Three-Dimensional Reconstruction Method for Machined Surface Topography Based on Gray Gradient Constraints

Abstract

Featured Application: Applying on mechanical engineering. Abstract: In the modern engineering field, recovering the machined surface topography is important for studying mechanical product function and surface characteristics by using the shape from shading (SFS)-based reconstruction method. However, due to the limitations of many constraints and oversmoothing, the existing SFS-based reconstruction methods are not suitable for machined surface topography. This paper presents a new three-dimensional (3D) reconstruction method of machined surface topography. By combining the basic principle of SFS and the analytic method, the analytic model of a surface gradient is established using the gray gradient as a constraint condition. By efficiently solving the effect of quantization errors and ambiguity of the gray scale on reconstruction accuracy using a wavelet denoising algorithm and image processing technology, the reconstruction algorithm is implemented for machined surface topography. Experimental results on synthetic images and machined surface topography images show that the proposed algorithm can accurately and efficiently recover the 3D shape of machined surface topography.Appl. Sci. 2019, 9, 591 2 of 16 wear particle surfaces based on a photometric stereo, and obtained the surface topographies of wear particles for further feature-based wear particle identification. Tang et al. [9] used the 3D reconstruction method based on the shape from a focus to determine the grinding wheel surface topography.Three-dimensional reconstruction is the process of recovering 3D shapes of objects from single or multiple images [10][11][12], and the shape from shading (SFS) method is considered to be one of the fastest and most efficient 3D reconstruction methods [13][14][15]. Therefore, the SFS-based reconstruction method has attracted attention from numerous researchers. Maurer et al. [16] used a method that combined SFS and stereo to establish the functional energy with a detail-preserving anisotropic second-order smoothness term, and estimated the depth of the object surface. Lu et al. [17] proposed a Lambert-Phong hybrid model algorithm, and obtained the coordinate information of the highlighted region of the droplet surface by the mask regions with convolutional neural network (R-CNN). Furthermore, they used the Taylor expansion and Newton iteration method in the reflection model to obtain the heights of all the positions. Sun et al. [18] proposed a new roughness evaluation method based on the complex wavelet enhanced SFS transform, and recovered the 3D topography of a milling surface using the 3D models that were developed from the digital image. Zhu et al. [19] proposed a novel method that uses Fourier transform to fuse the depth from focus (DFF) and SFS, and achieved the reconstruction of the tool wear image in addition to ensuring the continuity of the depth map. Yang et al. [20] used the SFS algorithm to reconstruct the 3D shapes of the welding seam, and extracted the curvature information as the feature vector of the ...

Influence of Tool Inclination and Effective Cutting Speed on Roughness Parameters of Machined Shaped Surfaces

“…Likewise, a deeper analysis and the ability to identify defects on the machined surface allow understanding of the surface characteristics of mechanical parts, such as lubrication, friction, wear, or corrosion resistance. Compared to the results of the author Shi [45], who dealt with the three-dimensional reconstruction of the topography of the machined surface based on gray gradient constraints, the applied method of evaluating surface roughness using Zeiss Smartproof 5 is equally suitable for monitoring the production and processes. In comparison with the author Shi, we expanded in our research and analyzed more deeply the defects formed on the surface of the machined surfaces, which were mainly shown when the tool was oriented perpendicularly in the cutting process.…”

Evaluation of the Feasibility of the Prediction of the Surface Morphologiesof AWJ-Milled Pockets by Statistical Methods Based on Multiple Roughness Indicators

“…Patel and Kiran [24] have proposed an image-based technique for surface roughness estimation by correlating roughness parameters with image characteristics. Shi et al [25] have used a shading-based surface reconstruction technique and surface gradient model, especially for the simulation of machined surface topography. Dhanasekar and Ramamoorthy [26] have developed surface quality enhancement algorithms in order to evaluate the surface topography from images and compared the results withexperimental data.…”