This paper is a contribution to the discussion on the possibilities of identification of the time behaviour of various stages of fatigue lifetime directly during experiments using non-destructive testing methods (mainly acoustic emission method). We focus especially on detection and characterization of the transition from the stage of cycling damage accumulation to micro-cracks creation. In the past years, the laboratory of fatigue properties of FME received and published plenty of information about propagation of damage mainly in Al and Ti alloys. In this paper are presented and compared the results of new experiments with steel used for nuclear reactors and Inconel alloy. This work is focused on comparison of selected parameters of acoustic emission signal in pre-initiation and initiation stage of fatigue crack creation. Acoustic emission hits with higher stress were detected in the pre-initiation stage whereas the initiation stage hits exhibited low stress

The integration of acoustic emission (AE) signals into adaptive control systems for CNC wood milling represents a promising advancement in intelligent manufacturing. This study investigated the feasibility of using AE signals for the real-time monitoring and control of CNC milling processes, focusing on medium-density fiberboard (MDF) as the workpiece material. AE signals were captured using dual-channel sensors during side milling on a five-axis CNC machine, and their characteristics were analyzed across varying spindle speeds and feed rates. The results showed that AE signals were sensitive to changes in machining parameters, with higher spindle speeds and feed rates producing increased signal amplitudes and distinct frequency peaks, indicating enhanced cutting efficiency. The statistical analysis confirmed a significant relationship between AE signal magnitude and cutting conditions. However, limitations related to material variability, sensor configuration, and the narrow range of process parameters restrict the broader applicability of the findings. Despite these constraints, the results support the use of AE signals for adaptive control in wood milling, offering potential benefits such as improved machining efficiency, extended tool life, and predictive maintenance capabilities. Future research should address signal variability, tool wear, and sensor integration to enhance the reliability of AE-based control systems in industrial applications.

This paper demonstrates the relationship between acoustic emission (AE) method parameters and loaded pneumatic cylinders. The cylinders in our experiment were loaded gradually by different weights in a vertical direction. The effect of the defect occurs when the cylinder is loaded at the retreat and progress strokes. This defect affects the relationship between the applied load and the recorded signal of the sensors. The signals of the acoustic emission were recorded from the progress and retreat strokes and then analysed. The time delay is calculated between the digital input and the initiation of movement, and the time of the stroke. The energy and root mean square of the acoustic emission compare between the distinctive different responses in damaged and undamaged pneumatic cylinders, with and without loading. The energy and RMS were calculated for each cylinder with gradual loading. The results of the test showed approximate linear relationship between the RMS and loading. This paper is a continuation of a previous article and the extension of my work to find distinctive differences that determine whether the cylinder is damaged or undamaged.