Gongcheng Kexue Yu Jishu/Advanced Engineering Science

The fast growth in the scale of large-scale data systems in sectors like cybersecurity, finance, healthcare, and industrial surveillance has enhanced the necessity of powerful anomaly detecting methods that can operate without indicated data. This paper explores the use of unsupervised machine learning to detect anomalies in high-dimensional and large-scale unhomogeneous data. Four exemplary algorithms, namely: Isolation Forest, One-Class Support Vector Machine (OC-SVM), Local Outlier Factor (LOF) and Autoencoder neural networks were evaluated systematically on several large-scale datasets with network traffic, transactional and sensor records. The experimental findings proved that deep learning-based Autoencoders had a top overall detection performance with an average precision of 93.6, recall of 90.8, F1-score of 92.2, and AUC of 0.96, which showed they were most effective in identifying non-linear patterns involving complex physiological behavior. The Isolation Forest was also particularly well performing obtaining an F1-score of 89.8% with much lower training time (71 seconds on 500k records) and much lower detection latency (1.8 ms per instance), which was similar to real time applications. Conversely, LOF displayed lower scalability and performance breakdown in a high-dimensional environment as well as OC-SVM. The proposed framework is competitive or better with other related work of recent interest and, therefore, can be compared with other comparable studies. On the whole, this study can contribute to practical understanding of the choice of unsupervised techniques of anomaly detection regarding the scale of a system, the nature of collected data, and the limitations of its operation.

Drilling super duplex stainless steel (SDSS 2507) is challenging because of its high strength and poor thermal conductivity, which lead to excessive heat generation during machining. In conventional flood cooling, the generated heat is not sufficiently dissipated, and prolonged exposure to cutting fluids can pose health risks to operators. Cryogenic cooling, particularly using liquid nitrogen (LN₂), offers an effective alternative for machining hard-to-cut materials. This study evaluates the drilling performance of a multilayer coated tool (AlCrN + TiN) on SDSS 2507 under dry and cryogenic LN2 environments. Experiments were carried out at different cutting speeds (40, 50, and 60 m/min) and feed rates (0.04, 0.06, and 0.08 mm/rev), while maintaining a constant drilling depth of 20 mm. The influence of cooling conditions on thrust force, cutting temperature, surface roughness, micro-structural characteristics, and tool wear was systematically examined. The results indicate that cryogenic drilling significantly improves machining performance compared to dry drilling. Reductions of up to 40% in thrust force, 52% in cutting temperature, and 52% in surface roughness were achieved under cryogenic conditions. Micro-structural analysis confirmed improved surface integrity, and tool wear was notably lower when LN2 was used, demonstrating the effectiveness of cryogenic cooling for drilling SDSS 2507.