A New Approach for Predicting andOptimizing Weld Bead Geometry in GMAW

نویسنده:
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سال
: 2010
چکیده: Generally, the quality of a weld joint is directly

influenced by the welding input parameter settings. In this study, the

regression modeling is used in order to establish the relationships

between input and output parameters for Gas Metal Arc Welding

(GMAW) process. To gather the required data for modeling, actual

tests were carried out based on the proposed Taguchi experimental

matrix design. The process variables considered here include voltage

(V); wire feed rate (F); torch Angle (A); welding speed (S) and

nozzle-to-plate distance (D). The process output characteristics

include weld bead height, width and penetration. To develop

mathematical models, various regression functions have been fitted

on the experimental data. The adequacies of the models are then

evaluated using analysis of variance (ANOVA) technique. The best

and most fitted model is then selected based on the ANOVA results

and other statistical analysis. The ANOVA results recommend that

the curvilinear model is the best fit in this case. In the next stage, the

selected model is implanted into a Simulated Annealing (SA)

optimization algorithm. This optimization procedure has been

developed in order to determine the best set of process variables

levels for any desired weld bead geometry characteristics.

Computational results show very good compatibility with

experimental data and demonstrate the effectiveness of the proposed

modeling and optimization approach.
کلیدواژه(گان): GMAW,Process parameters,Optimization,Regression modeling,SA algorithm
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