scholarly journals Optimization of Reverse Engineering Data for Regeneration of Triangular Mesh

2021 ◽  
Author(s):  
Ashish Deb
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Reverse Engineering ◽  
Surface Reconstruction ◽  
Mesh Generation ◽  
Triangular Mesh ◽  
Popular Method ◽  
Engineering Data ◽  
Simple Genetic Algorithm ◽  
Data Points

Surface reconstruction of 3D reverse engineering data through the application of a triangulated mesh is a popular method. This thesis proposes a new simple genetic algorithm, an artificial intelligence method, to optimize triangular mesh generation which reduces the number of data points required to depict an object without sacrificing the details and accuracy.

scholarly journals Optimization of Reverse Engineering Data for Regeneration of Triangular Mesh

2021 ◽  
Author(s):  
Ashish Deb
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Reverse Engineering ◽  
Surface Reconstruction ◽  
Mesh Generation ◽  
Triangular Mesh ◽  
Popular Method ◽  
Engineering Data ◽  
Simple Genetic Algorithm ◽  
Data Points

Surface reconstruction of 3D reverse engineering data through the application of a triangulated mesh is a popular method. This thesis proposes a new simple genetic algorithm, an artificial intelligence method, to optimize triangular mesh generation which reduces the number of data points required to depict an object without sacrificing the details and accuracy.

Iterative Genetic-Algorithm-Based Surface Reconstruction Method for Repair of Twisted Blade

2014 ◽  
Vol 722 ◽  
pp. 125-130 ◽  
Author(s):  
Hai Dong Wu ◽  
Jie Dong Chen
Keyword(s):  
Genetic Algorithm ◽  
Surface Reconstruction ◽  
Complex Surface ◽  
Reconstruction Method ◽  
Control Points ◽  
Least Squares Approximation ◽  
B Spline ◽  
Spline Surface ◽  
Data Points ◽  
Twisted Blade

When remanufacturing complex surface parts, such as twisted blade, it is difficult to obtain an accurate model. An iterative Genetic-algorithm-based-surface reconstruction method for repair of twisted blade is presented. Genetic algorithm is applied in parametrizing data points and computing knot vectors. Then, the control points of the fitting B-spline surface are calculated by least-squares approximation through either SVD or LU methods. It shows that the accuracy of the method is improved significantly when three different twisted blades surfaces are verified by using the method.

Modeling and Numerical Control Machining of the Mould of a Rearview Mirror of a Motorcycle Based on the Reverse Engineering Technology

2012 ◽  
Vol 215-216 ◽  
pp. 664-668
Author(s):  
Yong Xiang Gao
Keyword(s):  
Reverse Engineering ◽  
Surface Reconstruction ◽  
Coordinate Measuring Machine ◽  
Numerical Control ◽  
Engineering Technology ◽  
Numerical Control Machining ◽  
Modeling Environment ◽  
Data Points ◽  
Measuring Machine ◽  
Rearview Mirror

The Reverse Engineering Technology (RET) is extensively employed in the realm of product designing. In this paper, a Three-coordinate Measuring Machine is utilized first to measure the data points of the rearview mirror of a motorcycle, then under UG modeling environment, surface reconstruction is conducted, and lastly procedures like mold splitting and mold core machining are finished upon exercising the Moldwizard Module of UG software. In short, the application of the RET greatly shortened the period of product designing and manufacturing.

Head Surface Reconstruction and Correction of Reverse Engineering Based on CATIA's

2011 ◽  
Vol 189-193 ◽  
pp. 1575-1579
Author(s):  
San Gang Yao ◽  
Hang Li ◽  
Dong Hong Si ◽  
Yu Jun Xue ◽  
Ji Shun Li
Keyword(s):  
Reverse Engineering ◽  
Data Acquisition ◽  
Surface Reconstruction ◽  
Human Head ◽  
Processing Theory ◽  
Head Surface ◽  
Data Points ◽  
Cam Software

In this paper the implementation procedure of Human Head curve reconstitution has been investigated and the curve reconstitution model has been built based on reverse engineering and pre-processing theory of data-points. The data acquisition of head curve was implemented by means of the laser scan system. The data-points were disposed, and the head curve was reconstructed, and the error and smoothness were analyzed by means of high-end CAD / CAE / CAM software CATIA V5. Those laid the foundation for late further research surface reconstruction.

EXPRESS: Baseline Correction Based on a Search Algorithm from Artificial Intelligence

2020 ◽  
pp. 000370282097751
Author(s):  
Xin Wang ◽  
Xia Chen
Keyword(s):  
Artificial Intelligence ◽  
Objective Function ◽  
Least Squares ◽  
Least Squares Method ◽  
Search Algorithm ◽  
Absolute Error ◽  
Search Problem ◽  
Baseline Correction ◽  
Current Spectrum ◽  
Data Points

Many spectra have a polynomial-like baseline. Iterative polynomial fitting (IPF) is one of the most popular methods for baseline correction of these spectra. However, the baseline estimated by IPF may have substantially error when the spectrum contains significantly strong peaks or have strong peaks located at the endpoints. First, IPF uses temporary baseline estimated from the current spectrum to identify peak data points. If the current spectrum contains strong peaks, then the temporary baseline substantially deviates from the true baseline. Some good baseline data points of the spectrum might be mistakenly identified as peak data points and are artificially re-assigned with a low value. Second, if a strong peak is located at the endpoint of the spectrum, then the endpoint region of the estimated baseline might have significant error due to overfitting. This study proposes a search algorithm-based baseline correction method (SA) that aims to compress sample the raw spectrum to a dataset with small number of data points and then convert the peak removal process into solving a search problem in artificial intelligence (AI) to minimize an objective function by deleting peak data points. First, the raw spectrum is smoothened out by the moving average method to reduce noise and then divided into dozens of unequally spaced sections on the basis of Chebyshev nodes. Finally, the minimal points of each section are collected to form a dataset for peak removal through search algorithm. SA selects the mean absolute error (MAE) as the objective function because of its sensitivity to overfitting and rapid calculation. The baseline correction performance of SA is compared with those of three baseline correction methods: Lieber and Mahadevan–Jansen method, adaptive iteratively reweighted penalized least squares method, and improved asymmetric least squares method. Simulated and real FTIR and Raman spectra with polynomial-like baselines are employed in the experiments. Results show that for these spectra, the baseline estimated by SA has fewer error than those by the three other methods.

scholarly journals Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tiziana Ciano ◽  
Massimiliano Ferrara ◽  
Meisam Babanezhad ◽  
Afrasyab Khan ◽  
Azam Marjani
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Velocity Profile ◽  
Fluid Velocity ◽  
Accurate Prediction ◽  
Inference System ◽  
Porous Tube ◽  
Intelligence Algorithm ◽  
Water Based ◽  
Artificial Intelligence Algorithm

AbstractThe heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

Generation of a Complete Profile for Porosity Log While Drilling Complex Lithology by Employing the Artificial Intelligence

2021 ◽  
Author(s):  
Ahmed Al-Sabaa ◽  
Hany Gamal ◽  
Salaheldin Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Prediction Model ◽  
Real Time ◽  
Storage Capacity ◽  
Data Set ◽  
Drilling Parameters ◽  
Unseen Data ◽  
Rock Porosity ◽  
Data Points ◽  
Logging Tool

Abstract The formation porosity of drilled rock is an important parameter that determines the formation storage capacity. The common industrial technique for rock porosity acquisition is through the downhole logging tool. Usually logging while drilling, or wireline porosity logging provides a complete porosity log for the section of interest, however, the operational constraints for the logging tool might preclude the logging job, in addition to the job cost. The objective of this study is to provide an intelligent prediction model to predict the porosity from the drilling parameters. Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q). The novel contribution of this study is to provide a rock porosity model for complex lithology formations using drilling parameters in real-time. The model was built using 2,700 data points from well (A) with 74:26 training to testing ratio. Many sensitivity analyses were performed to optimize the ANN model. The model was validated using unseen data set (1,000 data points) of Well (B), which is located in the same field and drilled across the same complex lithology. The results showed the high performance for the model either for training and testing or validation processes. The overall accuracy for the model was determined in terms of correlation coefficient (R) and average absolute percentage error (AAPE). Overall, R was higher than 0.91 and AAPE was less than 6.1 % for the model building and validation. Predicting the rock porosity while drilling in real-time will save the logging cost, and besides, will provide a guide for the formation storage capacity and interpretation analysis.

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