In-process monitoring of inhomogeneous material characteristics based on machine learning for future application in additive manufacturing

Additively manufactured components often show insufficient component quality due to the formation of different defects. Defects such as porosity result in material inhomogeneity and structural integrity issues. The integration of in-process monitoring in machining processes facilitates the identification of inhomogeneity characteristics in manufacturing, which is crucial for process adaptation. The incorporation of artificial defects in components has the potential to mimic and study the behaviour of real-world defects in a more controlled way. This study highlights the potential benefits of cutting force and vibration monitoring during machining operations with the goal of providing insights into the machining behaviours and the effects of the artificially introduced defects on the process. Detection of anomalies relies on identifying changes in force profiles or vibration patterns that might indicate the interaction between the tool and the defect. Machine learning algorithms were used to process and interpret the collected data. The algorithms are trained to recognize patterns, anomalies, or deviations from expected behaviours, which can aid in evaluating the effect of detected defects on the machining process and the resultant component quality. The main objective of this study is to contribute to enhancing quality control of machining processes for inhomogeneous materials.