چکیده مقاله
This talk delves into the transformative potential of integrating Finite Element Analysis FEA and machine learning ML for optimizing the Friction Stir Welding FSW process A 3D thermo mechanical simulation of aluminum alloy Al 6061 joints was developed using ABAQUS, accurately predicting nodal temperatures and residual stresses under varying process parameters Nine cutting edge ML regression models, including ensemble methods like Random Forest, Gradient Boosting, and XGBoost, were leveraged to predict and optimize welding outcomes with exceptional accuracy The study demonstrates the critical impact of rotational speed and feed rate on heat generation and stress distribution, showcasing ML’s ability to enhance FSW performance This research paves the way for real time monitoring, adaptive control systems, and a new paradigm in high performance welding technologies, offering a robust, scalable approach for superior welded joints Join us to explore how computational intelligence is redefining the future of advanced welding processes
نویسندگان
Kanak Kalita
Associate Professor in the Department of Mechanical Engineering at the Vel Tech University, Chennai
شیوه ارجاع
Kalita, Kanak,1403,Revolutionizing Friction Stir Welding: A Synergy of Finite Element Simulation and Machine Learning for Enhanced Weld Quality,7th International Conference on Welding and Non Destructive Testing & 25th National Conference on Welding & Inspection & 14th National Conference on NDT & 3nd National Conference on Additive Manufacturing,Tehran
ارائهشده در
مجموعه مقالات هفتمین کنفرانس بین المللی جوشکاری وغیرمخرب، بیست و پنجمین کنفرانس ملی جوش و بازرسی، چهاردهمین کنفرانس ملی آزمایش های غیرمخرب و سومین کنفرانس ملی ساخت افزایشی1 اسفند 1403 · تهران