Automation is an inevitable trend in the development of tunnel shotcrete machinery. Tunnel environmental perception based on 3D LiDAR point cloud has become a research hotspot. Current researches about the detection of tunnel point clouds focus on the completed tunnel with a smooth surface. However, few people have researched the automatic detection method for steel arches installed on a complex rock surface. This paper presents a novel algorithm to extract tunnel steel arches. Firstly, we propose a refined function for calibrating the tunnel axis by minimizing the density variance of the projected point cloud. Secondly, we segment the rock surface from the tunnel point cloud by using the region-growing method with the parameters obtained by analyzing the tunnel section sequence. Finally, a Directed Edge Growing (DEG) method is proposed to detect steel arches on the rock surface in the tunnel. Our experiment in the highway tunnels under construction in Changsha (China) shows that the proposed algorithm can effectively extract the points of the edge of steel arches from 3D LiDAR point cloud of the tunnel without manual assistance. The results demonstrated that the proposed algorithm achieved 92.1% of precision, 89.1% of recall, and 90.6% of the F-score.
Carbon nanotubes (CNTs) have high photothermal conversion efficiency and hence can be used to prepare lightabsorbing nanocomposites with polymers. In this study, an interesting type of nanocomposites based on the biomass-derived poly(Lmalic acid) (PLMA) filled with CNTs were developed. CNTs were grafted with PLMA oligomers and then participated in the crosslinking of the PLMA. The as-obtained biocomposites show highly improved mechanical strengths and good biocompatibility with decreased rates of degradation. The elastoplastic response was then used as a probe to detect relations between the dispersion of CNTs and the relaxations of cross-linking networks. As a green and light-absorbing material, the cross-linked PLMA/CNT nanocomposite possesses good photoabsorption efficiency and photothermal conversion stability. The shape-memory behavior of the cross-linked PLMA matrix is activated then, and the recovery process can be controlled step by step. Using this kind of biocomposites, intelligent devices with programmable photothermal responses and tunable material performance can be easily fabricated.
NiCr/AgVO 3 self-lubricating composite was prepared by powder cold-pressed method with the NiCr alloy as the matrix and 10 wt.% additive of AgVO 3 as solid lubricant. The AgVO 3 additive powder was synthesized by the precipitation method which exhibits a melting point of 460 • C. Microstructure, phase composition and thermal properties of the AgVO 3 powder, as well as the composite of NiCr/AgVO 3 were analyzed using scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD) and differential scanning calorimeter (DSC). The friction and wear behavior of the specimens from room temperature (R.T.) to 800 • C was evaluated using a ball-on-disk tribometer and 3D white light interference (WLI). The results showed that the friction coefficient of this material under atmosphere decreases with temperature increasing from R.T. to 800 • C. However, the wear rate firstly increases from R.T. to 200 • C, almost remains stable from 200 • C to 600 • C, and then decreases with further increasing the temperature up to 800 • C. It is also found that the prepared composite materials show a better frictional behavior than NiCr alloy over the whole range of temperatures, which is mainly attributed to solid lubrication of AgVO 3 exhibiting a lamella-slip structure at temperatures below 460 • C and forms liquid-film at elevated temperatures above the melting point.
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