Cylindrical Fitting Research Articles

Extraction of complex pipeline features from incomplete point clouds

Abstract Control point detection in industrial pipelines, characterized by flanges
and multiple passes, is critical for accurate virtual simulations and quality assessments
in manufacturing. This paper introduces an innovative method for detecting control
points in complex pipelines using incomplete point clouds, significantly streamlining
the process. Our approach uniquely requires only straight sections and end-plane
localizations as inputs, markedly reducing both data acquisition and processing times.
We develop a robust feature descriptor to align the CAD model with incomplete
point clouds, facilitating semantic automatic segmentation despite the lack of explicit
semantic information. Following this, geometric primitives are fitted to the segmented
clouds, and a cylindrical fitting algorithm tailored for incomplete data is introduced.
The control points are computed based on the relative positions and geometric
parameters of these primitives. Our method has been validated through experiments
on several real-world industrial complex pipelines. The results confirm that our
approach achieves a high measurement accuracy of 0.067 mm, even with point cloud
incompleteness of up to 50%. These findings highlight the effectiveness of our method in
accurately determining the geometric parameters of complex pipelines and suggesting its
considerable potential for practical applications.

A comparative study on the effectiveness of pollutants control measures adopted in the steel industry to reduce workplace and environmental exposure: a case study

Our understanding of the environmental and occupational health implications of pollutants emitted in steel production is still lacking, despite the considerable amount of research devoted to this topic. Given the significance of steel recycling and the need to reduce greenhouse gas emissions, many steel factories are adopting electric arc furnace (EAF) technology. The use of a technological system designed for the capture of pollutants emitted through EAF steel production is highly ecological because of its utilization of iron scrap and low investment cost. Despite this, the main issue with the EAF is the environmental impact it poses, specifically the release of pollutants into the air, such as dust and organic substances, chlorinated dioxins and furans, dioxin-like polychlorinated biphenyls and brominated dioxins and furans. As a result, workers in this field have a considerable rate of morbidity. The main challenge for EAFs is to optimize the capture of powders produced during the techno-logical process, both from the EAF and the workplace. A state-of-the art solution for managing pollutants in modern steel manufacturing is highlighted in this paper, featuring a method used in Romania that employs the Best Available Techniques (BAT) reference document for iron and steel production to directly collect pollutants from the EAF. The system included a cylindrical fitting, a heat exchanger to cool the gases and a hood to collect contaminants. In comparison to other ventilation options, this equipment boasts lower investment and lower operational costs because of its effective and minimal air flow. Through the use of cutting-edge technology and progressive strategies, we can move closer toward our objective of a workplace free from injuries in the steel industry.

THREE-DIMENSIONAL MODELING OF SHRUBS BASED ON LIDAR POINT CLOUDS

Abstract. This paper proposes a method for constructing 3D models of shrubs with high accuracy from 3D point clouds acquired by terrestrial laser scanning (TLS). Since the shrub point cloud obtained by LiDAR scanning contains a large amount of redundant data, the focus of this method is to segment the branches and leaves of the shrub point cloud first, which can remove the noise and make the branch skeleton of the shrub stand out. Secondly, a triangulation network is constructed for the segmented branch points, and a minimum spanning tree (MST) is established from the triangulation network as the initial shrub skeleton of the input point cloud. Then, the redundant branches are removed by merging adjacent points and edges to simplify the initial skeleton. Finally, the 3D model of a shrub is constructed by a cylindrical fitting algorithm based on robust principal component analysis (RPCA). Experiments on different types of shrubs from different data sources demonstrate the effectiveness and robustness of the proposed method. The 3D models of shrubs can be further applied to the accurate estimation of shrub attributes and urban landscape visualization.

A method for obtaining maize phenotypic parameters based on improved QuickShift algorithm

Phenotype is an important bridge for studying the mechanism of “genotype-phenotype-environment”, and studying precise measurement of crop individual phenotypic information is of great significance for accelerating breeding processes and assisting in precision agriculture monitoring. The purpose of this study is to develop a clustering algorithm for corn population point clouds to accurately extract the three-dimensional morphology of individual crops. Firstly, this study used drones to obtain field corn data, reconstructed corn plant models based on the SfM algorithm, simplified and rotated point clouds, and extracted crop point clouds using methods based on color and distance thresholds. For density segmentation, this study improved the QuickShift method to segment individual corn crops. Color weights were added to the clustering density calculation process, and the original point cloud was projected onto a horizontal plane to calculate the density clustering core. Then, the remaining points were subjected to QuickShift clustering segmentation in 3D to obtain the point cloud data of individual corn plants. The use of cylindrical fitting and conditional Euclidean clustering algorithms to automatically segment point clouds of stems and leaves has achieved estimation of plant height, stem thickness, minimum bounding box volume per plant, and number of leaves. Finally, the true and measured values of plant height and stem thickness were compared. The results showed that the accuracy of the improved QuickShift method for segmentation was 93.10%, with an average measured plant height of 14.21 cm and an average stem diameter of 0.72 cm. The determination coefficient and root mean square error of the automatic and manual measurement results of plant height are 0.9186 and 0.5785 cm, respectively. The determination coefficients and root mean square errors of the automatic and manual measurement results of stem thickness are 0.8227 and 0.0574 cm, respectively. This study uses an improved QuickShift to cluster and segment corn point cloud data, achieving the segmentation of individual corn plants in the field, providing an automated, efficient, and low-cost solution for accurately measuring crop phenotypic information.

Mapping of Rubber Forest Growth Models Based on Point Cloud Data

The point cloud-based 3D model of forest helps to understand the growth and distribution pattern of trees, to improve the fine management of forestry resources. This paper describes the process of constructing a fine rubber forest growth model map based on 3D point clouds. Firstly, a multi-scale feature extraction module within the point cloud column is used to enhance the PointPillars learning capability. The Swin Transformer module is employed in the backbone to enrich the contextual semantics and acquire global features with the self-attention mechanism. All of the rubber trees are accurately identified and segmented to facilitate single-trunk localisation and feature extraction. Then, the structural parameters of the trunks calculated by RANSAC and IRTLS cylindrical fitting methods are compared separately. A growth model map of rubber trees is constructed. The experimental results show that the precision and recall of the target detection reach 0.9613 and 0.8754, respectively, better than the original network. The constructed rubber forest information map contains detailed and accurate trunk locations and key structural parameters, which are useful to optimise forestry resource management and guide the enhancement of mechanisation of rubber tapping.

A Novel Framework for Stratified-Coupled BLS Tree Trunk Detection and DBH Estimation in Forests (BSTDF) Using Deep Learning and Optimization Adaptive Algorithm

Diameter at breast height (DBH) is a critical metric for quantifying forest resources, and obtaining accurate, efficient measurements of DBH is crucial for effective forest management and inventory. A backpack LiDAR system (BLS) can provide high-resolution representations of forest trunk structures, making it a promising tool for DBH measurement. However, in practical applications, deep learning-based tree trunk detection and DBH estimation using BLS still faces numerous challenges, such as complex forest BLS data, low proportions of target point clouds leading to imbalanced class segmentation accuracy in deep learning models, and low fitting accuracy and robustness of trunk point cloud DBH methods. To address these issues, this study proposed a novel framework for BLS stratified-coupled tree trunk detection and DBH estimation in forests (BSTDF). This framework employed a stratified coupling approach to create a tree trunk detection deep learning dataset, introduced a weighted cross-entropy focal-loss function module (WCF) and a cosine annealing cyclic learning strategy (CACL) to enhance the WCF-CACL-RandLA-Net model for extracting trunk point clouds, and applied a (least squares adaptive random sample consensus) LSA-RANSAC cylindrical fitting method for DBH estimation. The findings reveal that the dataset based on the stratified-coupled approach effectively reduces the amount of data for deep learning tree trunk detection. To compare the accuracy of BSTDF, synchronous control experiments were conducted using the RandLA-Net model and the RANSAC algorithm. To benchmark the accuracy of BSTDF, we conducted synchronized control experiments utilizing a variety of mainstream tree trunk detection models and DBH fitting methodologies. Especially when juxtaposed with the RandLA-Net model, the WCF-CACL-RandLA-Net model employed by BSTDF demonstrated a 6% increase in trunk segmentation accuracy and a 3% improvement in the F1 score with the same training sample volume. This effectively mitigated class imbalance issues encountered during the segmentation process. Simultaneously, when compared to RANSAC, the LSA-RANCAC method adopted by BSTDF reduced the RMSE by 1.08 cm and boosted R2 by 14%, effectively tackling the inadequacies of RANSAC’s filling. The optimal acquisition distance for BLS data is 20 m, at which BSTDF’s overall tree trunk detection rate (ER) reaches 90.03%, with DBH estimation precision indicating an RMSE of 4.41 cm and R2 of 0.87. This study demonstrated the effectiveness of BSTDF in forest DBH estimation, offering a more efficient solution for forest resource monitoring and quantification, and possessing immense potential to replace field forest measurements.

3D Reconstruction of deformable linear objects based on cylindrical fitting

3D Reconstruction of deformable linear objects based on cylindrical fitting

Three-dimensional defects detection of high-voltage cable joint based on iterative residual fitting

Aiming at the three-dimensional (3D) defects detection of high-voltage cables joint, a method based on iterative residual fitting is proposed. The 3D point cloud data was obtained by reconstructing the laser line projected onto the surface of the high-voltage cable joint. In order to remove noise generated by reconstruction which is located above or below the curve, a simple and effective strategy that the noise can be filtered by the gradient change in the neighborhood is adopted. Then a continuous iterative residual fitting method is proposed to effectively reduce the interference of the defect area on the curve fitting accuracy, thereby obtaining the accurate equation expression of the cable surface curve. By comparing the residual value of the high-voltage cable surface points relative to the fitting curve, the points of the defect area are marked. Finally 3D defect area is extracted through segmentation and clustering. Experiments have verified the effectiveness of the proposed method on artificial standard size defects and real manual ground defects. The absolute error of the depth (height) of artificial standard size defects is less than 0.05 mm. Compared with the traditional cylindrical fitting method, it can be seen that the recognition rate of the proposed method for manually ground defects of high-voltage cable joint is much higher than that of cylinder fitting method.

Sagittal femoral condylar shape varies along a continuum from spherical to ovoid: a systematic review and meta-analysis.

Considerable anatomic variations of sagittal femoral condylar shape have been reported, with a continuum between spherical (or single-radius) and ovoid (or multi-radius) condyles. The purpose of this systematic review and meta-analysis was to critically appraise and synthesise the available literature on the sagittal femoral profile. The hypothesis was that studies would reveal considerable variability among individuals, but also in their methodology to quantify sagittal profiles. This systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. On 10 September 2021 two authors searched for Level I to IV studies that reported on the sagittal curvature of the medial and/or lateral femoral condyles using the MEDLINE®, EMBASE® and Cochrane Library. Results were summarised by tabulating means, standard deviations and/or ranges for the reported radii-of-curvature, or ellipsoidal semi-major and semi-minor lengths of the condyles. To quantify sagittal 'ovoidicity' and asymmetry, results were stratified according to coordinate reference frame (posterior condylar axis (PCA), clinical and surgical transepicondylar axis (cTEA and sTEA), unified sagittal plane (USP), or unclear) and summarised in forest plots as standardised mean differences (SMD). Thirty-eight articles wereeligible for full text extraction, quantifyingsagittal radii-of-curvature by best-fit circles (BFC), ellipsoids, polynomials, spherical or cylindrical fitting. Studies with clear definition of the measurement plane revealed that both condyles were generally ovoid, with considerably greater 'ovoidicity' at the medial condyle (SMD, 4.09) versus the lateral condyle (SMD, 3.33). In addition, distal condylar radii were greater medially when measured normal to the TEA (cTEA: SMD, 0.81; sTEA: SMD, 0.79), but greater laterally when measured in a USP (SMD, -0.83). Posterior condylar radii were greater laterally when measured in a USP (SMD, -0.60). Studies reported considerable variability of sagittal femoral condylar radii-of-curvature, which arenot incremental, but rathera continuum that ranges from spherical to ovoid. Although this variation could be accommodated by single-, dual- and multi-radii femoral components, a surgeon typically uses only one or two TKA designs. Hence, there is a risk of mismatch between the native and prosthetic sagittal profile that could result in mid-flexion ligament imbalance unless other parameters are changed. These findings support the drive towards patient-specific implants to potentially achieve accurate sagittal bone-implant fit through implant customisation. IV.

4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography

BackgroundBreakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial–temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting to spatially quantify several traits. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA).ResultsRoots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, several traits were computed. Two of them were accurately validated using the root digital twin as a ground truth against the cylindrical model: number of branches (RRMSE better than 9%) and volume, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001.ConclusionsThe experimental results support the viability of the developed methodology, being able to provide scalability to a comprehensive analysis in order to perform high throughput root phenotyping.

Cylindrical Similarity Measurement for Helices in Medium-Resolution Cryo-Electron Microscopy Density Maps.

Cryo-electron microscopy (cryo-EM) density maps at medium resolution (5-10 Å) reveal secondary structural features such as α-helices and β-sheets, but they lack the side chain details that would enable a direct structure determination. Among the more than 800 entries in the Electron Microscopy Data Bank (EMDB) of medium-resolution density maps that are associated with atomic models, a wide variety of similarities can be observed between maps and models. To validate such atomic models and to classify structural features, a local similarity criterion, the F1 score, is proposed and evaluated in this study. The F1 score is theoretically normalized to a range from zero to one, providing a local measure of cylindrical agreement between the density and atomic model of a helix. A systematic scan of 30,994 helices (among 3,247 protein chains modeled into medium-resolution density maps) reveals an actual range of observed F1 scores from 0.171 to 0.848, suggesting that the cylindrical fit of the current data is well stratified by the proposed measure. The best (highest) F1 scores tend to be associated with regions that exhibit high and spatially homogeneous local resolution (between 5 Å and 7.5 Å) in the helical density. The proposed F1 scores can be used as a discriminative classifier for validation studies and as a ranking criterion for cryo-EM density features in databases.

Six‐degree‐of‐freedom generalized displacements measurement based on binocular vision

Due to their large flexibility and small damping ratios, harmful vibrations could happen on cables under some external excitations, as for instance the rain–wind-induced vibration, which comes with a translational and rotational coupling. An experimental study of the phenomenon would be quite important in view of an appropriate design. However, the available rotational measurement approaches are not suitable for measuring the rotation of cable, because the installation of such devices alters the structural mechanical properties. Recently, low-cost vision-based structural displacement measurement methods have been widely used due to their flexible deployment and the noncontact measurement feature. But they cannot measure the rotational displacements directly. This paper proposes a rotational generalized displacement measurement method based on a binocular vision assisted by a coded cylinder. The three-dimensional coordinates of the coded points are acquired according to photogrammetry schemes. The cylinder centroid and its direction vector are obtained by a cylindrical fitting. Eventually, the translational and rotational generalized displacements are derived. In the cylinder fitting stage, one optimizes the cylinder model parameters via differential search. Experimental verifications are carried out to demonstrate that the proposed six-degree-of-freedom (6-DOF) generalized displacement measurement is feasible.

Fast Cylindrical Fitting Method Using Point Cloud’s Normals Estimation

Cylindrical fitting is an essential step in Large Process Pipeline’s measurement process, and precision of initial values of cylindrical fitting is a key element in getting a correct fitting result. In order to get well initial values, covariance matrixes of all points in cylinder’s three-dimensional laser scanning point cloud should be firstly established to estimate normals of all points, and then cylinder’s axis vector can be calculated by using least squares method. Secondly, remaining parameters’ initial values of the cylinder can be got by coordinate transformation. Finally, Levenberg-Marquardt algorithm is used in iterative optimization process to get fitting result by using the above values as initial values. Experiments demonstrate that this method can get precise initial values of cylindrical fitting and improve the accuracy and speed of cylindrical fitting.

Geometric Total Plaque Area Is an Equally Powerful Phenotype Compared With Carotid Intima-Media Thickness for Stroke Risk Assessment: A Deep Learning Approach

Currently, carotid intima-media thickness (cIMT) and geometric total plaque area (gTPA) are computed manually and thus are tedious and prone to interobserver and intraobserver variabilities. This study presents an intelligence-based automated deep learning (DL)–based technique for carotid wall interface detection, cIMT, and lumen diameter (LD) measurements, followed by a 3D cylindrical approach for TPA measurement. The observers were used for manual tracings of which were then used for the design of two DL-based systems. The DL boundaries for inner lumen wall and outer interadventitial borders were used for computing the cIMT and LD. Using cylindrical approach, we computed the gTPA. Furthermore, we compute the 10-year image-based cIMT and gTPA, using the progression rates. A total of 396 B-mode ultrasound right and left common carotid artery images were taken from 203 patients. The mean cIMT and gTPA using DL1 and DL2 is 0.91 mm, 20.52 mm2 and 0.88 mm, 19.44 mm2, respectively. The coefficient of correlation between gTPA and cIMT using DL1 and DL2 is 0.92 ( P &lt; .001) and 0.94 ( P &lt; .001), respectively. The area under the curve (AUC) for gTPA showed an improvement over cIMT by 14.36% and 12.57% for DL1 and DL2, respectively. The corresponding 10-year risk improvements were 9.09% and 6.26%. Our statistical significance tests successfully passed t test, Mann-Whitney, Wilcoxon, Kolmogorov-Smirnov, and Friedman. The study shows gTPA as an equally powerful carotid risk biomarker like cIMT. Given the cIMT and LD, cylindrical fitting is a fast method for gTPA measurements.

Vertical Optical Scanning with Panoramic Vision for Tree Trunk Reconstruction.

This paper presents a practical application of a technique that uses a vertical optical flow with a fisheye camera to generate dense point clouds from a single planimetric station. Accurate data can be extracted to enable the measurement of tree trunks or branches. The images that are collected with this technique can be oriented in photogrammetric software (using fisheye models) and used to generate dense point clouds, provided that some constraints on the camera positions are adopted. A set of images was captured in a forest plot in the experiments. Weighted geometric constraints were imposed in the photogrammetric software to calculate the image orientation, perform dense image matching, and accurately generate a 3D point cloud. The tree trunks in the scenes were reconstructed and mapped in a local reference system. The accuracy assessment was based on differences between measured and estimated trunk diameters at different heights. Trunk sections from an image-based point cloud were also compared to the corresponding sections that were extracted from a dense terrestrial laser scanning (TLS) point cloud. Cylindrical fitting of the trunk sections allowed the assessment of the accuracies of the trunk geometric shapes in both clouds. The average difference between the cylinders that were fitted to the photogrammetric cloud and those to the TLS cloud was less than 1 cm, which indicates the potential of the proposed technique. The point densities that were obtained with vertical optical scanning were 1/3 less than those that were obtained with TLS. However, the point density can be improved by using higher resolution cameras.

Non-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR

Global estimates of forest aboveground biomass and carbon storage have major discrepancies linked to limitations in tree-level biomass estimates. Robust allometric equations can improve biomass estimates; however, destructive sampling to measure single-tree biomass is expensive, challenging, and prone to measurement error. We present a method to efficiently and non-destructively estimate single-tree biomass from terrestrial LiDAR scan data and test the approach on 21 destructively-sampled lodgepole pine (Pinus contorta) trees. The approach estimates branch and foliage volume using voxelization and estimates trunk volume using a method developed in this study called the Outer Hull Model (OHM). The OHM iteratively fits convex hulls, accurately handles noisy scan data, and fits the true shape of the trunk rather than forcing a cylindrical fit. Volume from the LiDAR scans is converted to biomass using density values from the literature and from field sampling to assess model sensitivity to density values. Whole-tree aboveground biomass estimates derived from the LiDAR scans were nearly unbiased and agreed strongly with destructive sampling data (R2=0.98, RMSE=20.4kg). Estimation of the trunk component biomass (R2=0.99, RMSE=12.3kg) was stronger than foliage and needle component estimates (R2=0.54, RMSE=21.4kg). The approach presented in this study accurately and non-destructively estimated the aboveground biomass of needleleaf trees with minimal user input. The promising performance on coniferous trees advances efficient sampling of single-tree biomass.

Improved Quantification of Cerebral Vein Oxygenation Using Partial Volume Correction

Purpose: Quantitative susceptibility mapping (QSM) enables cerebral venous characterization and physiological measurements, such as oxygen extraction fraction (OEF). The exquisite sensitivity of QSM to deoxygenated blood makes it possible to image small veins; however partial volume effects must be addressed for accurate quantification. We present a new method, Iterative Cylindrical Fitting (ICF), to estimate voxel-based partial volume effects for susceptibility maps and use it to improve OEF quantification of small veins with diameters between 1.5 and 4 voxels.Materials and Methods: Simulated QSM maps were generated to assess the performance of the ICF method over a range of vein geometries with varying echo times and noise levels. The ICF method was also applied to in vivo human brain data to assess the feasibility and behavior of OEF measurements compared to the maximum intensity voxel (MIV) method.Results: Improved quantification of OEF measurements was achieved for vessels with contrast to noise greater than 3.0 and vein radii greater than 0.75 voxels. The ICF method produced improved quantitative accuracy of OEF measurement compared to the MIV approach (mean OEF error 7.7 vs. 12.4%). The ICF method provided estimates of vein radius (mean error <27%) and partial volume maps (root mean-squared error <13%). In vivo results demonstrated consistent estimates of OEF along vein segments.Conclusion: OEF quantification in small veins (1.5–4 voxels in diameter) had lower error when using partial volume estimates from the ICF method.

A robust cylindrical fitting to point cloud data

ABSTRACTEnvironmental, engineering and industrial modelling of natural features (e.g. trees) and man-made features (e.g. pipelines) requires some form of fitting of geometrical objects such as cylinders, which is commonly undertaken using a least-squares method that—in order to get optimal estimation—assumes normal Gaussian distribution. In the presence of outliers, however, this assumption is violated leading to a Gaussian mixture distribution. This study proposes a robust parameter estimation method, which is an improved and extended form of vector algebraic modelling. The proposed method employs expectation maximisation and maximum likelihood estimation (MLE) to find cylindrical parameters in case of Gaussian mixture distribution. MLE computes the model parameters assuming that the distribution of model errors is a Gaussian mixture corresponding to inlier and outlier points. The parameters of the Gaussian mixture distribution and the membership functions of the inliers and outliers are computed using an expectation maximisation algorithm from the histogram of the model error distribution, and the initial guess values for the model parameters are obtained using total least squares. The method, illustrated by a practical example from a terrestrial laser scanning point cloud, is novel in that it is algebraic (i.e. provides a non-iterative solution to the global maximisation problem of the likelihood function), is practically useful for any type of error distribution model and is capable of separating points of interest and outliers.

Comparison of three formal methods used to estimate the functional axis of rotation: an extensive in-vivo analysis performed on the knee joint

Estimating the main axis of rotation (AoR) of a human joint represents an important issue in biomechanics. This study compared three formal methods used to estimate functional AoR, namely a cylindrical fitting method, a mean helical axis transformation, and a symmetrical axis approach. These methods were tested on 106 subjects undergoing navigated total knee arthroplasty. AoR orientation in 3D and in the frontal and coronal planes provided by each method was compared to the transepicondylar axis direction. Although all the methods resulted effective, significant differences were identified among them, relatively to the orientation in 3D and in the frontal plane projection. This was probably due to the presence of secondary rotations during the first degrees of knee flexion.

Research of Advancing Mechanism with Three Circular Arc Equidistant Profile Connection of Drill

Study the existing advancing mechanism of drill, analyze the disadvantage of the flat key and the spline, and the feature of the three circular arc equidistant profile connection. Taking the common chain advancing mechanism as basis, using three circular arc equidistant curve to make sprocket bore and intermediate shaft external cylindrical fit constitute the three circular arc equidistant profile keyless connection. Then introduce the advancing mechanism with three circular arc equidistant profile connection of drill. Applying Solidworks2010, we created the three-dimensional modeling of the mechanism, which lay the foundation for the further research and application. The new type advancing mechanism has many advantages, such as automatic-centering accurately, convenient in assembling and disassembling, eliminating the stress concentration, simplicity of the fit section，transferring bigger torque, suitable for heavy-loaded rock drill machine.