With digital X-ray detectors and highly automated inspection systems, manufacturers can significantly optimize their quality control and assurance processes. However, due to the higher throughput of parts, the evaluation and interpretation of X-ray images can become an expensive bottleneck. Advanced technologies such as automated defect detection (ADR) and artificial intelligence (AI) have the potential to significantly reduce the cycle time required per component. Depending on the test standard and requirements, the algorithms can be implemented as an assistance system for the operator or fully automatically.
Machine learning as a supply for automatic defect recognition is important to the NDT sector because it is a data driven trend and the NDT sector supplies a lot of the data. To recreate the data and use it to improve processes and quality traditionally means putting the image on a screen, take some time, look at it and then make a decision. Now with the add of NDT 4.0 allows the making of even more semi-automatic decisions. It is a process to switch from manual to a system that automatically recognize the defect…
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