Papers by Keyword: GA

Abstract: Because of their extraordinary qualities, titanium alloys are very sought-after materials that can be applied to a wide range of sectors. Excellent mechanical and chemical qualities, including a high strength-to-weight ratio and resistance to corrosion, are present in it. The special properties of these alloys make machining them extremely difficult. As frequent tool wear occurs throughout the machining process, Computer Numerical Control (CNC) milling has become a potential method for machining titanium alloys due to its precision and versatility. This review article provides a comprehensive overview of the development of titanium alloy CNC milling, with an emphasis on the effects of cutting tool geometries and materials on machining efficiency. The process examines several aspects of cutting circumstances, including depth of cut, speed, feed rate, and lubrication techniques, and optimizes machining parameters and procedures to achieve the best results. Surface integrity and quality, surface roughness, residual stresses, and microstructural changes brought about by CNC milling are the main points of evaluation.

Abstract: In the electroless nickel-boron coating process, surfactant helps to minimize the surface tension between the substrate and the electrolyte in the bath. Despite, its high cost and the formation of micelles from monomeric surfactant molecules at its critical micelle concentration (CMC), it is essential to optimize the concentration while using in the bath. In this study, to solve this problem, mathematical models are developed using regression and artificial neural network (ANN) techniques to relate the concentration of amphoteric surfactant (0-0.162 g/L) as an independent variable and microhardness as a dependent variable. Then, the developed model was used to optimize microhardness at CMC using a genetic algorithm (GA). The goodness of fit of the models was evaluated using the coefficient of determination (R²). The ANN model was found to be the best fit with R² = 0.99. The maximum microhardness of 852 HV was achieved at the CMC of 0.064 g/L, from the GA using the validated model as a fitness function.

Abstract: Duplex stainless steel has become one of the fastest-growing materials in the stainless steel family due to pitting resistance, stress-corrosion cracking, the combination of excellent mechanical properties, production features, and the area of applications such as oil and gas, nuclear and thermal power plants, chemical processing industries, saltwater processing industries, and pipeline systems. However, it is more difficult to machine due to its high toughness, low thermal conductivity, and ductility. The experiment has conducted using 2205- Duplex Stainless steel round bar material considering carbide cutting tools using Computer Numerical Control lathe to estimate machining time to address and meet the industrial need. Using Central Composite Designed by using Response Surface Methodology technique develops a second-order mathematical model based on the machining parameters. The Analysis of Variance technique was used to investigate the material's performance characteristics, and the impact of cutting parameters on the work piece was analyzed using the Design Expert-V12 software. Cutting speed is the most crucial determining factor compared to other factors. The Genetic Algorithm is trained and tested in MATLAB to evaluate the best possible solutions. The genetic Algorithm recommends the most outstanding lowest predicted value of 1.2204 mm. The confirmatory analysis shows the experimental values, and their error percentage is within ±2%; these shows indicated predicted values are very close to the Genetic Algorithm results. The conclusions were in good agreement with the experimental machining time values.

Abstract: The newly fabricated hybrid metal matrix composite of Al 6061 reinforced with wt. % of Cu/Sic/Graphite is prepared by a stir casting route. Electrical discharge machining (EDM) is employed to machine this MMC with copper electrode. The purpose of this study is to investigate the second order mathematical model in terms of machining constraints were developed for Material removal rate prediction. The adequacy of the model on MRR has been established with a statistical analysis of variance (ANOVA) to investigate the influence of process parameters and their interactions. Further this model is processed with help of Genetic Algorithm (GA) to find out the optimum machining parameters. The best result for maximum MRR using GA are carried out to show a good agreement with the predicted results.

Abstract: Application of optimization method for structure design and analysis is a technology trend to provide optimum products. This paper describe optimization process using genetic algorithm that was applied on non traditional structure, geodesic beam element. Finite element method program was developed as a main computer code for supporting optimization process. Pre-and post processor program was created to support GA. Based on minimum structure weight as the target of design optimization, the number of beam, beam element angle, and its size are to be the output of optimization process. The geodesic structure weight of plane model have been compared with traditional structure, and the results show that geodesic structure provide better performance of responding load direction than traditional structure. The influence of swept angle to the weight structure show that the higher the swept angle of geodesic beam structure, the higher values of weight structure are resulted.

Abstract: A Combination of dynamic vibration absorbers (DVAs) consist of Tuned Mass Damper (TMD) and Tuned Liquid Column Damper (TLCD) for reducing vibration response of a two-DOF shear structure model is proposed. The absorber parameters are optimized using Genetic Algorithm (GA). The cost function is derived from the ratio between structure response and the excitation signal. The limitation in absorber space and fluid motion are considered during optimization process. The simulation results show that GA optimization procedure is effective to get the optimal absorber parameters in the case of limited absorber size and motion.

Abstract: Abstract. Kinetic energy of a machining process is converted into heat energy. The generated heat at cutting tool and work piece interface has substantial impact on cutting tool life and quality of the work piece. On the other hand, development of advanced cutting tool materials, coatings and designs, along with a variety of strategies for lubrication, cooling and chip removal, make it possible to achieve the same or better surface quality with dry or Minimum Quantity Lubrication (MQL) machining than traditional wet machining. In addition, dry and MQL machining is more economical and environment friendly. In this work, 20 no. of experiments were carried out under dry machining conditions with different combinations of cutting speed, feed rate and depth of cut and corresponding cutting temperature and surface roughness are measured. The no. of experiments is determined through Design of Experiments (DOE). Nonlinear regression methodology is used to model the process using Response Surface Methodology (RSM). Multi-objective optimization is carried using Genetic Algorithm which ensures high productivity with good product quality.

Abstract: In this study, modified genetic algorithm (MGA) and A* search method (A*) is proposed for optimal motion planning of mobile robots. MGA utilizes the classical search and modified A* to establish a sub-optimal collision-free path as initial solution in simple and complex static environment. The enhancements for the proposed approach are presented in initialization stage and enhanced operators. Five objective functions are used to minimize traveling length, time, smoothness, security and trajectory and to reduce the energy consumption for mobile robots by using Cubic Spline interpolation curve fitting for optimal planned path. The purpose of this study is to evaluate the proposed approach performance by taking into consideration the effect of changing the number of iteration (it) and the size of population (pop) on its performance index. The simulation results show the effectiveness of proposed approach in governing the robot’s movements successfully from start to goal point after avoiding all obstacles its way in all tested environment. In addition, the results indicate that the proposed approach can find the optimal solution efficiently in a single run. This approach has been carried out by GUI using a popular engineering programming language, MATLAB.

Abstract: — A metaheuristic is an iterative process that guides and updates the operations of subordinate heuristics to efficiently produce better quality solutions. It is used in cases where exact methods are not sufficient to provide a solution so that the method of manipulation of a single solution or a collection of solutions at each iteration is deployed. This paper addresses the application of generic algorithm for a parallel machine flow line scheduling problem using the algorithm proposed for minimizing the makespan. Makespan is an important requirement to achieve effective production from process planning. As the problem chosen is NP-hard, genetic algorithm is adopted as it is one of the proven methods to search for a feasible optimal solution to the chosen objective function. The methodology is based on creating a group of random solutions for the randomly generated samples and applying the operators of cross over and mutation to improve the solutions till an acceptable fitness level is reached. The computational experiments deployed indicate that the proposed methodology and procedures are helping to arriving at better solutions faster.Keywords: Scheduling-Parallel machine – Flow shop scheduling– GA.

Abstract: This paper presents a comparative study of clustering using Artificial Intelligence (AI) techniques. There are 3 methods to be compared, two methods are pure method, called Self Organising Map (SOM) which is branch of Artificial Neural Network (ANN) and Genetic Algorithm (GA), while one method is hybrid between GA and SOM, called GA-based SOM. SOM is one of the most popular method for cluster analysis. SOM will group objects based on the nearest distance between object and updateable cluster centres. However, there are disadvantages of SOM. Solution quality is depend on initial cluster centres that are generated randomly and cluster centres update algorithm is just based on a delta value without considering the searching direction. Basically, clustering case could be modelled as optimisation case. The objective function is to minimise total distance of all data to their cluster centre. Hence, GA has potentiality to be applied for clustering. Advantage of GA is it has multi searching points in finding the solution and stochastic movement from a phase to the next phase. Therefore, possibility of GA to find global optimum solution will be higher. However, there is still some possibility of GA just find near-optimum solution. The advantage of SOM is the smooth iterative procedure to improve existing cluster centres. Hybridisation of GA and SOM believed could provide better solution. In this study, there are 2 data sets used to test the performance of the three techniques. The study shows that when the solution domain is very wide then SOM and GA-based SOM perform better compared to GA while when the solution domain is not very wide then GA performs better.