Abstract
Abstract The aim of this research was to explore the potential of acoustic impact test to evaluate the condition of hardwood logs in regard to internal decay, void, crack and defect ratio using an acoustic signal separation and enhancement algorithm. Longitudinal acoustic signals were obtained from 15 logs of four hardwood species through acoustic impact testing. The defect components were separated from the acoustic response signals and enhanced based on the autoregressive minimum entropy deconvolution (AR-MED) method, and from which the kurtosis was derived and used as the global feature parameter for evaluating the internal condition of logs. Compared with the acoustic velocity obtained directly from the original signal, the kurtosis was deemed to be a more powerful predictor of log defect ratio with higher coefficient of determination (R 2 = 0.89) and was not affected by log species. To identify the type of defects, a complex Morlet wavelet-based spectral kurtosis (SK) method was proposed. The research results indicated that the SK can not only determine the type and primary and secondary major defects, but also be able to identify those that were not detectable by global acoustic parameters.
Highlights
Hardwood logs are ideal raw materials for making highvalue wood products such as furniture, flooring, and veneer because of its rich texture and colors and high mechanical properties
To demonstrate the effectiveness of the autoregressive minimum entropy deconvolution (AR-Minimum entropy deconvolution (MED)) algorithm in enhancing the mutation signal, the kurtosis of the residual signal filtered by the AR model and that of the output filtered by the AR-MED were compared using acoustic signal of log no.14 as an example (Figure 2)
With respect to the non-stationary and characteristic overlap of the response signals resulting from the acoustic impact testing of hardwood logs, a method of kurtosis extraction based on AR-MED was proposed to evaluate the internal quality of hardwood logs in terms of defect ratios and kurtosis
Summary
Hardwood logs are ideal raw materials for making highvalue wood products such as furniture, flooring, and veneer because of its rich texture and colors and high mechanical properties. The severity and frequency of defects vary widely within species, and even within the same tree Detection of these internal defects in hardwood logs could provide significant benefits to wood industry in terms of saving transportation cost, reducing wood waste, and increasing production efficiency; and to achieve optimal wood utilization and maximize the profits (Edlund et al 2006; Fischer et al 2015; Wang et al 2009; Xu et al 2018). Another potential market for hardwood materials is building industry. If information of internal defects in hardwood logs (such as location, type, and size of defects) is known and utilized effectively during the sawing process, the lumber value can be increased by 10–21% (Steele et al 1994; Thomas 2010)
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