Research Progress of Machine Vision-Based Gluing Performance Measurement Technology
Adhesives can make small-sized wood material unit into wood-based panels,plywood,and glued wood forms,which are used in engineering materials such as wood-frame buildings,load-bearing walls,decorative flooring,and bridges.The important basis for evaluating the gluing ability of adhesives is the gluing performance,which mainly includes the distribution of glue application,penetration ability,shear strength test,peeling test,wood breakage percentage measurement,and other indicators.With the popularization of machine vision technology,it has been applied as an important technique to assist in the evaluation of gluing performance.In order to have a more comprehensive understanding of the gluing performance measurement technology of machine vision,this paper outlines the image acquisition technology,image pre-processing technology,image segmentation technology,and calculation methods of machine vision technology on the basis of collecting domestic and foreign gluing performance measurement technology research literature.Secondly,it discusses the application of machine vision technology in the three aspects of adhesive sizing,adhesive permeability,and wood breakage percentage of glued surfaces.At the same time,the problem that machine vision technology can not segment digital image accurately,resulting in poor measurement accuracy is discussed.Finally,the optimization method of adhesive property measurement technology based on machine vision technology is summarized,and future research prospects are proposed:1)to achieve accurate identification of adhesives and wood materials on adhesive joints,especially the identification of light-colored adhesives and substrates,especially the glue points and small wood units of particleboard and fiberboard;2)Deeply explore the pixel-level unit characteristics of different measurement objects and the measurement mechanism of machine vision;3)Research on scientific,sound and systematic evaluation mechanism based on machine vision technology;4)Explore the influence mechanism of environmental factors such as illumination conditions,acquisition equipment and sample placement on image quality;5)Establish a gluing performance evaluation database to provide data sources for deep learning or in-depth analysis of gluing performance.
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