Chemie und Prozesstechnik
Filtern
Erscheinungsjahr
- 2022 (3) (entfernen)
Dokumenttyp
- Vortrag (3) (entfernen)
Sprache
- Englisch (3) (entfernen)
Referierte Publikation
- nein (3)
Schlagworte
- Machine learning (3) (entfernen)
Organisationseinheit der BAM
- 1 Analytische Chemie; Referenzmaterialien (2)
- 1.6 Anorganische Referenzmaterialien (2)
- 3 Gefahrgutumschließungen; Energiespeicher (1)
- 3.1 Sicherheit von Gefahrgutverpackungen und Batterien (1)
- 8 Zerstörungsfreie Prüfung (1)
- 8.4 Akustische und elektromagnetische Verfahren (1)
- P Präsident (1)
- P.0 Präsident und andere (1)
- S Qualitätsinfrastruktur (1)
- S.1 Qualität im Prüfwesen (1)
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.
Glow discharge optical emission spectroscopy (GD-OES) is a technique for the analysis of solids such as metals, semiconductors, and ceramics. A low-pressure glow discharge plasma is applied in this system, which ‘sputters’ and promotes the sample atoms to a higher energy state. When the atoms return to their ground state, they emit light with characteristic wavelengths, which a spectrometer can detect. Thus, GD-OES combines the advantages of ICP-OES with solid sampling techniques, which enables it to determine the bulk elemental composition and depth profiles. However, direct solid sampling methods such as glow-discharge spectroscopy require reference materials for calibration due to the strong matrix effect.
Reference materials are essential when the accuracy and reliability of measurement results need to be guaranteed to generate confidence in the analysis. These materials are frequently used to determine measurement uncertainty, validate methods, suitability testing, and quality assurance. In addition, they guarantee that measurement results can be compared to recognized reference values. Unfortunately, the availability of certified reference materials suited to calibrate all elements in different matrix materials is limited. Therefore various calibration strategies and the preparation of traceable matrix-matched calibration standards will be discussed.
Machine learning is an essential component of the growing field of data science. Through statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. Therefore, it was tried in our work to combine GD-OES with machine learning strategies to establish a new and robust calibration model, which can be used to identify the elemental composition and concentration of metals from a single spectrum. For this purpose, copper reference materials from different manufacturers, which contain various impurity elements, were investigated using GD-OES. The obtained spectra information are evaluated with different algorithms (e.g., gradient boosting and artificial neural networks), and the results are compared and discussed in detail.
High-resolution absorption isotopic spectrometry as a tool for aging studies of Li-ion batteries
(2022)
Lithium (Li) is the key element in the manufacturing of batteries. Isotopic study of Li may help to identify the causes of battery aging due to isotopic fractionation during charge/discharge cycles.
Isotopic ratio determination is based on monitoring the isotopic components of lithium by their spin-orbit coupling and its isotopic shift of about 15 pm for the 22P←22S electronic transition around 670.788 nm. In this work, we propose improvements to our previous work [1] by using a higher-resolution double echelle modular spectrometer (HR-DEMON II) coupled to a continuum source graphite furnace atomic absorption spectrometer (HR-CS-GF-AAS) for the isotopic analysis of Li.
The data analysis was carried out by using a decision-tree-based ensemble machine learning (ML) algorithm (XGBoost). A set of samples with 6Li isotope amount fractions ranging from 0.0004 to 0.99 mol mol-1 was used for the algorithm's training. Subsequently, the procedure was validated by a set of stock chemicals (Li2CO3, LiNO3, LiCl, and LiOH) and a BAM candidate reference material, a cathode material (NMC111). Finally, the ML model was applied to determine the isotope ratio of geological samples, including anorthosite, granite, soil, rhyolite, nepheline syenite, and basalt and battery samples. These samples were measured as digested without any further purification step.
Improvements in the optical resolution resolve the lithium isotopic components of the atomic spectra. In the studied geological samples, were found δ7Li values between -0.5 and 4.5 ‰ with a precision range of 1 to 2 ‰. In addition, the proposed method was validated with multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS), and these results are comparable and compatible.