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超喷射熔化极活性气体保护焊的热源建模、熔深分析及参数优化

Heat source modeling, penetration analysis and parametric optimization of super spray MAG welding.

作者信息

Li Zhifeng, Xia Yimin

机构信息

College of Mechanical and Electrical Engineering, Central South University, Changsha, 410083, China.

State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, 410083, China.

出版信息

Sci Rep. 2023 Jun 9;13(1):9435. doi: 10.1038/s41598-023-36505-6.

Abstract

Main drives, cutterheads and other critical components of tunnel shield machines require welding with thick plates that leave roots over 5 mm. Full penetration welds cannot be achieved by conventional Pulsed MAG welding methods. This article introduces Super Spray MAG Welding technology and investigates its penetrating regularities and mechanisms through high-speed camera images, finite element simulation, and microstructural analysis. An optimal welding procedure was generated using a combination of Genetic Algorithm and Back Propagation Neural Network. The data show that Super Spray MAG arc exhibits greater concentration and stability than traditional MAG arc, marking its strong qualities in emitting high-energy beams. The morphological solidification pattern of the molten pool closely matches the FEM simulation results of the composite Gaussian surface heat source model and peak linear attenuation Gaussian cylinder heat source. The welding current mainly affects the penetration of the weld, followed by the extension of the wire, and lastly the welding speed. Increasing the welding current can transition droplet transfer from globular to spray, as well as alter microstructure development and mechanical characteristics. Suggested parameters for penetrating the 5 mm root were put forward. The BPNN-GA model established can effectively predict weld formation, and points out the optimal welding parameters.

摘要

隧道盾构机的主驱动、刀盘等关键部件需要用根部超过5毫米的厚板进行焊接。传统的脉冲熔化极活性气体保护焊(Pulsed MAG)方法无法实现全熔透焊接。本文介绍了超喷射熔化极活性气体保护焊(Super Spray MAG)技术,并通过高速摄像机图像、有限元模拟和微观结构分析研究了其熔透规律和机理。采用遗传算法和反向传播神经网络相结合的方法生成了最佳焊接工艺。数据表明,超喷射熔化极活性气体保护焊电弧比传统熔化极活性气体保护焊电弧具有更高的集中度和稳定性,在发射高能束方面表现出强大的性能。熔池的形态凝固模式与复合高斯面热源模型和峰值线性衰减高斯圆柱热源的有限元模拟结果密切匹配。焊接电流主要影响焊缝的熔深,其次是焊丝伸出长度,最后是焊接速度。增加焊接电流可以使熔滴过渡从球状转变为喷射状,同时改变微观结构发展和力学性能。提出了穿透5毫米根部的建议参数。所建立的反向传播神经网络-遗传算法(BPNN-GA)模型能够有效地预测焊缝成形,并指出最佳焊接参数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc10/10256848/74fc6c5e3eb7/41598_2023_36505_Fig1_HTML.jpg

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