Effect of laser bionic treatment on fatigue crack growth behavior of TIG welded AZ31B magnesium alloy joint

Meng, Chao; Zhang, Zhengqiang; Yang, Juntao; Zhuang, Weibin; Shi, Haifang

doi:10.1016/j.matlet.2022.132334

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…At the end of welding, the molten pool and the heat-affected zone cooled rapidly, and the heat-affected zone produced large tensile stress, which was the direct factor for the initiation and propagation of liquefaction cracks in the heat-affected zone during the cooling process. [ 20 , 21 ]. In Figure 7 d, there are some oxides in the thermal reaction zone, and the shape of magnesium invading the steel is elliptical.…”

Section: Resultsmentioning

confidence: 99%

Effect of Laser Welding Parameters on Joint Structure of AZ31B Magnesium Alloy and 304 Stainless Steel

Wan

Zhang

et al. 2022

Materials

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The effects of laser welding parameters on the interface microstructure of AZ31B magnesium alloy and 304 stainless steel were investigated. After welding, a scanning electron microscope and ultra-depth of field microscope were used to observe the microstructure of the welded material, to analyze the effects of power on the interface morphology. The simulation of laser welding of magnesium and steel was carried out by the COMSOL software. The results showed that when the power was 15 W–20 W, the temperature did not reach the melting point of magnesium alloy, there was MgO at the welding, and the interface had poor connection strength. When the power was 35 W–50 W, the temperature reached or even exceeded the boiling point of magnesium alloy, and the interface formed hot cracks, pores, and oxides and had poor joint strength. When the power was 25 W–30 W, the temperature was between the melting point and boiling point of magnesium, and the interface had excellent connection strength.

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Section: Resultsmentioning

confidence: 99%

Effect of Laser Welding Parameters on Joint Structure of AZ31B Magnesium Alloy and 304 Stainless Steel

Wan

Zhang

et al. 2022

Materials

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“…(8) Finally, the neural network approaches were influenced to observe the actual and predictable outcomes with support of 3D scatter plot. (9) In the analysis of ANN, most of the outputs were improved by the predicted values and these process parameters were also validated with the ANOVA technique. (10) The different particles categories like TiO 2 , B4C, and chromium-based ceramic particles could be utilized in the upcoming research work.…”

Section: Discussionmentioning

confidence: 99%

“…At the same time, TIG welding was used where there were requirements to fulfill the quality and serious situations [9]. It has protected the weld pool by shielding gas, and this welding is easy to carry in any position, and fine soft welding was carried out.…”

Section: Introductionmentioning

confidence: 99%

Analytical and Neural Network Analysis on Flux-Coated Aluminium Alloy by Activated TIG Welding with Synthesized Nanocomposites

Raja¹,

Kumar

Kumari

et al. 2023

Journal of Nanomaterials

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This research focused to synthesize the material by the tungsten inert gas (TIG) welding process with support of appropriate flux coating material. Therefore the required amount of flux coating material was utilized to enhance the mechanical properties of the specified localized welded regions. Hence, this study concentrated to select the nano-SiO2 flux particles that were employed for TIG process. This activated TIG welding composes the flux-coated welding on the base metal of AA5083-H111, as this material was highly reactive with SiO2 by the presence of magnesium precipitates and well synthesized after the welding. The post- and preheat treatment process was achieved before and after welding. The selection of activated TIG process parameters composed of strengthened weld specimens along with constant parameters like electrode tip angle and flow rate, respectively. Initially, the process parameters were designed by the statistical analysis of Box Behnken method with support of regression formulation to determine the optimal solution. The maximum tensile strength was attained at the welding process parameters of welding speed (100 mm/min), voltage (13 V), and current (125 amps). The higher hardness was achieved at the process parameters of welding speed (80 mm/min), voltage (12 V), and current (125 amps), respectively. Finally, the neural network approach was utilized to verify the predicted responses of tensile and microhardness properties. The interaction plots, mean plots, and 3D scatter plots were influenced to enhance the process parameters. In this research, mechanical properties were enhanced by the flux-coated SiO2 and the analytical method also advances the optimal parameters.

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Effect of AC signal ratio on the formation, microstructure and mechanical properties of welded joint of AZ31B Mg alloy during the AC/DC mix GTAW

Guo

Cong

Du³

et al. 2023

Journal of Manufacturing Processes

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Effect of laser bionic treatment on fatigue crack growth behavior of TIG welded AZ31B magnesium alloy joint

Cited by 5 publications

References 14 publications

Effect of Laser Welding Parameters on Joint Structure of AZ31B Magnesium Alloy and 304 Stainless Steel

Effect of Laser Welding Parameters on Joint Structure of AZ31B Magnesium Alloy and 304 Stainless Steel

Analytical and Neural Network Analysis on Flux-Coated Aluminium Alloy by Activated TIG Welding with Synthesized Nanocomposites

Effect of AC signal ratio on the formation, microstructure and mechanical properties of welded joint of AZ31B Mg alloy during the AC/DC mix GTAW

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