3D Web Reconstruction of a Fibrous Filter Using Sequential Multi-Focus Images

et al. 2023

Polymers

As a type of fiber system, nonwoven fabric is ideal for solid–liquid separation and air filtration. With the wide application of nonwoven filter materials, it is crucial to explore the complex relationship between its meso structure and filtration performance. In this paper, we proposed a novel method for constructing the real meso-structure of spun-bonded nonwoven fabric using computer image processing technology based on the idea of a “point-line-body”. Furthermore, the finite element method was adopted to predict filtration efficiencies based on the built 3D model. To verify the effectiveness of the constructed meso-structure and simulation model, filtration experiments were carried out on the fabric samples under different pollution particle sizes and inlet velocities. The experimental results show that the trends observed in the simulation results are consistent with those of the experimental results, with a relative error smaller than 10% for any individual datum.

Section: Image Processing Methodsmentioning

confidence: 99%

Three-Dimensional Modeling of Spun-Bonded Nonwoven Meso-Structures

et al. 2023

Polymers

“…The proposed 3D reconstruction method is implemented on real milling tool tip objects, and its performance is compared with some SFF methods including the Laplacian-based operators (Flp) [28]、the tenengrad-based operators (Ften) [29]、the gradient-based operators (Fmean) [30]、the Fourier-based operators (Ffft) [31]、the wavelet-based operators (Fdwt) [32], and the NSST-based operators (Fnsstmdml) [33]. These methods are the most widely used focus measure operators to estimate image depth.…”

Section: Comparison With Sff Methodsmentioning

confidence: 99%

Volume monitoring of the milling tool tip wear and breakage based on multi-focus image three-dimensional reconstruction

Peng¹,

Qin²,

et al. 2023

Preprint

In precision machining, the milling tool’ geometry has a great influence on the milled surface quality. The research on milling tool state monitoring was mainly based on one-dimensional signals and two-dimensional images, which could indirectly obtain the tool state and wear area, but it could not provide the volume of milling tool wear and breakage area, thereby making it difficult to achieve quantitative analysis tool wear. This paper proposed a three-dimensional (3D) reconstruction method of the milling tool tip, it could build a 3D model of the milling tool tip, and then the volume of the wear and breakage region of the milling tool tip was extracted by the 3D model. Firstly, the focusing degree of image sequence’s pixels was calculated based on the non-subsampled discrete shearlet transform (NSST) and Laplace algorithm, and the 3D reconstruction of the milling tool tip was completed according to the shape-from-focus (SFF) principle; secondly, the depth values were optimized by fitting the focusing degree curve of pixels in the image sequence with Gaussian function; finally, the volume of the 3D point cloud of the milling tool tip was calculated by the Simpson double numerical integration method, and the material loss in the damaged region could be obtained. In the 3D reconstruction experiment of the milling tool tip, comparing the different focus degree evalution operators of SFF, the 3D point cloud obtained by this paper's proposed 3D reconstruction method has the least noise and the best performance in the root-mean-square error, correlation, and smoothness indexes. In addition, compared with Genmagic software, the 3D point cloud volume calculation method adopted in this paper could accurately calculate the 3D point cloud volume of the milling tool tip, and the percentage error was less than 1%.

Section: Comparison With Sff Methodsmentioning

confidence: 99%

Volume monitoring of the milling tool tip wear and breakage based on multi-focus image three-dimensional reconstruction

Peng

Qin

Int J Adv Manuf Technol

et al. 2023

In precision machining, the milling tool' geometry has a great influence on the milled surface quality. The research on milling tool state monitoring was mainly based on one-dimensional signals and two-dimensional images, which could indirectly obtain the tool state and wear area, but it could not provide the volume of milling tool wear and breakage area, thereby making it difficult to achieve quantitative analysis tool wear. This paper proposed a three-dimensional (3D) reconstruction method of the milling tool tip, it could build a 3D model of the milling tool tip, and then the volume of the wear and breakage region of the milling tool tip was extracted by the 3D model. Firstly, the focusing degree of image sequence's pixels was calculated based on the non-subsampled discrete shearlet transform (NSST) and Laplace 2 algorithm, and the 3D reconstruction of the milling tool tip was completed according to the shape-from-focus (SFF) principle; secondly, the depth values were optimized by fitting the focusing degree curve of pixels in the image sequence with Gaussian function; finally, the volume of the 3D point cloud of the milling tool tip was calculated by the Simpson double numerical integration method, and the material loss in the damaged region could be obtained. In the 3D reconstruction experiment of the milling tool tip, comparing the different focus degree evalution operators of SFF, the 3D point cloud obtained by this paper's proposed 3D reconstruction method has the least noise and the best performance in the root-mean-square error, correlation, and smoothness indexes. In addition, compared with Genmagic software, the 3D point cloud volume calculation method adopted in this paper could accurately calculate the 3D point cloud volume of the milling tool tip, and the percentage error was less than 1%.