scholarly journals Penggunaan Metode Particle Swarm Optimization (PSO) pada Optimasi Multirespon Gaya Tekan dan Momen Torsi Penggurdian Material Komposit Glass Fiber Reinforce Polymer (GFRP) yang ditumpuk dengan Material Stainless Steel (SS)

2019 ◽  
Vol 10 (01) ◽  
pp. 1-7
Author(s):  
Angga Sateria ◽  
Indra Dwi Saputra ◽  
Yuli Dharta
Keyword(s):  
Stainless Steel ◽  
Particle Swarm Optimization ◽  
Glass Fiber ◽  
Process Parameters ◽  
Thrust Force ◽  
Particle Swarm ◽  
Drilling Process ◽  
Swarm Optimization ◽  
Feeding Speed ◽  
Different Levels

The Particle Swarm Optimization (PSO) method is one of the methods used for multirespon optimization in the manufacturing process. In this research, the material used is Glass fiber reinforced polymer (GFRP) composite material which is stacked with stainless steel material. The machining process used is a drilling process conducted on a vertical CNC machine Brother TC-22A-O. The thrust force and torque is the response used to evaluate the performance of the drilling process. The quality characteristics of this response "the smaller the better". The aim of this study was to identify the combination of process parameters to achieve the performance characteristics required in drilling process the GFRP-SS material using Particle Swarm Optimization methode (PSO). The three process parameters i.e. point angle, spindle speed, and feeding speed is used as a process parameter. Point angle was set at two different levels, while the other two were set at three different levels. Therefore, the 2 x 3 x 3 factorial is used as the experimental design. The experiments were replicated two times. The minimum thrust force and torque could be obtained by using point angle, spindle speed, and feeding speed are 118o, 2330 rpm, and 65 mm/minrespectively.

Optimasi Multirespon Gaya Tekan Dan Momen Torsi Pada Penggurdian Material Komposit Glass Fiber Reinforce Polymer (Gfrp) yang Ditumpuk Dengan Material Stainless Steel (SS) Menggunakan Metode Algoritma Genetika

2019 ◽  
Vol 9 (01) ◽  
pp. 1-5
Author(s):  
Angga Sateria
Keyword(s):  
Stainless Steel ◽  
Glass Fiber ◽  
Process Parameters ◽  
Feed Rate ◽  
Thrust Force ◽  
Spindle Speed ◽  
Quality Characteristic ◽  
Drilling Process ◽  
Glass Fiber Reinforced ◽  
Different Levels

Glass fiber reinforced polymer (GFRP)-stainless steel stacks used in the aircraft structural components. The assembly process of this components requires mechanical joining using bolt and nut. The drilling process is commonly used for producing hole to position the bolt correctly. Thrust force and torque are responses that used to evaluate the performance of drilling process. The quality characteristic of these responses are “smaller-is-better.” The aim of this experiment is to identify the combination of process parameters for achieving required multiple performance characteristics in drilling process of GFRP-stainless steel stacks materials. The three important process parameters, i.e., point angle, spindle speed, and feed rate were used as input parameters. Point angle was set at two different levels, whilethe other two were set at three different levels. Hence, a 2 x 3 x 3 full factorial was used as designexperiments. The experiments were replicated two times. The optimization was conducted by using genetic algorithm method. The minimum thrust force and torque could be obtained by using point angle, spindle speed and feed rate of 118o, 2383 rpm, 62 mm/min respectively.

scholarly journals Parameter Optimization of Gas Metal Arc Welding Process on AISI: 430 Stainless Steel Using Meta Heuristic Optimization Techniques

2020 ◽  
Author(s):  
P. Ravichandran ◽  
Meenakshipriya B ◽  
R. Parameshwaran ◽  
C. Maheswari ◽  
E.B. Priyanka ◽  
...  
Keyword(s):  
Stainless Steel ◽  
Particle Swarm Optimization ◽  
Process Parameters ◽  
Firefly Algorithm ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Depth Of Penetration ◽  
Swarm Optimization ◽  
Aisi 430

Abstract The superiority and profile of the weld obtained through Gas Metal Arc Welding (GMAW) are not only depends on the chemical configuration of the flux, but also on the choice of welding parameters. Since variety of process parameters influence the results, a proper empathetic of process performance and identification of suitable welding conditions (i.e. optimum setting of process parameters) are indeed essential to enhance quality. The present work highlights the application and comparison of single-response optimization using Response Surface Methodology (RSM) with Meta Heuristic Optimization techniques namely Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). The experimental analysis is conducted by optimizing the input parameters like Current Rating (Amp), Feed Rate (m/min), Welding Speed (mm/sec) and Gas Flow (l/m). An attempt has been made in the present research work by taking AISI: 430 stainless steel specimens to compare and analyse the performance in terms of weld bead geometry (Bead Width (mm), Bead Height (mm) and Depth of Penetration (mm)), Hardness (VHN) and Tensile Strength (N/mm²) using IRB 1410 Industrial manipulator. The effect of process parameters on ferritic stainless steel of series 400 (AISI: 430) grade has been analysed using Response Surface Methodology (RSM) method. Further, Meta Heuristic Optimization techniques namely Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) have been developed further to minimize the bead width, bead height and maximize the depth of penetration. While fairly similar results were achieved with the implementation of Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) were computationally efficient. Experimental validation of the single-objective as well as multi-objective optimization results indicates that the empirical models for the quality prediction with proposed optimization results are better for the GMAW process by IRB 1410 Industrial manipulator.

Optimization of Drilling Process via Weightless Swarm Algorithm

2015 ◽  
pp. 181-190
Author(s):  
T. O. Ting
Keyword(s):  
Particle Swarm Optimization ◽  
Reduction Rate ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Pso Algorithm ◽  
Drilling Process ◽  
Swarm Optimization ◽  
Lower Standard ◽  
Swarm Algorithm ◽  
Novel Algorithm

In this chapter, the main objective of maximizing the Material Reduction Rate (MRR) in the drilling process is carried out. The model describing the drilling process is adopted from the authors' previous work. With the model in hand, a novel algorithm known as Weightless Swarm Algorithm is employed to solve the maximization of MRR due to some constraints. Results show that WSA can find solutions effectively. Constraints are handled effectively, and no violations occur; results obtained are feasible and valid. Results are then compared to previous results by Particle Swarm Optimization (PSO) algorithm. From this comparison, it is quite impossible to conclude which algorithm has a better performance. However, in general, WSA is more stable compared to PSO, from lower standard deviations in most of the cases tested. In addition, the simplicity of WSA offers abundant advantages as the presence of a sole parameter enables easy parameter tuning and thereby enables this algorithm to perform to its fullest.

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