix A, the best grade of training convergence was reached for the configuration 25-30-20-10-1. After 30 000 epochs and a training time of 15 min, SSE and RMS values
Carbon surface analysis
Finally, the system was adapted to identify imperfections on the carbon surface of the rings that are the internal elements of mechanical seals. A training set of 40 (20 OK–20 NOK) images and a testing one constituted of 27 (18 OK–9 NOK) exemplars were collected.
In this case the configuration, characterized by a number of neurons in the input, inner and output layers of 25-30-30-10-1, guarantees the best grade of training convergence after 30 000 training epochs (convergence time of 15 min). The
System performance adding artificial noise to input images
As discussed in Section 1, all the images object of this study were acquired with a common camera and, therefore, already characterized by an intrinsic noise in comparison with the ones which in the implementation phase of the quality control system will be acquired by means of a suitable vision system.
Anyway, in order to carry out a sensitivity analysis of system according to the quality of the acquired images, it is interesting to verify the performances in the case of additional noise
Conclusions
Applied to all classes of analyzed defects, the implemented method achieves high level of accuracy for not-high quality input images and also in the case of supplementary low-level noise introduction. In fact, as seen in the previous paragraphs, the neural classifier, applied to the three defects (lack of seal, scratches on the metal surface and carbon ring defects), is able to separate 100% of faulty elements from the good ones. One more important aspect to underline is that, once the system
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