Optimization of laser welding process parameters for super austenitic stainless steel using artificial neural networks and genetic algorithm

Sathiya, P.; Panneerselvam, K.; Jaleel, M.Y. Abdul

doi:10.1016/j.matdes.2011.11.028

Cited by 119 publications

(42 citation statements)

References 20 publications

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“…Genetic algorithm (GA) is an algorithm which is widely applied to some optimization problemsolving, both for maximization and minimization problems [3]. Genetic algorithms are starting to be used to model and solve complex physical problems, for which analytical solutions are prohibitively difficult to obtain.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…In addition, genetic algorithm can be used for problems which have certain restrictions or constraints. This algorithm is usable for finding the solution of general mathematical equation systems, including systems which are impossible, or difficult, to solve analytically [3].…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…The repetition of this procedure is expressed in a set of binary numbers or binary digits in the range of (binary digit/bit) as well as in the ranks of real numbers (the real). The algorithm ends when the maximum number of generations has been produced, or the best approximation level has been reached [3].…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…In the second step, if the perceptron gives the wrong answer, modify all weights b i according to For example, the architecture of a neural network consisting of neuron s, input r, and tan-sigmoid transfer function can be expressed in Fig. 2(a) and (b) [1,3]; (a) Fig. 2(a).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Optimization of Residual Stress of High Temperature Treatment Using Genetic Algorithm and Neural Network

2016

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In a nuclear industry area, high temperature treatment of materials is a factor which requires special attention. Assessment needs to be conducted on the properties of the materials used, including the strength of the materials. The measurement of material properties under thermal processes may reflect residual stresses. The use of Genetic Algorithm (GA) to determine the optimal residual stress is one way to determine the strength of a material. In residual stress modeling with several parameters, it is sometimes difficult to solve for the optimal value through analytical or numerical calculations. Here, GA is an efficient algorithm which can generate the optimal values, both minima and maxima. The purposes of this research are to obtain the optimization of variable in residual stress models using GA and to predict the center of residual stress distribution, using fuzzy neural network (FNN) while the artificial neural network (ANN) used for modeling. In this work a single-material 316/316L stainless steel bar is modeled. The minimal residual stresses of the material at high temperatures were obtained with GA and analytical calculations. At a temperature of 650 0 C, the GA optimal residual stress estimation converged at -711.3689 MPa at a distance of 0.002934 mm from center point, whereas the analytical calculation result at that temperature and position is -975.556 MPa . At a temperature of 850 0 C, the GA result was -969.868 MPa at 0.002757 mm from the center point, while with analytical result was -1061.13 MPa. The difference in residual stress between GA and analytical results at a temperature of 650 o C is about 27%, while at 850 o C it is 8.67%. The distribution of residual stress showed a grouping concentrated around a coordinate of (-76; 76) MPa. The residuals stress model is a degree-two polynomial with coefficients of 50.33, -76.54, and -55.2, respectively, with a standard deviation of 7.874.

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Section: Genetic Algorithmmentioning

confidence: 99%

Section: Genetic Algorithmmentioning

confidence: 99%

Section: Genetic Algorithmmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

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