Feasibility study of thermo-compensated resistance brazing welding of C/C composites and T2 copper with AgCuTi filler metal

The dissimilar materials joining of C/C composites to T2 copper were performed successfully by thermo-compensated Resistance Brazing Welding(RBW)with AgCuTi filler powder.The interfacial microstructure,phase composition,and shear strength of the resistance brazed joints were investigated by the relevant analysis method.Experiment results indicated that the order affecting the shear strength of the C/C-Cu joint was welding current,welding pressure,and welding time in turn.The shear strength of backward thermo-compensated RBW was higher than that of forward thermo-compensated RBW due to the Peltier effect.The maximum shear strength of the C/C-Cu joint was 11.56 MPa in the optimized welding parameter with welding current of 8.0 kA,welding time of 60 ms,and welding pressure of 0.10 MPa by backward thermo-compensated RBW.The interface structure at the resistance brazed joint with this welding parameter was C/C composites/TiC/Cu(s.s)/T2 copper.The TiC phase was verified at the interface of the brazed joint by Scanning Electron Microscope(SEM),Energy-Dispersive X-ray Spectrometer(EDS),and X-ray Diffraction(XRD).Considerable fractures occurred in the C/C composites and partial fracture occurred at the interfacial reaction layer.

Characteristics of TIG arc-assisted laser welding–brazing joint of aluminum to galvanized steel with preset filler powder

Tungsten inert gas(TIG) arc-assisted laser welding–brazing was used for the butt joining of 5A06 aluminum alloy to the galvanized steel by preset filler powder without groove. The spreading behavior of liquid metal on the back of the galvanized steel at different assisted welding currents was also investigated. The results show that the assisted TIG arc optimizes the interface reaction temperature, enhances the wettability of liquid metal on the steel side, and forms a sound butt joint at an appropriate welding current. A non-uniform intermetallic compound is formed at the interfacial layer, which is composed of Fe2Al5 close to the steel substrate and Fe4Al13 close to the solidified aluminum. The superior tensile strength of joint is indicated when the welding current ranges from 13 to 16 A. The average tensile strength can reach 151 MPa at the welding current of 16 A, and the corresponding fracture belongs to the ductile and brittle hybrid mode.

Effect of Heat Input on Microstructure and Mechanical Properties of Joints Made by Bypass-Current MIG Welding–Brazing of Magnesium Alloy to Galvanized Steel

Experiments were carried out with bypass-current MIG welding–brazing of magnesium alloy to galvanized steel to investigate the effect of heat input on the microstructure and mechanical properties of lap joints. Experimental results indicated that the joint efficiency tended to increase at first and then to reduce with the increase of heat input. The joint efficiency reached its maximum of about 70% when the heat input was 155 J/mm. The metallurgical bonding between magnesium alloy and steel was a thin continuous reaction layer, and the intermetallic compound layer consisted of Mg–Zn and slight Fe–Al phases. It is concluded that bypass-current MIG welding–brazing is a stable welding process, which can be used to achieve defect-free joining of magnesium alloy to steel with good weld appearances.

Strength Prediction of Aluminum–Stainless Steel-Pulsed TIG Welding–Brazing Joints with RSM and ANN

Pulsed TIG welding–brazing process was applied to join aluminum with stainless steel dissimilar metals. Major parameters that affect the joint property significantly were identified as pulsed peak current, base current, pulse on time,and frequency by pre-experiments. A sample was established according to central composite design. Based on the sample,response surface methodology（RSM） and artificial neural networks（ANN） were employed to predict the tensile strength of the joints separately. With RSM, a significant and rational mathematical model was established to predict the joint strength.With ANN, a modified back-propagation algorithm consisting of one input layer with four neurons, one hidden layer with eight neurons, and one output layer with one neuron was trained for predicting the strength. Compared with RSM, average relative prediction error of ANN was ／10% and it obtained more stable and precise results.