A Study on Minimizing Measurement Time Based on Active Experimentation for Energy dispersive X-Ray Diffraction

Particularly in laboratory XRD measurements, where the intensities in diffraction experiments tend to be low, an adaption of the exposure time to the investigated microstructure is crucial. Measurement times that are too short result in poor signal-to-background ratios or dominant signal noise, making subsequent evaluation more difficult or even impossible. Then, it is necessary to repeat measurements with adjusted, usually significantly longer measurement time. Additionally, the effort is increasing with the total number of measuring points. To prevent redundant measurements, it is state-of-the-art to use the full measurement range regardless of whether the measurement points are relevant and contribute to the subsequent materials characterization. Examples for such cases are texture and residual stress measurements. Since the first evaluation steps following the measurement are standardized procedures, they provide an interesting approach for intelligent methods directly embedded in the measurement sequence. In the present study, different approaches are investigated that analyze the continuously growing data set during an energy dispersive diffraction measurement on a complex application like shown in. Different selection strategies are proposed that intelligently choose the next point of investigation by means of key characteristics of prior acquired data. It is shown that such strategies are able to significantly minimize the required measurement time according to the material's microstructure without losing data quality for subsequent analyses, thus, open up the possibility for in process active experimental design.