Multi-Objective Particle Swarm Optimization of Machining Parameters for End Milling Titanium Alloy Ti-6AL-4V

2014 ◽  
Author(s):  
Durul Ulutan ◽  
Abram Pleta ◽  
Laine Mears
Keyword(s):  
Titanium Alloy ◽  
Particle Swarm Optimization ◽  
End Milling ◽  
Chemical Reactivity ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Swarm Optimization ◽  
Multi Objective

Titanium alloy Ti-6Al-4V is a material with superior properties such as high mechanical strength, corrosion and creep resistance, and high strength-to-weight ratio, which make it an attractive material for various industries such as automotive, aerospace, power generation, and biomedical industries. However, these superior properties as well as its low thermal conductivity and chemical reactivity make it a challenge to machine Ti-6Al-4V at optimal conditions. In order to overcome this challenge, researchers constantly develop new tools and new techniques, but the extent of machining rates that can be used efficiently with those tools and techniques are usually not clear. Considering only one variable in the process and optimizing according to that variable is not sufficient because of the interactions between parameters. Also, selecting one objective function from a pool of many is not beneficial since those objectives are in conflict with one another. Therefore, this study proposes the use of a combined optimization algorithm in order to account for three major variables in end milling of Ti-6Al-4V: cutting speed, feed, and depth of cut. These variables are optimized for multiple objectives. Although it is possible to optimize the process for many different objectives, some of them are heavily correlated to each other, hence two objectives representing machinability and efficiency are selected: tool flank wear and material removal rate. The study aims to establish an optimal Pareto front of machining parameters that would optimize the conflicting outputs of the process, utilizing the multi-objective particle swarm optimization technique.

Optimization of Surface Grinding Operations Using Particle Swarm Optimization Technique

2005 ◽  
Vol 127 (4) ◽  
pp. 885-892 ◽  
Author(s):  
P. Asokan ◽  
N. Baskar ◽  
K. Babu ◽  
G. Prabhaharan ◽  
R. Saravanan
Keyword(s):  
Particle Swarm Optimization ◽  
Production Cost ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Process Models ◽  
Cutting Conditions ◽  
Approximate Determination ◽  
Depth Of Cut ◽  
Grinding Process ◽  
Swarm Optimization

The development of comprehensive grinding process models and computer-aided manufacturing provides a basis for realizing grinding parameter optimization. The variables affecting the economics of machining operations are numerous and include machine tool capacity, required workpiece geometry, cutting conditions such as speed, feed, and depth of cut, and many others. Approximate determination of the cutting conditions not only increases the production cost, but also diminishes the product quality. In this paper a new evolutionary computation technique, particle swarm optimization, is developed to optimize the grinding process parameters such as wheel speed, workpiece speed, depth of dressing, and lead of dressing, simultaneously subjected to a comprehensive set of process constraints, with an objective of minimizing the production cost and maximizing the production rate per workpiece, besides obtaining the finest possible surface finish. Optimal values of the machining conditions obtained by particle swarm optimization are compared with the results of genetic algorithm and quadratic programming techniques.

scholarly journals Optimization of Cutting Conditions in End Milling Process with the Approach of Particle Swarm Optimization

2011 ◽  
pp. 120-124
Author(s):  
Vikas Pare ◽  
Geeta Agnihotri ◽  
C.M. Krishna
Keyword(s):  
Surface Roughness ◽  
Particle Swarm Optimization ◽  
Surface Finish ◽  
Metal Cutting ◽  
End Milling ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wide Range ◽  
Input Variables

Milling is one of the progressive enhancements of miniaturized technologies which has wide range of application in industries and other related areas. Milling like any metal cutting operation is used with an objective of optimizing surface roughness at micro level and economic performance at macro level. In addition to surface finish, modern manufacturers do not want any compromise on the achievement of high quality, dimensional accuracy, high production rate, minimum wear on the cutting tools, cost saving and increase of the performance of the product with minimum environmental hazards. In order to optimize the surface finish, the empirical relationships between input and output variables should be established in order to predict the output. Optimization of these predictive models helps us to select appropriate input variables for achieving the best output performance. In this paper, four input variables are selected and surface roughness is taken as output variable. Particle swarm optimization technique is used for finding the optimum set of values of input variables and the results are compared with those obtained by GA optimization in the literature.

Aerodynamic Shape Optimization Using Improved Territorial Particle Swarm Algorithm

2012 ◽  
Author(s):  
Amir Nejat ◽  
Pooya Mirzabeygi ◽  
Masoud Shariat-Panahi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Weighted Average ◽  
Particle Swarm Algorithm ◽  
Density Estimator ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Multi Objective

In this paper, a new robust optimization technique with the ability of solving single and multi-objective constrained design optimization problems in aerodynamics is presented. This new technique is an improved Territorial Particle Swarm Optimization (TPSO) algorithm in which diversity is actively preserved by avoiding overcrowded clusters of particles and encouraging broader exploration. Adaptively varying “territories” are formed around promising individuals to prevent many of the lesser individuals from premature clustering and encouraged them to explore new neighborhoods based on a hybrid self-social metric. Also, a new social interaction scheme is introduced which guided particles towards the weighted average of their “elite” neighbors’ best found positions instead of their own personal bests which in turn helps the particles to exploit the candidate local optima more effectively. The TPSO algorithm is developed to take into account multiple objective functions using a Pareto-Based approach. The non-dominated solutions found by swarm are stored in an external archive and nearest neighbor density estimator method is used to select a leader for the individual particles in the swarm. Efficiency and robustness of the proposed algorithm is demonstrated using multiple traditional and newly-composed optimization benchmark functions and aerodynamic design problems. In final airfoil design obtained from the Multi Objective Territorial Particle Swarm Optimization algorithm, separation point is delayed to make the airfoil less susceptible to stall in high angle of attack conditions. The optimized airfoil also reveals an evident improvement over the test case airfoil across all objective functions presented.

scholarly journals Evolutionary Optimization of Machining Parameters Based on Surface Roughness in End Milling of Hot Rolled Steel

Materials ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 5494
Author(s):  
Issam Abu-Mahfouz ◽  
Amit Banerjee ◽  
Esfakur Rahman
Keyword(s):  
Surface Roughness ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
End Milling ◽  
Particle Swarm ◽  
Depth Of Cut ◽  
Selection Methods ◽  
Swarm Optimization ◽  
Vibration Signals ◽  
Better Than

Surface roughness measurements of machined parts are usually performed off-line after the completion of the machining operation. The objective of this work is to develop a surface roughness prediction method based on the processing of vibration signals during steel end milling operation performed on a vertical CNC machining center. The milling cuts were run under varying conditions (such as the spindle speed, feed rate, and depth of cut). This is a first step in the attempt to develop an online milling process monitoring system. The study presented here involves the analysis of vibration signals using statistical time parameters, frequency spectrum, and time-frequency wavelet decomposition. The analysis resulted in the extraction of 245 features that were used in the evolutionary optimization study to determine optimal cutting conditions based on the measured surface roughness of the milled specimen. Three feature selection methods were used to reduce the extracted feature set to smaller subsets, followed by binarization using two binarization methods. Three evolutionary algorithms—a genetic algorithm, particle swarm optimization and two variants, differential evolution and one of its variants, have been used to identify features that relate to the “best” surface finish measurements. These optimal features can then be related to cutting conditions (cutting speed, feed rate, and axial depth of cut). It is shown that the differential evolution and its variant performed better than the particle swarm optimization and its variants, and both differential evolution and particle swarm optimization perform better than the canonical genetic algorithm. Significant differences are found in the feature selection methods too, but no difference in performance was found between the two binarization methods.

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