Bîrsan Dan Cătălin, Păunoiu Viorel, Teodor Virgil Gabriel
Faculty of Engineering, Department of Manufacturing Engineering, "Dunărea de Jos" University of Galati, Domnească Street, 47, RO-800008 Galati, Romania.
Materials (Basel). 2023 Jun 21;16(13):4519. doi: 10.3390/ma16134519.
Refill friction stir spot welding (RFSSW) technology is a solid-state joint that can replace conventional welding or riveting processes in aerospace applications. The quality of the new welding process is directly influenced by the welding parameters selected. A finite element analysis was performed to understand the complexity of the thermomechanical phenomena during this welding process, validated by controlled experiments. An optimization model using neural networks was developed based on 98 parameter sets resulting from changing 3 welding parameters, namely pin penetration depth, pin rotation speed, and retention time. Ten parameter sets were used to verify the learning results of the optimization model. The 10 results were drawn to correspond to a uniform distribution over the training domain, with the aim of avoiding areas that might have contained distortions. The maximum temperature and normal stress reached at the end of the welding process were considered output data.
再填充搅拌摩擦点焊（RFSSW）技术是一种固态连接方式，在航空航天应用中可替代传统焊接或铆接工艺。新焊接工艺的质量直接受所选焊接参数的影响。进行了有限元分析，以了解该焊接过程中热机械现象的复杂性，并通过控制实验进行了验证。基于改变3个焊接参数（即销钉穿透深度、销钉转速和保持时间）得到的98组参数集，开发了一个使用神经网络的优化模型。使用10组参数集来验证优化模型的学习结果。绘制这10个结果以对应训练域上的均匀分布，目的是避免可能存在变形的区域。将焊接过程结束时达到的最高温度和法向应力视为输出数据。
