https://www.selleckchem.com/products/JNJ-26481585.html The fatigue life of the tested material was detected using a device designed by us. The measurement results were processed in the form of the Wöhler's S-N curves (alternating stress versus number cycles to failure) and compared with the current regulations issued by the International Institute of Welding (IIW) in the form of the FAT curves (IIW fatigue class). The achieved research results indicate that the modern welding technologies (laser and electron beams) used on the high-strength steel had no principal influence on the fatigue life of the tested material.Process monitoring is a critical task in ensuring the consistent quality of the final drug product in biopharmaceutical formulation, fill, and finish (FFF) processes. Data generated during FFF monitoring includes multiple time series and high-dimensional data, which is typically investigated in a limited way and rarely examined with multivariate data analysis (MVDA) tools to optimally distinguish between normal and abnormal observations. Data alignment, data cleaning and correct feature extraction of time series of various FFF sources are resource-intensive tasks, but nonetheless they are crucial for further data analysis. Furthermore, most commercial statistical software programs offer only nonrobust MVDA, rendering the identification of multivariate outliers error-prone. To solve this issue, we aimed to develop a novel, automated, multivariate process monitoring workflow for FFF processes, which is able to robustly identify root causes in process-relevant FFF features. We demonstrate the successful implementation of algorithms capable of data alignment and cleaning of time-series data from various FFF data sources, followed by the interconnection of the time-series data with process-relevant phase settings, thus enabling the seamless extraction of process-relevant features. This workflow allows the introduction of efficient, high-dimensional monitoring