<i>Engineering Experimental Design Fundamentals</i>

Bartee, Edwin M.; Weber, Robert L.

doi:10.1063/1.3035754

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the present study, for each feature, linear regression equations were obtained first considering only main variable effects and thereafter considering both main and two factor interaction effects. This is done with the help of measured values using the linear technique [4,5]. Weld geometric features were then computed from these linear regression equations and compared with actual experimental values.…”

Section: Equations For Weld Bead Geometric Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Modeling of fillet welded joint of GMAW process: integrated approach using DOE, ANN and GA

Nagesh

Datta

2008

Int J Interact Des Manuf

View full text Add to dashboard Cite

An integrated approach based on the use of Design of Experiment (DOE), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) for modeling of Gas Metal Arc Welding (GMAW) process has been explained in this paper. The effects on the five weld bead geometric descriptors by the five weld process variables has been initiated by means of 2 n−1 fractional factorial experimental design technique. In this study, the 2 n−1 fractional factorial experimental design method was applied on the data available from a published work (Wu in Weld J 43(4): 179s-183s, 1964) to determine the main effects as well as 2-factor interaction effects. Using the results of 25-1 fractional factorial design experiments, multiple linear regression equations are postulated for both main as well as 2-factor interaction effects. It is observed that, main and 2-factor interaction linear equations have resulted in a small error between the estimated and experimental values while multiple linear regression equations have fitted the data very well. Back-propagation neural networks are used to associate the welding process variables with the features of the weld bead geometry. It is seen that neural network for estimating the weld bead geometric parameters can be effectively implemented, with little error percentage difference between the estimated and experimental results. In this study, Genetic Algorithms are used for optimizing the process parameters. The five process variables optimized by the GA are well within the vectors of minimum and maximum values of the controllable process variables of the experimental conditions in all the bead geometry descriptor cases. It can be concluded that genetic algorithms

show abstract

Section: Equations For Weld Bead Geometric Parametersmentioning

confidence: 99%

“…But the interaction effects can also have significant effects on the weld quality. Therefore, in the present work multiple linear regression technique [4,5] was used to establish mathematical models for Gas Metal Arc Welding process (GMAW) considering the main effects as well as two factor interaction effects.…”

Section: Introductionmentioning

confidence: 99%