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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. [ABSTRACT FROM AUTHOR]

The wood industry demands advanced methods for material classification and workpiece characteristic modelling to enhance process monitoring and adaptive process control. This paper presents a sensor fusion approach that integrates data from acoustic emissions, airborne sound, and power consumption during the milling of solid wood and wood-based composites. The aims are to achieve accurate material classification and to model workpiece characteristics such as surface roughness or density. A design matrix was generated by extracting relevant features from the multimodal signals to serve as an input for the classification and regression algorithms. The tested classification approaches to differentiate between workpiece type demonstrated high precision with an average validation accuracy of 92.16 %. Regression models for predicting the surface roughness showed R 2 values between 0.79 and 0.97. The density could be predicted with R2 values between 0.84 and 0.98. As a conclusion, workpiece types could be classified and important workpiece properties during machining, such as surface roughness and density, could be well described by using information from multiple sensors during machining. [ABSTRACT FROM AUTHOR]

When using a force measuring unit, measurement errors occur because of deformations of the cover plate due to different force application points in the workspace. This paper deals with the question to what extent machine learning approaches are suitable to compensate such measurement errors. Two approaches, the deep neural network and multiple linear regression, are investigated. The programs created receive as input the measurement signals of a force measuring unit equipped with strain gauges and return as output estimated values for the acting force vector. Finally, the results of the two algorithms used are compared. [ABSTRACT FROM AUTHOR]