Service provider for industrial computed tomography
The use of artificial intelligence in NDT
The use of artificial intelligence (AI) ensures efficiency, cost savings and error prevention in all application areas. These effects can also be achieved in the field of non-destructive testing (NDT) by means of automatic evaluation procedures in which components are tested for defects.
Methods which automatically evaluate data from non-destructive testing reach their limits in the defect testing of components due to many special cases and correspondingly complex algorithms - in many cases the results do not show a clearly definable separation between a "permissible" (IO) and a "prohibited" (NIO) component. A manual follow-up check must therefore often be carried out afterwards. The time and cost savings aimed at by the use of an automatic evaluation are only partially achieved or completely nullified.
A typical example of this is the automatic evaluation of a test according to a limit sample catalogue. Because they are relatively easy to create, boundary sample catalogues are well suited for defining the desired quality. However, the requirements described in this way can hardly be implemented in an evaluation algorithm. For the quality assessment by means of modern AI, boundary sample catalogues are almost optimally suited.
Machine learning is used in the NDT field for the detection of pores and burrs as well as for the evaluation of solder joints including a classification of components in IO/ NIO. It is also possible to quantify the size of disparities.
Solder joint evaluation - component IO
Solder joint evaluation - component NIO
Your advantages with us
We offer you a fast, automated AI procedure that is highly robust against fluctuations in the measurement process.
We are able to teach our machine learning approach flexibly and adapt it to different customer requirements.
How do we proceed?
In a first conversation we ask you for an exact description of the problem, e.g. a limit sample catalogue or similar. On the basis of this information we can also tell you whether our AI procedure is suitable for your request.
If no pictures are available, it is best to send us sample parts from which we can create matching pictures.
For many people, machine learning is a process that seems too abstract and can therefore arouse distrust. However, it has been proven that results obtained with artificial intelligence are equivalent or even better than those obtained with manual testing, as the error rate is reduced. However, automatic procedures must also be subjected to random sampling and compared with reference data in order to guarantee the quality of the evaluation or to adjust it if necessary.