scholarly journals Optimization of GMAW Process Parameters Using Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
P. Sreeraj ◽  
T. Kannan ◽  
Subhashis Maji
Keyword(s):  
Particle Swarm Optimization ◽  
Carbon Steel ◽  
Process Parameters ◽  
Gas Metal Arc Welding ◽  
Particle Swarm ◽  
Thick Layer ◽  
Bead Geometry ◽  
Low Carbon ◽  
Swarm Optimization ◽  
Corrosion Resistant

To improve the corrosion-resistant properties of carbon steel cladding process is usually used. It is a process of depositing a thick layer of corrosion resistant material-over carbon steel plate. Most of the engineering applications require high strength and corrosion resistant materials for long-term reliability and performance. By cladding, these properties can be achieved with minimum cost. The main problem faced in cladding is the selection of optimum combinations of process parameters for achieving quality clad and hence good clad bead geometry. This paper highlights an experimental study to optimize various input process parameters (welding current, welding speed, gun angle, contact tip to work distance, and pinch) to get optimum dilution in stainless steel cladding of low-carbon structural steel plates using gas metal arc welding (GMAW). Experiments were conducted based on central composite rotatable design with full-replication technique and mathematical models were developed using multiple regression method. The developed models have been checked for adequacy and significance. Using particle swarm optimization (PSO) the parameters were optimized to get minimal dilution.

scholarly journals Estimation of Optimum Dilution in the GMAW Process Using Integrated ANN-GA

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
P. Sreeraj ◽  
T. Kannan ◽  
Subhashis Maji
Keyword(s):  
Carbon Steel ◽  
Process Parameters ◽  
Gas Metal Arc Welding ◽  
Thick Layer ◽  
Welding Current ◽  
Minimum Cost ◽  
High Strength ◽  
Bead Geometry ◽  
Low Carbon ◽  
Corrosion Resistant

To improve the corrosion resistant properties of carbon steel, usually cladding process is used. It is a process of depositing a thick layer of corrosion resistant material over carbon steel plate. Most of the engineering applications require high strength and corrosion resistant materials for long-term reliability and performance. By cladding these properties can be achieved with minimum cost. The main problem faced on cladding is the selection of optimum combinations of process parameters for achieving quality clad and hence good clad bead geometry. This paper highlights an experimental study to optimize various input process parameters (welding current, welding speed, gun angle, and contact tip to work distance and pinch) to get optimum dilution in stainless steel cladding of low carbon structural steel plates using gas metal arc welding (GMAW). Experiments were conducted based on central composite rotatable design with full replication technique, and mathematical models were developed using multiple regression method. The developed models have been checked for adequacy and significance. In this study, artificial neural network (ANN) and genetic algorithm (GA) techniques were integrated and labeled as integrated ANN-GA to estimate optimal process parameters in GMAW to get optimum dilution.

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.

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