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Non-destructive testing of rail tracks is carried out by using rail inspection cars equipped with ultrasonic and eddy current measurement. The evaluation of test data is mainly done manually, supported by a software tool which pre-selects relevant indications shown to the evaluators. The resulting indications have to be checked on-site using hand-held testing equipment. Maintenance interventions are then derived on the basis of these on-site findings.
Overall aim of the AIFRI (Artificial Intelligence For Rail Inspection) project - funded by the German Federal Ministry of Digital and Transport (BMDV) as part of the mFUND programme under funding code 19FS2014 – is to increase the degree of automation of the inspection process from the evaluation of the data to the planning of maintenance interventions. The accuracy of defect detection shall be increased by applying AI methods in order to enable an automated classification of detected indications into risk classes. For this purpose, data from both eddy current inspections and ultrasonic inspections will be used in combination.
Within the framework of this data-driven project, relevant defect patterns and artefacts present in the rail are analysed and implemented into a configurable digital twin. With the help of this digital twin virtual defects can be generated and used to train AI algorithms for detection and classification. With the help of reliability assessment trained AI algorithms will be evaluated with regard to the resulting quality in defect detection and characterisation.
A particular aspect of the development of AI methods is the data fusion of different NDT data sources: Thereby, synergies are used that arise from linking eddy current and ultrasonic inspection data in a combined model.
In the course of the project a demonstrator consisting of the developed IT-tool and an asset management system will be implemented and tested in the field using real-world data.
Spray drying based granulation processes aim for flowable granules neither containing voids nor hard shells thus leading to a homogenous microstructure in the green and sinter bodies without strength reducing large pores. The increase of the specific surface area due to the utilization of finer powders as raw materials makes the production of granules of demanded quality more sophisticated. Innovations regarding additives as well as process engineering are therefore required.
While conventional spray granulation processes of ceramic materials are based on rotational, one stream or two stream nozzles for nebulization, the investigations in the ZIM project concentrate on the applicability of an ultra sound atomizer unit. A spray dryer comprised of the aforementioned ultra sound atomization unit implemented in a commercial spray dryer (Niro, Denmark) was used as test system. Potential advantages of the ultra sound nebulization are investigated for model systems of alumina, zirconia and a ZTA composite while focusing on solids content, yield, pressability and granule properties (size, size distribution, flowability, shape and microstructure) as well as the final sinter body properties (density, microstructure and flexural strength).
First ultra sound spray drying experiments yielded granules with excellent processability. Spray drying of identical slurries, as before tested and optimized for a two stream nozzle atomization process, resulted in a more suitable size distribution for dry pressing (less particles below 20 µm) and a higher yield. Furthermore, sinter bodies produced of ultra sound granules seem to have less large pores and a more homogenous microstructure