A Multiple Response Prediction Model for Dissimilar AA-5083 and AA-6061 Friction Stir Welding Using a Combination of AMIS and Machine Learning
نویسندگان
چکیده
This study presents a methodology that combines artificial multiple intelligence systems (AMISs) and machine learning to forecast the ultimate tensile strength (UTS), maximum hardness (MH), heat input (HI) of AA-5083 AA-6061 friction stir welding. The model integrates two methods, Gaussian process regression (GPR) support vector (SVM), into single model, then uses AMIS as decision fusion strategy merge SVM GPR. generated was utilized anticipate three objectives based on seven controlled/input parameters. These parameters were: tool tilt angle, rotating speed, travel shoulder diameter, pin geometry, type reinforcing particles, movement mechanism. effectiveness evaluated using two-experiment framework. In first experiment, we used newly produced datasets, (1) 7PI-V1 dataset (2) 7PI-V2 dataset, compared results with state-of-the-art approaches. second experiment existing datasets from literature varying base materials computational revealed proposed method more accurate prediction than previous methods. For all outperformed methods processes by an average 1.35% 6.78%.
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ژورنال
عنوان ژورنال: Computation (Basel)
سال: 2023
ISSN: ['2079-3197']
DOI: https://doi.org/10.3390/computation11050100