High-speed milling is often modeled as a kind of highly interrupted machining, when the ratio of time spent cutting to not cutting can be considered as a small parameter. In these cases, the classical regenerative vibration model, playing an essential role in machine tool vibrations, breaks down to a simplified discrete mathematical model. The linear analysis of this discrete model leads to the recognition of the doubling of the so-called instability lobes in the stability charts of the machining parameters. This kind of lobe-doubling is related to the appearance of period doubling vibrations originated in a flip bifurcation. This is a new phenomenon occurring primarily in low-immersion high-speed milling along with the Neimark-Sacker bifurcations related to the classical self-excited vibrations or Hopf bifurcations. The present work investigates the nonlinear vibrations in the case of period doubling and compares this to the well-known subcritical nature of the Hopf bifurcations in turning processes. The identification of the global attractor in the case of unstable cutting leads to contradiction between experiments and theory. This contradiction draws the attention to the limitations of the small parameter approach related to the highly interrupted cutting condition.

In the paper the results of the acoustic emission (AE) based detection and characterization of stress-corrosion cracking (SCC) in stainless steel are presented. As supportive methods for AE interpretation, electrochemical noise, specimen elongation measurements, and digital imaging of the specimen surface were used. Based on the defined qualitative and quantitative time and power spectra characteristics of the AE bursts, a manual and an automatic procedure for the detection of crack related AE bursts were introduced. The results of the analysis of the crack related AE bursts indicate that the AE method is capable of detecting large scale cracks, where, apart from intergranular crack propagation, also some small ductile fractures occur. The sizes of the corresponding ductile fracture areas can be estimated based on a relative comparison of the energies of the detected AE bursts. It has also been shown that AE burst time and power spectra features can be successfully used for the automatic detection of SCC.