Cross-grain fracture characterization in softwood using artificial neural network analysis of acoustic emissions

Author:

Belalpour Dastjerdi Parinaz,Landis Eric N.

Funder

Directorate for Engineering

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Plant Science,General Materials Science,Forestry

Reference34 articles.

1. ASTM D442-16, (2016) Standard test methods for direct moisture content measurement of wood and wood-base materials. Tech. rep, American Society for Testing and Materials

2. Badgujar TY, Wani VP (2022) Wavelet transform and mel-frequency cepstral coefficient-based feature extraction of the sheet metal trimming process to study burr formation. Int J Mechatron Manufact Syst 15(1):20–36

3. Baensch F, Sause MG, Brunner AJ et al (2015) Damage evolution in wood-pattern recognition based on acoustic emission (ae) frequency spectra. Holzforschung 69(3):357–365. https://doi.org/10.1515/hf-2014-0072

4. Belalpour Dastjerdi P, Landis EN (2021) Growth ring orientation effects in transverse softwood fracture. Materials 14(19):5755. https://doi.org/10.3390/ma14195755

5. Bodig JJB (1993) Mechanics of Wood and Wood Composites. Krieger Publishing Company, Malabar, FL

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