NDT 4.0 - Digital transformation and its impact on NDT

In the age of Industry 4.0, numerous new challenges and tasks await the »NDT of the future«, also known as »NDT 4.0« or »NextGen NDT«.

The expectations for NDT also rises in response to the growing complexity of connected networks from consumer electronics, business software or production (IoT). This applies for all kind of »4.0« methods like Big Data or Artifacial intelligence.

At the Fraunhofer Institute for Nondestructive Testing IZFP, Saarbrücken, the further development of NDT systems towards intelligent and IIoT-capable NDT sensor systems for NDT 4.0, based on eddy current, ultrasound and 3MA is therefore the subject of ongoing research and development. These Sensor Systems transport relevant information into the digital product memory along every phase of the product value creation cycle as well as the product life cycle. The focus is particularly on interfaces using OPC-UA as well as modern documentation options by connecting the systems to DICONDE servers, thus enabling their integration into existing IIoT networks. The combination of several technologies to solve a inspection task is also possible.

With the help of these technologies and the existing know-how of Fraunhofer IZFP in the field of non-destructive testing technology, new possibilities are offered with regard to digitalization, optimization as well as modernization of existing inspection tasks. In addition, this approach supports the development and application of »hybrid« inspection technologies, i.e. the best possible results can be achieved through the combined application of several NDT methods.

Areas of application can be found wherever existing infrastructure is being expanded, modernized or redesigned. OPC-UA and DICONDE ensure essential prerequisites for quality assurance in production facilities of any kind. In the context of integration into Industry 4.0, both offer an optimal basis for intelligent, networked production. Only NDT 4.0 will provide Industry 4.0 with the relevant data on material, product and process changes and thus achieve digitally optimized process chains in the future.