With digital x-ray detectors and highly automated inspection systems, manufacturers can dramatically optimize their quality control and assurance processes. However, due to the higher part throughput, the evaluation and interpretation of x-ray images can become an expensive bottleneck.
Advanced technologies such as automated fault detection (ADR) and artificial intelligence (AI) have the potential to dramatically reduce the cycle time required per component. Depending on the test standard and requirements, the algorithms can be implemented as a support system for the operator or fully automatically.
Machine learning as a source of automatic fault recognition is important for NDT industry as it is a data driven trend and NDT industry provides much of the data. Recreating the data and using it to improve processes and quality has traditionally meant putting the image on a screen, taking time, looking at it, and then making a decision. Now, with the addition of NDT 4.0, it is possible to make even more semi-automatic decisions. It is a process of going from manual to a system that automatically recognizes the fault.
Under the aegis of the Fourth Industrial Revolution, also known as NDT 4.0, new technologies such as artificial intelligence (AI), cloud computing, IIOT, simulation and big data allow another increase significant efficiency. The availability of precise, low-maintenance algorithms for Automatic Error Detection (ADR) helps operators make better decisions in less time. Digitization and smart data assessment strategies have the potential to contribute to serious improvements in testing processes.
Data-driven decisions – Allows the creation of significant statistics on component faults. Acquire knowledge on the distribution of defects (type, size, etc.) and create your own digital defect catalogs.
Automated assessment – Increases the efficiency of quality assurance by evaluating and interpreting industrial x-ray images using artificial intelligence.
Process information – Learn more about the performance and quality of your production processes and use this knowledge to optimize processes through a real-time feedback loop.
Inspection quality – An assistance system can help the operator to make decisions in order to increase the probability of detection and the overall reliability of the test.
The availability of precise, low-maintenance algorithms for Automatic Error Detection (ADR) can help operators make better decisions in less time. Digitization and smart data assessment strategies have the potential to contribute to serious improvements in testing processes.
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