Research On Digital Images Measurement Technology Of Wood Failure Percentage Of Glued Laminated Timber Adhesive Bonded Joints Based On Machine Vision

Wood failure percentage(WFP)is an important indicator to characterize the gluing performance of glued laminated wood beams and structural glulam,and the accurate and intelligent measurement of wood breakage rate is of great significance to the high-quality development of laminated wood.The development of machine vision and its intelligent technology provides a new development idea to achieve accurate and intelligent measurement of WFP.In this thesis,based on the systematic study of digital image pixel statistics and machine learning WFP measurement technology,a deep learning-based WFP measurement technology is proposed,which effectively solves the bottleneck of intelligent measurement of glued laminated wood failure location,but is influenced by the scene.On this basis,an optimization technique of digital image intelligent recognition of WFP by direct staining of adhesive is further proposed to improve the measurement accuracy and adaptability of WFP measurement by machine vision technology,and the research results provide methodological guidance for the optimal design of glued laminated wood beams as well as integrated lumber gluing for structures.(1)The digital characterization results of the pixel statistics-based WFP measurement technique are in good agreement with the actual wood failure area.the measured values of the technique in eight scenarios are not significantly different from the actual values,and the maximum absolute and relative errors are 3.6 and 12.2%,respectively,which can be applied to actual production.However,scene changes have an impact on the measurement accuracy of the technique,in descending order:adhesive type>shear type>specimen type.Also,the measurement error is caused by subjective factors such as the emotion of the measurement personnel,but the technique has a small measurement error in scenarios with a small number of measurements.(2)The numerical characterization results of the machine-learning-based WFP measurement technique are in poor agreement with the actual wood failure area.the measured values of the technique in eight scenarios are significantly different from the actual values,and the relative error in one scenario is 23.5%higher than 20%,which cannot be applied in actual production,indicating that the scenario changes have an impact on the measurement accuracy of the technique,in descending order:adhesive type>Shear type>specimen type.However,the technique also has certain advantages in that it can be applied to many measurement scenarios and can directly obtain the WFP.(3)The model type of the WFP measurement technology based on deep learning has a greater impact on the segmentation ability of the gluing failure surface image,in which the UNet model has a better segmentation effect and is more suitable for measuring the WFP.there are large significant differences between the measured and actual values of the technology in eight scenes,such as the relative error of three scenes is greater than 20%,which cannot be applied in actual production,indicating that the scene change has a The measurement accuracy is relatively poor,but the measurement accuracy of the technique is mainly affected by scene changes,in descending order:adhesive type>specimen type>shear type.Finally,compared with the WFP measurement technique based on manual visual inspection,the measurement speed of the WFP measurement technique based on deep learning is 108.5 times higher;the coefficient of variation is 0,which indicates that the technique has the advantages of high measurement efficiency and high stability.(4)The optimization technique of WFP based on machine vision improved the digital image segmentation ability and measurement accuracy of three machines vision-based WFP measurement techniques.the maximum relative errors of the three machine vision techniques in eight scenarios were 3.1,7.8,and 13.8,respectively,indicating that the optimization technique has a large optimization effect and the maximum measurement accuracy was improved by 4.2,8.7,and 14.34 times.Secondly,the MDI adhesive dyeing treatment had no significant effect on the adhesive gluing performance.