Directed Energy Deposition-Arc (DED-Arc) and Numerical Welding Simulation as a Hybrid Data Source for Future Machine Learning Applications
نویسندگان
چکیده
This research presents a hybrid approach to generate sample data for future machine learning applications the prediction of mechanical properties in directed energy deposition-arc (DED-Arc) using GMAW process. DED-Arc is an additive manufacturing process which offers cost-effective way 3D metal parts, due its high deposition rate up 8 kg/h. The additively manufactured wall structures made filler material G4Si1 (ER70 S-6) are shown dependency t8/5 cooling time. numerical simulation used link parameters and geometrical features specific With input average welding power, speed such as thickness, layer height heat source size temperature field can be calculated each iteration simulated novel allows large, artificial sets training methods by combining experimental results regression equation based on experimentally measured Therefore, equations combination with numerically times accurate was possible this error only 2.6%. Thus, small set generated achieve enable precise properties. Moreover, validated model suitable calculation time, 0.3%.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2021
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app11157075