scholarly journals Multi-Objective Optimization of Deep Groove Ball Bearings Using Fatigue-Wear-Thermal Considerations Through Genetic Algorithms

2021 ◽  
Author(s):  
Raghavendra Rohit Dabbara ◽  
Rajiv Tiwari
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Load ◽  
Maximum Temperature ◽  
Fatigue Wear ◽  
Nsga Ii ◽  
Design Variables ◽  
Deep Groove ◽  
Wide Range ◽  
Metal Metal ◽  
Load Carrying

Abstract Bearings are the key components in a wide range of machines used in different sectors of industries. Consequently, any improvement in the performance of bearings would be a step forward to extract better performance from those machines. With this motivation in mind, we selected the most common type of bearing, the Deep Groove Ball Bearing (DGBB), for optimizing its performance. Obviously, the first and foremost performance characteristic would be the dynamic load carrying capacity (CD), whose improvement directly leads to the increased service life of the bearing. We have considered two more characteristics of bearings, which we thought would have an impact on the bearings’ performance. They are elasto-hydrodynamic film thickness (hmin) and maximum temperature developed (Tmax) inside the bearing. Maximization of the lubricant thickness decreases the damage to the rolling elements and the raceways due to metal-metal contact. And minimization of temperature is desirable in every machine element. Later, we would also see that the three objective functions chosen are conflicting in nature and hence mutually independent. For the current optimization problem, a genetic algorithm, Elitist Non-dominating Sorting Genetic Algorithm (NSGA-II) is chosen. And the bearing dimensions, which could be controlled during manufacturing are chosen as the design variables. Multiple constraints are chosen based on the design space and strength considerations. The optimization algorithm is used on a set of commercially available bearings. Pareto fronts are drawn to give the designer a multitude of optimal solutions to choose from. However, in this paper, the knee-point solution is presented, which is one of the optimum solutions. When compared with the commercial bearings, the bearings with optimized dimensions have higher dynamic load carrying capacities and hence longer life. Also, the sensitivity analysis is done to check the robustness of the bearings to manufacturing tolerances in the design variables. Finally, for visualization and as a check for physical plausibility, the radial dimensions of one of the optimized bearings have been shown.

A Multi-Dwell Temperature Profile Design for the Cure of Thick CFRP Composite Laminates

2021 ◽  
Author(s):  
Wenchang Zhang ◽  
Yingjie Xu ◽  
Xinyu Hui ◽  
Weihong Zhang
Keyword(s):  
Temperature Profile ◽  
Composite Laminates ◽  
Optimization Method ◽  
Maximum Temperature ◽  
Nsga Ii ◽  
Multi Objective ◽  
Optimization Result ◽  
Design Variables ◽  
Cure Time ◽  
Thick Laminates

Abstract This paper develops a multi-objective optimization method for the cure of thick composite laminates. The purpose is to minimize the cure time and maximum temperature overshoot in the cure process by designing the cure temperature profile. This method combines the finite element based thermo-chemical coupled cure simulation with the non-dominated sorting genetic algorithm-II (NSGA-II). In order to investigate the influence of the number of dwells on the optimization result, four-dwell and two-dwell temperature profiles are selected for the design variables. The optimization method obtains successfully the Pareto optimal front of the multi-objective problem in thick and ultra-thick laminates. The result shows that the cure time and maximum temperature overshoot are both reduced significantly. The optimization result further illustrates that the four-dwell cure profile is more e ective than the two-dwell, especially for the ultra-thick laminates. Through the optimization of the four-dwell profile, the cure time is reduced by 51.0% (thick case) and 30.3% (ultra-thick case) and the maximum temperature overshoot is reduced by 66.9% (thick case) and 73.1% (ultra-thick case) compared with the recommended cure profile. In addition, Self-organizing map (SOM) is employed to visualize the relationships between the design variables with respect to the optimization result.

scholarly journals Using a Goal-Switching Selection Operator in Multi-Objective Genetic Algorithm Optimization Problems

2013 ◽  
Author(s):  
Daniel Shaefer ◽  
Scott Ferguson
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Product Line ◽  
Algorithm Optimization ◽  
Nsga Ii ◽  
Solution Quality ◽  
Design Variables ◽  
Mp3 Player ◽  
Selection Operator

This paper demonstrates how solution quality for multiobjective optimization problems can be improved by altering the selection phase of a multiobjective genetic algorithm. Rather than the traditional roulette selection used in algorithms like NSGA-II, this paper adds a goal switching technique to the selection operator. Goal switching in this context represents the rotation of the selection operator among a problem’s various objective functions to increase search diversity. This rotation can be specified over a set period of generations, evaluations, CPU time, or other factors defined by the designer. This technique is tested using a set period of generations before switching occurs, with only one objective considered at a time. Two test cases are explored, the first as identified in the Congress on Evolutionary Computation (CEC) 2009 special session and the second a case study concerning the market-driven design of a MP3 player product line. These problems were chosen because the first test case’s Pareto frontier is continuous and concave while being relatively easy to find. The second Pareto frontier is more difficult to obtain and the problem’s design space is significantly more complex. Selection operators of roulette and roulette with goal switching were tested with 3 to 7 design variables for the CEC 09 problem, and 81 design variables for the MP3 player problem. Results show that goal switching improves the number of Pareto frontier points found and can also lead to improvements in hypervolume and/or mean time to convergence.

Dynamic Optimization of a Three Translational Degrees of Freedom Parallel Robot Based on a Multi-Objective Genetic Algorithm

2015 ◽  
Vol 789-790 ◽  
pp. 723-734
Author(s):  
Xing Guo Lu ◽  
Ming Liu ◽  
Min Xiu Kong
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Optimization ◽  
Degrees Of Freedom ◽  
Optimization Problem ◽  
Parallel Robot ◽  
Nsga Ii ◽  
Multi Objective ◽  
Global Dynamic ◽  
Design Variables ◽  
Dynamic Fluctuation

This work tends to deal with the multi-objective dynamic optimization problem of a three translational degrees of freedom parallel robot. Two global dynamic indices are proposed as the objective functions for the dynamic optimization: the index of dynamic dexterity, the index describing the dynamic fluctuation effects. The length of the linkages and the circumradius of the platforms were chosen as the design variables. A multi-objective optimal design problem, including constrains on the actuating and passive joint angle limits and geometrical interference is then formulated to find the Pareto solutions for the robot in a desired workspace. The Non-dominated Sorting Genetic Algorithm (NSGA-II) is adopted to solve the constrained nonlinear multi-objective optimization problem. The simulation results obtained shows that the robot can achieve better dynamic dexterity and less dynamic fluctuation simultaneously after the optimization.

scholarly journals Multi-Objective Optimization of Two-Stage Helical Gear Train Using NSGA-II

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
R. C. Sanghvi ◽  
A. S. Vashi ◽  
H. P. Patolia ◽  
R. G. Jivani
Keyword(s):  
Bending Strength ◽  
Gear Train ◽  
Design Parameters ◽  
Uniform Motion ◽  
Nsga Ii ◽  
Surface Fatigue ◽  
Face Width ◽  
Design Variables ◽  
Load Carrying ◽  
Do So

Gears not only transmit the motion and power satisfactorily but also can do so with uniform motion. The design of gears requires an iterative approach to optimize the design parameters that take care of kinematics aspects as well as strength aspects. Moreover, the choice of materials available for gears is limited. Owing to the complex combinations of the above facts, manual design of gears is complicated and time consuming. In this paper, the volume and load carrying capacity are optimized. Three different methodologies (i) MATLAB optimization toolbox, (ii) genetic algorithm (GA), and (iii) multiobjective optimization (NSGA-II) technique are used to solve the problem. In the first two methods, volume is minimized in the first step and then the load carrying capacities of both shafts are calculated. In the third method, the problem is treated as a multiobjective problem. For the optimization purpose, face width, module, and number of teeth are taken as design variables. Constraints are imposed on bending strength, surface fatigue strength, and interference. It is apparent from the comparison of results that the result obtained by NSGA-II is more superior than the results obtained by other methods in terms of both objectives.

Conceptual Hull Design Using a Genetic Algorithm

2003 ◽  
Vol 47 (03) ◽  
pp. 222-236
Author(s):  
T. W. Lowe ◽  
J. Steel
Keyword(s):  
Genetic Algorithm ◽  
Design Tool ◽  
Geometric Parameters ◽  
Design Variables ◽  
Distinct Cluster ◽  
Wide Range ◽  
Hull Design ◽  
Variable Space ◽  
Hull Forms ◽  
Differential Equation Method

A genetic algorithm is used to search the design variable space of a model ship hull for forms having specified values of various primary and secondary geometric parameters. A representative of each distinct cluster of the forms found is identified and presented as a candidate hull design having the required geometric characteristics. This could form the basis of a prototype conceptual design tool enabling the generation of hull forms satisfying specified requirements. The geometric parameters considered include the displacement, waterline length, waterline beam, and waterplane coefficient together with the locations of the centers of flotation and buoyancy. The hull surface is represented using the partial differential equation method, which permits a wide range of fair hull forms to be accessible using a relatively small number of design variables.

scholarly journals MULTI-OBJECTIVE OPTIMIZATION OF REINFORCED CONCRETE FRAMES USING NSGA-II ALGORITHM

2016 ◽  
Vol 8 (4) ◽  
pp. 157-164 ◽  
Author(s):  
Mehdi Babaei ◽  
Masoud Mollayi
Keyword(s):  
Genetic Algorithm ◽  
Reinforced Concrete ◽  
Steel Structures ◽  
American Concrete Institute ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Design Variables ◽  
Pareto Fronts

In recent decades, the use of genetic algorithm (GA) for optimization of structures has been highly attractive in the study of concrete and steel structures aiming at weight optimization. However, it has been challenging for multi-objective optimization to determine the trade-off between objective functions and to obtain the Pareto-front for reinforced concrete (RC) and steel structures. Among different methods introduced for multi-objective optimization based on genetic algorithms, Non-Dominated Sorting Genetic Algorithm II (NSGA II) is one of the most popular algorithms. In this paper, multi-objective optimization of RC moment resisting frame structures considering two objective functions of cost and displacement are introduced and examined. Three design models are optimized using the NSGA-II algorithm. Evaluation of optimal solutions and the algorithm process are discussed in details. Sections of beams and columns are considered as design variables and the specifications of the American Concrete Institute (ACI) are employed as the design constraints. Pareto-fronts for the objective space have been obtained for RC frame models of four, eight and twelve floors. The results indicate smooth Pareto-fronts and prove the speed and accuracy of the method.

Optimum design of rolling element bearings using a genetic algorithm—differential evolution (GA—DE) hybrid algorithm

2010 ◽  
Vol 225 (3) ◽  
pp. 714-721 ◽  
Author(s):  
W Y Lin
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Objective Function ◽  
Optimum Design ◽  
Dynamic Loads ◽  
Representative Case ◽  
De Algorithm ◽  
Design Variables ◽  
Deep Groove ◽  
Rolling Element

Binary-code genetic algorithms (BGA) have been used to obtain the optimum design for deep groove ball bearings, based on maximum fatigue life as an objective function. The problem has ten design variables and 20 constraint conditions. This method can find better basic dynamic loads rating than those listed in standard catalogues. However, the BGA algorithm requires a tremendous number of evaluations of the objective function per case to achieve convergence (e.g. about 5 200 000 for a representative case). To overcome this difficulty, a hybrid evolutionary algorithm by combining real-valued genetic algorithm (GA) with differential evolution (DE) is used together with the proper handling of constraints for this optimum design task. Findings show that the GA—DE algorithm can successfully find the better dynamic loads rating, about 1.3—11.1 per cent higher than those obtained using the traditional BGA. Moreover, the mean number of evaluations of the objective function required to achieve convergence is about 3011, using the GA—DE algorithm, as opposed to about 5 200 000 for a representative case using the BGA. Comparison shows the GA—DE algorithm to be much more effective and efficient than the BGA.

A Non-Dominated Sorting Genetic Algorithm Approach for Optimization of Multi-Objective Airport Gate Assignment Problem

2018 ◽  
Vol 2672 (23) ◽  
pp. 59-70 ◽  
Author(s):  
Seyedmirsajad Mokhtarimousavi ◽  
Danial Talebi ◽  
Hamidreza Asgari
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Supply And Demand ◽  
Multiple Criteria Decision Making ◽  
Financial Loss ◽  
Nsga Ii ◽  
Solution Approach ◽  
Multi Objective ◽  
Wide Range ◽  
Gate Assignment

Gate assignment problems (GAP) are one of the most substantial issues in airport operation. The ever-increasing demand producing high occupancy rates of gates, the potential financial loss from imbalances between supply and demand in congested airports, and the limited scope for expanding facilities present challenges that require an advanced methodology for optimal supply allocation. In principle, tackling GAP involves seeking to maintain an airport’s maximum capacity through the best possible allocation of resources (gates). There are a wide range of dependent and independent resources and limitations involved in the problem, adding to the complexity of GAP from both theoretical and practical perspectives. In this study, GAP is extended and mathematically formulated as a three-objective problem, taking into account all resources and restrictions, which can be directly linked to airport authorities’ multiple criteria decision-making processes. The preliminary goal of multi-objective formulation is to consider a wider scope, in which a higher number of objectives are simultaneously optimized, and thus to increase the practical efficiency of the final solution. The problem is solved by applying the second version of Non-dominated Sorting Genetic Algorithm (NSGA-II) as a parallel evolutionary optimization algorithm. Results illustrate that the proposed mathematical model could address most of the major criteria in the decision-making process in airport management in terms of passenger walking distances, robustness, and traditional costs. Moreover, the proposed solution approach shows promise in finding acceptable and plausible solutions compared with other multi-objective algorithms (BAT, PSO, ACO, and ABC).

Evolutionary Optimization of Micro- Thrust Bearings With Periodic Partial Trapezoidal Surface Texturing

2010 ◽  
Author(s):  
C. I. Papadopoulos ◽  
P. G. Nikolakopoulos ◽  
L. Kaiktsis
Keyword(s):  
Carrying Capacity ◽  
Stokes Equations ◽  
Surface Texturing ◽  
Load Carrying Capacity ◽  
Moving Wall ◽  
Stationary Wall ◽  
Design Variables ◽  
Optimization Study ◽  
Wide Range ◽  
Load Carrying

An optimization study of trapezoidal surface texturing in slider micro-bearings, via Computational Fluid Dynamics (CFD), is presented. The bearings are modeled as microchannels, consisting of a moving and a stationary wall. The moving wall (rotor) is assumed smooth, while part of the stationary wall (stator) exhibits periodic dimples of trapezoidal form. The extent of the textured part of the stator, and the dimple geometry are defined parametrically; thus, a wide range of texturing configurations is considered. Flow simulations are based on the numerical solution of the Navier-Stokes equations for incompressible isothermal flow. To optimize the bearing performance, an optimization problem is formulated, and solved by coupling the CFD code with an optimization tool based on genetic algorithms and local search methods. Here, the design variables define the bearing geometry, while load carrying capacity is the objective function to be maximized. Optimized texturing geometries are obtained for the case of parallel bearings, for several numbers of dimples, illustrating significant load carrying capacity levels. Further, these optimized texturing patterns are applied to converging bearings, for different convergence ratio values; the results demonstrate that, for small and moderate convergence ratios, substantial increase in the load carrying capacity, in comparison to smooth bearings, is obtained. Finally, an optimization study performed at a high convergence ratio shows that, in comparison to the parallel slider, the optimal texturing geometry is substantially different, and that performance improvement over smooth bearings is possible even for steep sliders.

Sign in / Sign up

Export Citation Format

Share Document