Significance
Engineering materials exhibit various properties, including brittleness. At the microscopic scale, brittle materials are highly susceptible to failure initiated by crack initiation, propagation, and coalescence. Thus, it is imperative to analyze the cracks to ensure high structural integrity. Currently, the acoustic emission technique has been widely used to study the formation and growth of cracks in brittle materials and predict the safety and performance of engineering structures. Acoustic emission can be classified into a waveform-based approach and parameter-based approach/parameter analysis. However, parameter analysis is commonly used owing to its convenience and less time-consuming. Besides, it uses two key parameters: RA value and average frequency, used as a criterion for classifying crack modes. Unfortunately, parameter analysis in crack classification is mostly based on the empirical relation between the parameters, which may not provide the desired accuracy. Moreover, the optimal transition line between shear and tensile cracks have not been clarified.
Recent research revealed that improving crack classification based on parametric analysis requires effective methods for determining the optimal ratio of the RA value and average frequency. To this note, Dr. Zheng-Hu Zhang from the Dalian University of Technology, in collaboration with Professor Jian-Hui Deng from Sichuan University developed a new method for determining the crack transition line for crack classification in acoustic emission parameter analysis. The novel approach was based on the statistical analysis of the dominant frequency characteristics of acoustic emission signals. The aim was to combine the advantages of waveform and parametric analysis to improve the accuracy and efficiency of acoustic emission crack analysis. Their work is currently published in the International Journal of Rock Mechanics and Mining Sciences.
In their approach, four types of rocks: marble, diorite, fine-grained granite, and coarse-grained granite, were utilized as specimens. First, the four specimens were subjected to unconfined compression tests, and their acoustic emissions were monitored. Next, the optimal transition line in the acoustic emission parameter analysis was determined via statistical analysis of dominant frequency characteristics of AE waveforms. Lastly, the feasibility of the proposed method was verified by comparing the crack modes acquired by the proposed method with those obtained by the polarity method.
The authors determined the predicted ratios of both tensile and shear cracks for a series of different transition lines. Also, the proportions of waveforms distributed in the low and high dominant frequency bands were determined. Specifically, the optimal transition line was reported to correspond to the least square difference between the measurements and predicted data. From the laboratory tests, the optimal line in the parameter analysis of brittle rocks subjected to compression was observed to lie in the range 1:100 to 1:500. Different rock types exhibited different optimal transition lines, which could be attributed to the difference in the microstructure and mineral composition.
In summary, the study proposed a new method, based on a statistical analysis of dominant frequency characteristics, for determining the crack classification criterion in acoustic emission parameter analysis. Combining the merits of waveform and parameter analysis, the proposed method significantly improved the accuracy and efficiency of acoustic emission classification of cracks as well as structural monitoring and damage analysis. In a statement to Advances in Engineering, the authors said their study would improve crack classification, thus enhancing the performance and longevity of engineering structures.
Reference
Zhang, Z., & Deng, J. (2020). A new method for determining the crack classification criterion in acoustic emission parameter analysis. International Journal of Rock Mechanics and Mining Sciences, 130, 104323.
Go To International Journal of Rock Mechanics and Mining Sciences