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Volume 2013 (2013), Article ID 460651, 10 pages
Optimization of GMAW Process Parameters Using Particle Swarm Optimization
1Department of Mechanical Engineering, Valia Koonambaikulathamma College of Engineering and Technology, Kerala 692574, India
2Department of Mechanical Engineering, SVS College of Engineering, Coimbatore, Tamilnadu 642109, India
3Department of Mechanical Engineering, IGNOU, Delhi 110068, India
Received 2 October 2012; Accepted 4 November 2012
Academic Editors: M. Carboneras, C. Panagopoulos, and S. C. Wang
Copyright © 2013 P. Sreeraj et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Prevention of corrosion is a major problem in industries. Even though it cannot be eliminated completely, it can be reduced to some extent. A corrosion resistant protective layer is made over the less corrosion resistant substrate by a process called cladding. This technique is used to improve life of engineering components but also reduce their cost. This process is mainly used in industries such as chemical, textiles, nuclear, steam power plants, food processing, and petro-chemical industries .
Most accepted method of employed in weld cladding is GMAW. It has got the following advantages.(i)High reliability;(ii)all position capability;(iii)ease to use;(iv)low cost;(v)high productivity;(vi)suitable for both ferrous and non ferrous metals; (vii)high deposition rate;(viii)absences of fluxes;(ix)cleanliness and ease of mechanization.
The mechanical strength of clad metal is highly influenced by the composition of metal but also by clad bead shape. This is an indication of bead geometry. Figure 1 shows the clad bead geometry. It mainly depends on wire feed rate, welding speed, arc voltage, and so forth. Therefore it is necessary to study the relationship between in process parameters and bead parameters to study clad bead geometry. Using mathematical models it can be achieved.
This paper highlights the study carried out to develop mathematical and PSO models to optimize clad bead geometry, in stainless steel cladding deposited by GAMAW. The experiments were conducted based on four factor five level central composite rotatable designs with full replication technique . The developed models have been checked for their adequacy and significance. Again using PSO, the bead parameters were optimized.
2. Experimental Procedure
The following machines and consumables were used for the purpose of conducting experiments. (1)A constant current gas metal arc welding machine (Invrtee V 350-PRO advanced processer with 5–425 amps output range);(2)welding manipulator;(3)wire feeder (LF-74 Model);(4)filler material Stainless Steel wire of 1.2 mm diameter (ER-308 L).;(5)gas cylinder containing a mixture of 98% argon and 2% of oxygen;(6)mild steel plate (grade IS-2062).
Test plates of size 300 × 200 × 20 mm were cut from mild steel plate of grade IS-2062 and one of the surfaces is cleaned to remove oxide and dirt before cladding. ER-308 L stainless steel wire of 1.2 mm diameter was used for depositing the clad beads through the feeder. Argon gas at a constant flow rate of 16 litres per minute was used for shielding. The properties of base metal and filler wire are shown in Table 1. The important and most difficult parameter found from trial run is wire feed rate. The wire feed rate is proportional to current .
Wire feed rate must be greater than critical wire feed rate to achieve pulsed metal transfer. The relationship found from trial run is shown in (1). The formula derived is shown in Figure 2: The selection of the welding electrode wire based on the matching the mechanical properties and physical characteristics of the base metal, weld size, and existing electrode inventory. A candidate material for cladding which has excellent corrosion resistance and weldability is stainless steel. These have chloride stress corrosion cracking resistance and strength significantly greater than other materials. These have good surface appearance, good radiographic standard quality, and minimum electrode wastage. Experimental design procedure used for this study is shown in Figure 3 and important steps are briefly explained.
3. Plan of Investigation
The research work was planned to be carried out in the following steps.(1)Identification of factors and responses.(2)Finding limits of process variables. (3)Development of design matrix.(4)Conducting experiments as per design matrix.(5)Recording the responses.(6)Development of mathematical models.(7)Checking the adequacy of developed models.(8)Conducting conformity tests.
4. Prediction of Clad Bead Geometry Using Regression Equation
The following independently controllable process parameters were found to be affecting output parameters. These are wire feed rate (W), welding speed (S), welding gun angle (T), contact tip to work to distance (N) and pinch (Ac), the responses chosen were clad bead width (W), height of reinforcement (R), depth of Penetration (P), and percentage of dilution (D). The responses were chosen based on the impact of parameters on final composite model.
The basic difference between welding and cladding is the percentage of dilution. The properties of the cladding is the significantly influenced by dilution obtained. Hence control of dilution is important in cladding where a low dilution is highly desirable. When dilution is quite low, the final deposit composition will be closer to that of filler material and hence corrosion resistant properties of cladding will be greatly improved. The chosen factors have been selected on the basis to get minimal dilution and optimal clad bead geometry .
Few significant research works have been conducted in these areas using these process parameters and so these parameters were used for experimental study. Working ranges of all selected factors are fixed by conducting trial runs. This was carried out by varying one of the factors while keeping the rest of them as constant values. Working range of each process parameters was decided upon by inspecting the bead for smooth appearance without any visible defects. The upper limit of given factor was coded as −2. The coded value of intermediate values were calculated using where is the required coded value of parameter X is any value of parameter from . is the lower limit of parameters and is the upper limit parameters. The chosen level of the parameters with their units and notation are given in Table 2.
Design matrix chosen to conduct the experiments was central composite rotatable design. The design matrix comprises of full replication of 25(= 32), Factorial designs. All welding parameters in the intermediate levels (o) constitute the central points and combination of each welding parameters at either is highest value (+2) or lowest value (−2) with other parameters of intermediate levels (0) constitute star points. 32 experimental trails were conducted that make the estimation of linear quadratic and two way interactive effects of process parameters on clad geometry .
The experiments were conducted at SVS College of Engineering, Coimbatore, India. In this work thirty-two experimental runs were allowed for the estimation of linear quadratic and two-way interactive effects of corresponding each treatment combination of parameters on bead geometry as shown Table 3 at random. At each run settings for all parameters were disturbed and reset for next deposit. This is very essential to introduce variability caused by errors in experimental set up.
In order to measure clad bead geometry of transverse section of each weld overlays were cut using band saw from mid length . Position of the weld and end faces were machined and grinded. The specimen and faces were polished and etched using a 5% nital solution to display bead dimensions. The clad bead profiles were traced using a reflective type optical profile projector at a magnification of 10, in M/s Roots Industries Ltd. Coimbatore. Then the bead dimension such as depth of penetration height of reinforcement and clad bead width were measured. The traced bead profiles were scanned in order to find various clad parameters and the percentage of dilution with help of AUTO CAD software. This is shown in Figure 4 .
5. Development of Mathematical Models
The response function representing any of the clad bead geometry can be expressed as ,
where response variable, = welding current (I) in amps, welding speed (S) in mm/min, C = contact tip to work distance (N) in mm, D = welding gun angle (T) in degrees, pinch (Ac).
The second order surface response model equals can be expressed as below . where is the free term of the regression equation, the coefficients , , , and are linear terms, the coefficients β11, β22, β33, β44 and 55 quadratic terms, and the coefficients β12, β13, β14, β15, and so forth are the interaction terms. The coefficients were calculated using Quality America six sigma software (DOE-PC IV). After determining the coefficients, the mathematical models were developed. The developed mathematical models are given as follows.
6. Checking Adequacy of the Developed Models
The adequacy of the developed model was tested using the analysis of variance (ANOVA) technique. As per this technique, if the F-ratio values of the developed models do not exceed the standard tabulated values for a desired level of confidence (95%) and the calculated R-ratio values of the developed model exceed the standard values for a desired level of confidence (95%) then the models are said to be adequate within the confidence limit . These conditions were satisfied for the developed models. The values are shown in Table 5.
7. Optimization of GMAW Process Parameters by PSO
Heuristic technique PSO is proposed to optimize clad bead geometry of stainless steel cladding deposited by GMAW. This is for achieving good cladding. The optimization procedure is shown in Figure 6. Initial populations is the possible number of solutions (particles) of the optimization problem and each possible solution is called an individual. In this study possible number of solutions is formed by the values of welding current, welding speed, contact tip to work distance, welding gun angle, and pinch. The objective of this study is to minimize percentage of dilution taken from (8).
Initial population is created using the cladding parameters (welding current, welding speed, contact tip to work distance, welding gun angle, and pinch) and for the current populations using objective function. The best fitness value is stored as Pbest from history. From all random solutions reobtained for percentage of dilution. Fitness function values for each individual (particles) are calculated particles choose the best fitness value called Gbest . For each particle, calculate the velocity of the particle by Velocity = Velocity + C1rand1(Pbest − present) + C2xrand2(Gbest − present).
Particle position is updated by Present.
rand1 and rand2 are two random functions in the range where C1 and C2 are two positive constants named learning factors taken as 2 and “w” is the inertial weight taken as 0.5.The parameters used for PSO optimization are shown in Table 6.
7.1. Method for Developing PSO Model
(i)Initiate each particle.(ii)Calculate fitness value of each particle. If the fitness value is better than the best fitness value (Pbest) in history. Set the current value as new Pbest.(iii)Calculate Gbest.(iv)For each particle calculate the particle velocity.
7.2. Numerical Illustration for Developed PSO Model
The numerical illustration for the developed model to find optimal parameters for percentage of dilution as summarised in below. Welding current + () − rand() Welding speed + () − rand() Contact tip to work distance + () − rand() Welding gun angle + () − rand() Pinch + () − rand()
These values are substituted in (8) and dilution is obtained Consider. = A = Welding current (I) in Amps, = B = Welding Speed (S) in mm/min, = C = Contact to work piece distance (N) in mm, = D = Welding gun angle (T) in degree, = E = Pinch (Ac).
Subjected to bounds:
7.3. Objective Function
This is the percentage of dilution.
7.4. Constraint Equations
(Clad bead width () mm lower limit)
(Depth of penetration upper limit),
(Depth of penetration lower limit),
(Clad bead width upper limit),
(Height of reinforcement lower limit),
(Heights of reinforcement upper limit),
(Dilution Upper and lower limit),
7.5. Calculation of Pbest Value
7.6. Calculation of Gbest Value
The minimum dilution for initial solution or the whole iteration is considered as Gbest value. Table 7 shows Pbest values and Table 8 shows velocity of particle. A program on MATLAB 7 is created and run to get optimal dilution. Figure 7 shows convergence of PSO for optimal dilution.
8. Results and Discussions
Experiments were conducted using GMAW to produce cladding of austenitic stainless steel material. From the experimental results a mathematical model was developed using regression model. Further to enhance scope of work PSO model was developed to optimize clad bead geometry.
In this study, a PSO model to optimize clad bead geometry was developed. To ensure accuracy of model developed evolutionary computing technique PSO invoked to optimize the parameters of GMAW, for optimal weld quality. Convergence of developed model for optimal dilution is shown in Figure 7. From the figure it is evident that dilution is increasing up to 15th iteration then it is constant up to 92 and iterations and decreasing and then constant.
PSO maintains internal memory to store the Gbest and Pbest solutions. Each individual in the population will try to emulate the Gbest and Pbest solutions in the memory through updating PSO equations. Hence effectiveness of finding the global solutions is s very effective.
It can be see that PSO models can be effectively used to model cladding parameters. These optimized values can be directly used in automatic cladding in the forms of programs and for real time quality control and for the entire cladding process control application to improve bead geometry.
(i)A PSO model has been developed from the experimental data to achieve desired clad bead geometry. PSO models are capable of making optimization of clad bead geometry with reasonable accuracy. (ii)The developed models are able to optimize process parameters required to achieve the desired clad bead geometry of stainless steel cladding deposited by GMAW with reasonable accuracy. (iii)In this study, the following steps were applied for prediction of stainless steel clad bead geometry using GMAW: (a) data collection using experimental studies, analysing and processing of data, (c) prediction using regression equation, (d) development of PSO model, and (e) optimizing using PSO algorithm.(iv)The results showed that PSO models can be used as an alternative tool according to the present conventional calculation methods.
- P. K. Palani and N. Murugan, “Prediction of delta ferrite content and effect of welding process parameters in claddings by FCAW,” Materials and Manufacturing Processes, vol. 21, no. 5, pp. 431–438, 2006.
- T. Kannan and N. Murugan, “Prediction of Ferrite Number of duplex stainless steel clad metals using RSM,” Welding Journal, vol. 85, no. 91, p. 99, 2006.
- N. Murugan and V. Gunaraj, “Prediction and control of weld bead geometry and shape relationships in submerged arc welding of pipes,” Journal of Materials Processing Technology, vol. 168, no. 3, pp. 478–487, 2005.
- I. S. Kim, K. J. Son, Y. S. Yang, and P. K. D. V. Yaragada, “Sensitivity analysis for process parameters in GMA welding processes using a factorial design method,” International Journal of Machine Tools and Manufacture, vol. 43, no. 8, pp. 763–769, 2003.
- W. G. Cochran and G. M. Coxz, Experimental Design, John Wiley & Sons, New York, NY, USA, 1987.
- S. Karaoǧlu and A. Seçgin, “Sensitivity analysis of submerged arc welding process parameters,” Journal of Materials Processing Technology, vol. 202, no. 1–3, pp. 500–507, 2008.
- P. K. Ghosh, P. C. Gupta, and V. K. Goyal, “Stainless steel cladding of structural steel plate using the pulsed current GMAW process,” Welding Journal, vol. 77, no. 7, pp. 307s–314s, 1998.
- V. Gunaraj and N. Murugan, “Prediction and comparison of the area of the heat-affected zone for the bead-on-plate and bead-on-joint in submerged arc welding of pipes,” Journal of Materials Processing Technology, vol. 95, no. 1–3, pp. 246–261, 1999.
- D. C. Montgamery, Design and Analysis of Experiments, John Wiley & Sons, 2003.
- T. Kannan and J. Yoganandh, “Effect of process parameters on clad bead geometry and its shape relationships of stainless steel claddings deposited by GMAW,” International Journal of Advanced Manufacturing Technology, vol. 47, pp. 1083–1095, 2010.
- R. Poli, J. Kennedy, and T. BlackWell, “Particle swarm optimization. An Overview,” Swaran Intelligence, vol. 1, no. 1, pp. 33–57, 2007.
- F. Madadi, F. Ashrafizadel, and M. Shamanian, “Optimization of pulsed TIG welding process of satellite alloy on carbon steel using RSM,” Journal of Alloys and Compounds, vol. 510, pp. 71–77, 2012.
- R. Mudkerjee, S. Chakraborty, and S. Samanta, “Selection of wire electrical discharge machining process parameters using non traditional optimization algorithms,” Applied Soft Computing, vol. 12, no. 8, pp. 2506–2516, 2012.
- N. Yusup, A. M. Zain, and S. Z. M. Hashim, “Evolutionary techniques in optimizing machining parameters: review of recent applications,” Expert Systems with Applications, vol. 39, no. 10, pp. 9909–9927, 2012.