The use of multi-objective genetic algorithm (MOGA) in optimizing and predicting weld quality

The search for acceptable optimal or near-optimal weld process parameters through the application of suitable optimization technique cannot be over emphasized, as this will help prevent weld defects capable of causing remarkable decrease in the mechanical properties of welded joints. This study expl...

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Bibliographic Details
Published in:Cogent engineering 2020-01, Vol.7 (1), p.1741310
Main Author: Sada, Samuel O.
Format: Article
Language:English
Subjects:
Evolutionary algorithms
Flow velocity
Gas flow
Gas tungsten arc welding
genetic algorithm
Genetic algorithms
Low carbon steels
Mechanical properties
modelling
Optimization
Optimization techniques
process control
Process parameters
Regression analysis
Regression models
Steel plates
Variance analysis
Weld defects
Weld strength
Welded joints
welding
Welding current
Welding parameters
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Summary:The search for acceptable optimal or near-optimal weld process parameters through the application of suitable optimization technique cannot be over emphasized, as this will help prevent weld defects capable of causing remarkable decrease in the mechanical properties of welded joints. This study explodes the application of multi-objective genetic algorithm (MOGA), an evolutionary optimization technique, alongside a regression model, in finding the optimal process parameters of a GTAW welded mild steel plate. Analysis of variance ANOVA was used in determining the significance of the model as well as studying the main and interactive effects of the process parameters on the responses. With the mathematical models obtained, used as objective functions, the genetic algorithm provided the best optimization on the 186th generation. An optimal weld strength of 546.8 N/mm 2 and hardness of 159.1 at the combined input variable of 140 ampere welding current, 24.9 V weld voltage, 20 l/min gas flow rate, and 2.4 mm filler rod diameter were obtained. Confirmatory tests conducted using the generated optimal results showed that the percentage of error was within the permissible limit of 5%, a validation of the optimization technique.
ISSN:2331-1916
2331-1916
DOI:10.1080/23311916.2020.1741310