<p>Machine learning models enable automated process monitoring in manufacturing and support adaptive quality assurance and predictive maintenance systems. Autoencoders have emerged as a promising approach to unsupervised anomaly detection in processes. However, their application to turning processes, particularly their generalizability across different process variants such as varying machine kinematics, cutting geometries, and process variability, remains largely unexplored. Existing studies mainly focus on controlled laboratory environments using single machine tools and standardized parameters, limiting industrial scalability. This work addresses this gap by systematically comparing long short-term memory (LSTM) and convolutional (CNN) based autoencoders developed in collaboration with two research projects. The models are trained and evaluated on two harmonized datasets derived from orthogonal and longitudinal turning processes. The datasets differ with respect to machine tools, kinematics, and cutting tools. Each model is trained on both its native process data and on the respective other dataset to evaluate cross-domain performance. The results on normalized reconstruction errors show that LSTM-based autoencoders yield higher temporal sensitivity, whereas CNN-based autoencoders exhibit greater reconstruction stability but reduced sensitivity to transient dynamics. Moreover, relative reconstruction errors are additionally evaluated in the original physical force units to improve process interpretability. This analysis reveals larger relative deviations for LSTM models due to their heightened sensitivity. Furthermore, the variability of the training dataset is identified as a key factor influencing out-of-distribution generalization performance. The findings demonstrate the generalizability of autoencoder based monitoring approaches across different cutting conditions and provide insights for data-driven and grey-box modeling frameworks in manufacturing.</p>