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

• Main Author: Samuel O. Sada
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2020-01-01
• Series: Cogent Engineering
• Subjects: genetic algorithm; optimization; modelling; process control; welding
• Online Access: http://dx.doi.org/10.1080/23311916.2020.1741310

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/mm2 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.