The Model for Estimate of Cylindrical Machined Surfaces Parameters

2015 ◽  
Vol 770 ◽  
pp. 579-584
Author(s):  
Vadim Andreevich Pechenin ◽  
Michael Alexandrovich Bolotov ◽  
M.V. Yanyukina
Keyword(s):  
Surface Roughness ◽  
Objective Function ◽  
Point Cloud ◽  
Approximation Problem ◽  
Optimization Methods ◽  
Geometrical Parameters ◽  
Approximation Model ◽  
Method Of Least Squares ◽  
Coordinate Measurements ◽  
Nonlinear Optimization Methods

In the paper we propose an approximation model of the measured point cloud for machined cylindrical surfaces. The developed model is different from the traditional view because it relies on the contact of mating surfaces. The model allows to determine the parameters of cylindrical surfaces such as the coordinates of the center and the radius. To evaluate the geometrical parameters of the model the objective function is formulated, which characterizes the nature of the interface and a number of limitations. The approximation problem is solved by use of nonlinear optimization methods. The model takes into account the surfaces contact, which influences in deformation of its irregularities. The irregularities it is form deviations and surface roughness. The application of the model on the example of the shaft coupling with the inner ring of the bearing. There is given an estimate of geometric parameters for interfacing compared to method of least squares, which is widely used for coordinate measurements. As a result of research it was obtained that the proposed method can predict more accurately the parameters, which describe the location of the cylindrical surfaces made during assembly.

scholarly journals Optimization of the cross-sectional shape of the belts of trihedral lattice supports

2019 ◽  
Vol 7 (4) ◽  
pp. 5-8
Author(s):  
Linar Sabitov ◽  
Ilnar Baderddinov ◽  
Anton Chepurnenko
Keyword(s):  
Objective Function ◽  
Nonlinear Optimization ◽  
Cross Section ◽  
Optimization Methods ◽  
Cross Sectional ◽  
Maximum Value ◽  
Axial Moment ◽  
The Cross ◽  
Nonlinear Optimization Methods ◽  
Sectional Shape

The article considers the problem of optimizing the geometric parameters of the cross section of the belts of a trihedral lattice support in the shape of a pentagon. The axial moment of inertia is taken as the objective function. Relations are found between the dimensions of the pentagonal cross section at which the objective function takes the maximum value. We introduce restrictions on the constancy of the consumption of material, as well as the condition of equal stability. The solution is performed using nonlinear optimization methods in the Matlab environment.

Color Reproduction Accuracy Promotion of 3D-Printed Surfaces Based on Microscopic Image Analysis

2019 ◽  
Vol 34 (01) ◽  
pp. 2054004 ◽  
Author(s):  
Xiaochun Wang ◽  
Chen Chen ◽  
Jiangping Yuan ◽  
Guangxue Chen
Keyword(s):  
Surface Roughness ◽  
Image Analysis ◽  
Three Dimensional ◽  
Prediction Method ◽  
Chromatic Aberration ◽  
Optimization Methods ◽  
Experimental Models ◽  
Microscopic Image ◽  
Color Reproduction ◽  
Microscopic Image Analysis

Full-color three-dimensional (3D) printing technology is a powerful process to manufacture intelligent customized colorful objects with improved surface qualities; however, poor surface color optimization methods are the main impeding factors for its commercialization. As such, the paper explored the correlation between microstructure and color reproduction, then an assessment and prediction method of color optimization based on microscopic image analysis was proposed. The experimental models were divided into 24-color plates and 4-color cubes printed by ProJet 860 3D printer, then impregnated according to preset parameters, at last measured by a spectrophotometer and observed using both a digital microscope and a scanning electron microscope. The results revealed that the samples manifested higher saturation and smaller chromatic aberration ([Formula: see text]) after postprocessing. Moreover, the brightness of the same color surface increased with the increasing soaked surface roughness. Further, reduction in surface roughness, impregnation into surface pores, and enhancement of coating transparency effectively improved the accuracy of color reproduction, which could be verified by the measured values. Finally, the chromatic aberration caused by positioning errors on different faces of the samples was optimized, and the value of [Formula: see text] for a black cube was reduced from 8.12 to 0.82, which is undetectable to human eyes.

A More Effective Formulation of the Path Generation Mechanism Synthesis Problem

1994 ◽  
Author(s):  
Irfan Ullah ◽  
Sridhar Kota
Keyword(s):  
Objective Function ◽  
Error Function ◽  
Mathematical Optimization ◽  
Optimization Methods ◽  
Complex Nature ◽  
Mechanism Synthesis ◽  
Structural Error ◽  
Crank Angle ◽  
The Common ◽  
Selection Of

Abstract Use of mathematical optimization methods for synthesis of path-generating mechanisms has had only limited success due to the very complex nature of the commonly used Structural Error objective function. The complexity arises, in part, because the objective function represents not only the error in the shape of the coupler curve, but also the error in location, orientation and size of the curve. Furthermore, the common introduction of timing (or crank angle), done generally to facilitate selection of corresponding points on the curve for calculating structural error, has little practical value and unnecessarily limits possible solutions. This paper proposes a new objective function, based on Fourier Descriptors, which allows search for coupler curve of the desired shape without reference to location, orientation, or size. The proposed objective function compares overall shape properties of curves rather than making point-by-point comparison and therefore does not requires prescription of timing. Experimental evidence is provided to show that it is much easier to search the space of the proposed objective function compared to the structural error function.

OPTIMAL PREDICTION AND DESIGN OF SURFACE ROUGHNESS FOR CNC TURNING OF AL7075-T6 BY USING THE TAGUCHI HYBRID QPSO ALGORITHM

2016 ◽  
Vol 40 (5) ◽  
pp. 883-895 ◽  
Author(s):  
Wen-Jong Chen ◽  
Chuan-Kuei Huang ◽  
Qi-Zheng Yang ◽  
Yin-Liang Yang
Keyword(s):  
Surface Roughness ◽  
Particle Swarm Optimization ◽  
Predictive Models ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Cutting Parameters ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Cnc Turning ◽  
Multi Objective

This paper combines the Taguchi-based response surface methodology (RSM) with a multi-objective hybrid quantum-behaved particle swarm optimization (MOHQPSO) to predict the optimal surface roughness of Al7075-T6 workpiece through a CNC turning machining. First, the Taguchi orthogonal array L27 (36) was applied to determine the crucial cutting parameters: feed rate, tool relief angle, and cutting depth. Subsequently, the RSM was used to construct the predictive models of surface roughness (Ra, Rmax, and Rz). Finally, the MOHQPSO with mutation was used to determine the optimal roughness and cutting conditions. The results show that, compared with the non-optimization, Taguchi and classical multi-objective particle swarm optimization methods (MOPSO), the roughness Ra using MOHQPSO along the Pareto optimal solution are improved by 68.24, 59.31 and 33.80%, respectively. This reveals that the predictive models established can improve the machining quality in CNC turning of Al7075-T6.

scholarly journals Multi-View Laser Point Cloud Global Registration for a Single Object

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3729 ◽  
Author(s):  
Shuai Wang ◽  
Hua-Yan Sun ◽  
Hui-Chao Guo ◽  
Lin Du ◽  
Tian-Jian Liu
Keyword(s):  
Spatial Distribution ◽  
Objective Function ◽  
Point Cloud ◽  
Point Clouds ◽  
Low Rank ◽  
Single Object ◽  
Loop Closure Detection ◽  
Global Registration ◽  
Distribution Features ◽  
Better Than

Global registration is an important step in the three-dimensional reconstruction of multi-view laser point clouds for moving objects, but the severe noise, density variation, and overlap ratio between multi-view laser point clouds present significant challenges to global registration. In this paper, a multi-view laser point cloud global registration method based on low-rank sparse decomposition is proposed. Firstly, the spatial distribution features of point clouds were extracted by spatial rasterization to realize loop-closure detection, and the corresponding weight matrix was established according to the similarities of spatial distribution features. The accuracy of adjacent registration transformation was evaluated, and the robustness of low-rank sparse matrix decomposition was enhanced. Then, the objective function that satisfies the global optimization condition was constructed, which prevented the solution space compression generated by the column-orthogonal hypothesis of the matrix. The objective function was solved by the Augmented Lagrange method, and the iterative termination condition was designed according to the prior conditions of single-object global registration. The simulation analysis shows that the proposed method was robust with a wide range of parameters, and the accuracy of loop-closure detection was over 90%. When the pairwise registration error was below 0.1 rad, the proposed method performed better than the three compared methods, and the global registration accuracy was better than 0.05 rad. Finally, the global registration results of real point cloud experiments further proved the validity and stability of the proposed method.

scholarly journals Calibration of a Nonlinear DC Motor under Uncertainty Using Nonlinear Optimization Techniques

2021 ◽  
Vol 65 (1) ◽  
pp. 42-52
Author(s):  
Hamed Keshmiri Neghab ◽  
Hamid Keshmiri Neghab
Keyword(s):  
Nonlinear Optimization ◽  
Model Calibration ◽  
Optimization Methods ◽  
Dc Motor ◽  
Optimization Techniques ◽  
Unknown Parameters ◽  
Labview Software ◽  
Dc Motors ◽  
Industrial Software ◽  
Nonlinear Optimization Methods

The use of DC motors is increasingly high and it has more parameters which should be normalized. Now the calibration of each parameters is important for each motor, because it affects in its performance and accuracy. A lot of researches are investigated in this area. In this paper demonstrated how to estimate the parameters of a Nonlinear DC Motor using different Nonlinear Optimization techniques of fitting parameters to model, that called model calibration. First, three methods for calibration of a DC motor are defined, then unknown parameters of the mathematical model with the nonlinear optimization techniques for the fitting routines and model calibration process, are identified. In addition, three optimization techniques such as Levenberg-Marquardt, Constrained Nonlinear Optimization and Gauss-Newton, are compared. The goal of this paper is to estimate nonlinear parameters of a DC motor under uncertainty with nonlinear optimization methods by using LabVIEW software as an industrial software and compare the nonlinear optimization methods based on position, velocity and current. Finally, results are illustrated and comparison between these methods based on the results are made.

scholarly journals Optimizing community detection in social networks using antlion and K-median

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Amany A. Naem ◽  
Neveen I. Ghali
Keyword(s):  
Social Networks ◽  
Objective Function ◽  
Community Detection ◽  
Optimization Problem ◽  
Real Life ◽  
Optimization Methods ◽  
Population Based ◽  
Detection Problem ◽  
Network Communities ◽  
Antlion Optimization

Antlion Optimization (ALO) is one of the latest population based optimization methods that proved its good performance in a variety of applications. The ALO algorithm copies the hunting mechanism of antlions to ants in nature. Community detection in social networks is conclusive to understanding the concepts of the networks. Identifying network communities can be viewed as a problem of clustering a set of nodes into communities. k-median clustering is one of the popular techniques that has been applied in clustering. The problem of clustering network can be formalized as an optimization problem where a qualitatively objective function that captures the intuition of a cluster as a set of nodes with better in ternal connectivity than external connectivity is selected to be optimized. In this paper, a mixture antlion optimization and k-median for solving the community detection problem is proposed and named as K-median Modularity ALO. Experimental results which are applied on real life networks show the ability of the mixture antlion optimization and k-median to detect successfully an optimized community structure based on putting the modularity as an objective function.

An Adjoint Based Approach for Optimal Design of Morphing SMP

2013 ◽  
Author(s):  
Shuang Wang ◽  
John C. Brigham
Keyword(s):  
Shape Change ◽  
Mechanical Energy ◽  
Optimization Methods ◽  
Design Parameters ◽  
Optimization Strategy ◽  
Thermally Activated ◽  
Gradient Based ◽  
Nonlinear Optimization Methods ◽  
Numerical Representations ◽  
Objective Functional

This work presents a strategy to identify the optimal localized activation and actuation for a morphing thermally activated SMP structure or structural component to obtain a targeted shape change or set of shape features, subject to design objectives such as minimal total required energy and time. This strategy combines numerical representations of the SMP structure’s thermo-mechanical behavior subject to activation and actuation with gradient-based nonlinear optimization methods to solve the morphing inverse problem that includes minimizing cost functions which address thermal and mechanical energy, morphing time, and damage. In particular, the optimization strategy utilizes the adjoint method to efficiently compute the gradient of the objective functional(s) with respect to the design parameters for this coupled thermo-mechanical problem.

Reduced Order Design Optimization of Liquid Cooled Heat Sinks

2021 ◽  
Vol 143 (4) ◽  
Author(s):  
Aniket Ajay Lad ◽  
Kai A. James ◽  
William P. King ◽  
Nenad Miljkovic
Keyword(s):  
Design Optimization ◽  
Power Density ◽  
Building Blocks ◽  
Optimization Methods ◽  
Population Based ◽  
Global Optimum ◽  
Heat Sinks ◽  
Geometrical Parameters ◽  
Power Module ◽  
Reduced Order

Abstract The recent growth in electronics power density has created a significant need for effective thermal management solutions. Liquid-cooled heat sinks or cold plates are typically used to achieve high volumetric power density cooling. A natural tradeoff exists between the thermal and hydraulic performance of a cold plate, creating an opportunity for design optimization. Current design optimization methods rely on computationally expensive and time consuming computational fluid dynamics (CFD) simulations. Here, we develop a rapid design optimization tool for liquid cooled heat sinks based on reduced-order models for the thermal-hydraulic behavior. Flow layout is expressed as a combination of simple building blocks on a divided coarse grid. The flow layout and geometrical parameters are incorporated to optimize designs that can effectively address heterogeneous cooling requirements within electronics packages. We demonstrate that the use of population-based searches for optimal layout selection, while not ensuring a global optimum solution, can provide optimal or near-optimal results for most of the test cases studied. The approach is shown to generate optimal designs within a timescale of 60–120 s. A case study based on cooling of a commercial silicon carbide (SiC) electronics power module is used to demonstrate the application of the developed tool and is shown to improve the performance as compared to an aggressive state-of-the-art single-phase liquid cooling solution by reducing the SiC junction-to-coolant thermal resistance by 25% for the same pressure drop.

Optimization of the geometrical parameters for elevated temperature hydro-mechanical deep drawing process of 2024 aluminum alloy

2020 ◽  
pp. 095440892094936
Author(s):  
S Yaghoubi ◽  
F Fereshteh-Saniee
Keyword(s):  
Aluminum Alloy ◽  
Elevated Temperature ◽  
Objective Function ◽  
Deep Drawing ◽  
Sheet Thickness ◽  
Bees Algorithm ◽  
Group Method ◽  
Geometrical Parameters ◽  
2024 Aluminum Alloy ◽  
Experimental Findings

This research is concerned with the effects of the geometrical parameters of the die in elevated temperature Hydro-Mechanical Deep Drawing (HMDD) process of 2024 aluminum alloy. A Group Method of Data Handling (GMDH) process was used to train a neural network in order to study the process behavior. Based on the maximum reduction in sheet thickness and the uniformity of the final product, an objective function was constructed. The Bees Algorithm (BA) was used to achieve the optimal values for process variables. To verify the simulation results, they were compared with the experimental findings gained via this research and an appropriate correlation was observed between these results. This comparison showed that, by optimization of the geometrical parameters of the process, the value of the combined objective function was the best one compared with all of the cases tried in the present investigation.

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