Flexural buckling load prediction of aluminium alloy columns using soft computing techniques

In this study, we propose a simulated annealing algorithm (SA) to train an adaptive neurofuzzy inference system (ANFIS). We performed different types of optimization algorithms such as genetic algorithm (GA), SA and artificial bee colony algorithm on two different problem types. Then, we measured the performance of these algorithms. First, we applied optimization algorithms on eight numerical benchmark functions which are sphere, axis parallel hyper-ellipsoid, Rosenbrock, Rastrigin, Schwefel, Griewank, sum of different powers and Ackley functions. After that, the training of ANFIS is carried out by mentioned optimization algorithms to predict the strength of heat-treated fine-drawn aluminium composite columns defeated by flexural bending. In summary, the accuracy of the proposed soft computing model was compared with the accuracy of the results of existing methods in the literature. It is seen that the training of ANFIS with the SA has more accuracy. Keywords: Soft computing, ANFIS, simulated annealing, flexural buckling, aluminium alloy columns.

Download Full-text

Comparative study for deriving stagedischarge– sediment concentration relationships using soft computing techniques

Journal of Achievements of Materials and Manufacturing Engineering ◽

10.5604/01.3001.0014.8489 ◽

2021 ◽

Vol 2 (104) ◽

pp. 57-76

Author(s):

P. Sihag ◽

M.R. Sadikhani ◽

V. Vambol ◽

S. Vambol ◽

A.K. Prabhakar ◽

...

Keyword(s):

Random Forest ◽

Soft Computing ◽

Sediment Load ◽

Prediction Models ◽

Absolute Error ◽

Load Prediction ◽

Statistical Parameters ◽

Hydro Power ◽

Input Combination ◽

Soft Computing Techniques

Purpose: Knowledge of sediment load carried by any river is essential for designing and planning of hydro power and irrigation projects. So the aim of this study is to develop and evaluating the best soft-computing-based model with M5P and Random Forest regressionbased techniques for computation of sediment using datasets of daily discharge, daily gauge and sediment load at the Champua gauging site of the Upper Baitarani river basin of India. Design/methodology/approach: Last few decades, the soft computing techniques based models have been successfully used in water resources modelling and estimation. In this study, the potential of tree based models are examined by developing and comparing sediment load prediction models, based on M5P tree and Random forest regression (RF). Several M5P and RF based models have been applied to a gauging site of the Baitarani River at Odisha, India. To evaluate the performance of the selected M5P and RF-based models, three most popular statistical parameters are selected such as coefficient of correlation, root mean square error and mean absolute error. Findings: A comparison of the results suggested that RF-based model could be applied successfully for the prediction of sediment load concentration with a relatively higher magnitude of prediction accuracy. In RF-based models Qt, Q(t-1), Q(t-2), S(t-1), S(t-2), Ht and H(t-1) combination based M10 model work superior than other combination based models. Another major outcome of this investigation is Qt, Q(t-1) and S(t-1) based model M4 works better than other input combination based models using M5P technique. The optimum input combination is Qt, Q(t-1) and S(t-1) for the prediction of sediment load concentration of the Baitarani River at Odisha, India. Research limitations/implications: The developed models were tested for Baitarani River at Odisha, India.

Download Full-text

Soft computing model for analysing the effect of friction stir processing parameters on the intergranular corrosion susceptibility of aluminium alloy AA5083

Koroze a ochrana materialu ◽

10.1515/kom-2018-0014 ◽

2018 ◽

Vol 62 (3) ◽

pp. 97-107 ◽

Cited By ~ 3

Author(s):

R. Vaira Vignesh ◽

R. Padmanaban ◽

Chinnaraj K.

Keyword(s):

Mass Loss ◽

Aluminium Alloy ◽

Soft Computing ◽

Process Parameters ◽

Friction Stir Processing ◽

Fuzzy System ◽

Intergranular Corrosion ◽

Friction Stir ◽

Soft Computing Techniques ◽

Corrosion Susceptibility

Abstract Aluminium alloy AA5083 is prone to intergranular corrosion in marine environments. In an attempt to reduce the intergranular corrosion, AA5083 was subjected to friction stir processing (FSP). The FSP experimental trials were conducted as per face-centered central composite design with three levels of variation in FSP process parameters viz. tool rotation speed (TRS), tool traverse speed (TTS) and tool shoulder diameter (SD). Intergranular corrosion susceptibility of the processed specimens was assessed by performing nitric acid mass loss test. The mass loss of the specimens was correlated with the intergranular corrosion susceptibility as per the standard ASTM G67-13. The experimental results indicate that FSP had significantly reduced the intergranular corrosion susceptibility of the AA5083 alloy. Soft computing techniques namely Artificial Neural Network, Mamdani Fuzzy system, and Sugeno Fuzzy system were used to predict the intergranular corrosion (IGC) susceptibility (mass loss) of the friction stir processed specimens. Among the developed models, Sugeno fuzzy system displayed minimum percentage error in prediction. So Sugeno fuzzy system was used to analyze the effect of friction stir processing process parameters on the IGC of the processed specimens. The results suggest that stir processing of AA5083 at a TRS of 1300 rpm, TTS of 60 mm/min and SD of 21 mm would make the alloy least susceptible to intergranular corrosion.

Download Full-text