当前位置: X-MOL 学术Proc. Inst. Mech. Eng. E J. Process Mech. Eng. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Modeling and optimization of A-GTAW process using Box–Behnken design and hybrid BPNN-PSO approach
Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering ( IF 2.4 ) Pub Date : 2021-10-20 , DOI: 10.1177/09544089211050457
Masoud Azadi Moghaddam 1 , Farhad Kolahan 1
Affiliation  

Gas tungsten arc welding (GTAW) is the most extensively used process capable of fabricating a wide range of alloys based on its distinctive merits has been introduced. However, some demerits have been reported among which shallow penetration is the most crucial ones. In order to deal with the poor penetration of the process, different procedures have been proposed among which activated gas tungsten arc welding (A-GTAW) is the most extensively used one. In this study effect of percentage of activating fluxes (TiO2 and SiO2) combination (F) and the most important process variables (welding current (C), welding speed (S)) on the most important process measures (weld bead width (WBW), depth of penetration (DOP), and consequently aspect ratio (ASR)) in welding of AISI316L austenite stainless steel parts have been investigated. Box-behnken design (BBD) has been used to design the experimental matrix required for date gathering, modeling, and statistical analysis purposes. A neural network with a back propagation algorithm (BPNN) in artificial neural network (ANN) modeling approach has been employed to relate the process input variables and output characteristics. The proper BPNN architecture (number of hidden layers and neurons/nodes in each hidden layer) has been determined using particle swarm optimization (PSO) algorithm. Moreover, process optimization in such a way that maximum DOP, minimum WBW, and desired ASR achieved has been carried out using PSO algorithm. Next, the performance of PSO algorithm has been checked using simulated annealing (SA) algorithm. Finally, to evaluate the performance of the proposed method a set of confirmation experimental test has been conducted. Results of experimental tests revealed that the proposed method is quite efficient in modeling and optimization (with less than 4% error) of A-GTAW process.



中文翻译:

使用 Box-Behnken 设计和混合 BPNN-PSO 方法对 A-GTAW 过程进行建模和优化

气体钨极电弧焊 (GTAW) 是使用最广泛的工艺,能够制造基于其独特优点的各种合金。然而,已经报道了一些缺点,其中浅穿透是最关键的。为了解决工艺熔深差的问题,已经提出了不同的程序,其中活性气体钨极电弧焊(A-GTAW)是使用最广泛的一种。在本研究中,活化助焊剂(TiO 2和 SiO 2) 结合 (F) 和最重要的工艺变量(焊接电流 (C)、焊接速度 (S))对最重要的工艺措施(焊缝宽度 (WBW)、熔深 (DOP) 以及纵横比) ASR)) 在 AISI316L 奥氏体不锈钢零件的焊接中进行了研究。Box-behnken 设计 (BBD) 已用于设计数据收集、建模和统计分析所需的实验矩阵。在人工神经网络 (ANN) 建模方法中采用反向传播算法 (BPNN) 的神经网络已被用于关联过程输入变量和输出特性。正确的 BPNN 架构(隐藏层和每个隐藏层中的神经元/节点的数量)已使用粒子群优化 (PSO) 算法确定。而且,使用 PSO 算法以实现最大 DOP、最小 WBW 和所需 ASR 的方式进行过程优化。接下来,使用模拟退火(SA)算法检查了 PSO 算法的性能。最后,为了评估所提出方法的性能,进行了一组确认实验测试。实验测试结果表明,所提出的方法在A-GTAW工艺的建模和优化方面非常有效(误差小于4%)。

更新日期:2021-10-20
down
wechat
bug