Optimization and Prediction of Chip Thickness Profiles of Machined Heat Affected Zone Mild Steel Weld Using Genetic Algorithm
Abstract
Research has revealed that most of the failures observed in fabricated metal structures is linked to excessive heat input and large heat affect zone. This work is utilizing genetic algorithm to optimizing and predicting chip thickness of machined affected zone of heat of mild steel weld. The design expert software was utilized to bring out a design matrix utilizing the class and level of the input parameters. The central composite design (CCD) was used. 30 sets of experiment were performed according to the design of experiment, the input parameters are speed of cutting, rate of feed, nose roughness and chip thickness. From the results obtained, the ANOVA showed that the second order polynomial was suggested as the best fit to predict the large response, contour plot and surface plot showed the interaction between the speed of cutting, rate of feed, and the chip thickness. The object produced have maximum strength and appropriate.
Keywords
Chip thickness, Heat affected zone, Machined heat affected zone, Optimization, Prediction.
Citations
IRE Journals:
Sibete Godfrey Ayeabu , Eyitemi Tonbra , Okachi Ikegwuru Ibezimakor , Uchendu Imereoma Frank
"Optimization and Prediction of Chip Thickness Profiles of Machined Heat Affected Zone Mild Steel Weld Using Genetic Algorithm" Iconic Research And Engineering Journals Volume 8 Issue 5 2024 Page 629-638
IEEE:
Sibete Godfrey Ayeabu , Eyitemi Tonbra , Okachi Ikegwuru Ibezimakor , Uchendu Imereoma Frank
"Optimization and Prediction of Chip Thickness Profiles of Machined Heat Affected Zone Mild Steel Weld Using Genetic Algorithm" Iconic Research And Engineering Journals, 8(5)
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