Prediction of Angular Distortion in Gas Metal Arc Welding of Structural Steel Plates Using Artificial Neural Networks
نویسندگان
چکیده
The manufacturing of structures ranging from bridges and machinery to all types seaborne vehicles nuclear reactors space rockets has made considerable use arc welding technologies. This is as a result benefits including increased joint efficiency, air water tightness, no thickness restriction (0.6 25 mm), decreased fabrication time cost, etc. when compared alternative methods. Gas metal (GMAW) frequently used technology in industries due its inherent benefits, deeper penetration, smooth bead, Local heating cooling that takes place during the multi-pass process causes complicated stresses develop at weld zone, which ultimately angular distortion weldment. Angular major flaw affects weld’s properties well cracking misalignment welded joints. issue can be successfully solved by predicting it relation certain GMAW variables. A neural network model was created this research predict distortion. fractional factorial approach with 125 runs conduct exploratory experiments. feed forward backward propagation developed using experimental data. To train model, Levenberg–Marquardt method utilised. results indicate based on 4-9-3 more effective forecasting gaps between two, three, four passes than other three networks (4-2-3, 4-4-3, 797 4-8-3). Prediction accuracy 95 percent. study manage cycle structural steel plates achieve best possible quality least amount
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ژورنال
عنوان ژورنال: Metals
سال: 2023
ISSN: ['2075-4701']
DOI: https://doi.org/10.3390/met13020436