Keywords
Electric arc signal
Broadband mode decomposition
Welding stability
Submitted : 2024-07-15
Accepted : 2024-07-30
Published : 2024-08-14
Abstract
Welding voltage and current in arc signals are directly related to arc stability and welding quality. Process experiments with different parameters were organized according to the orthogonal experimental design method by constructing an aluminum alloy double-pulse metal inert gas (MIG) welding arc electric signal test platform. The data acquisition system of the aluminum alloy MIG welding process was established to obtain real-time arc signal information reflecting the welding process. The aluminum alloy’s collected double-pulse arc current signals are decomposed adaptively by broadband mode decomposition (BMD). The direct current (DC) signal, pulse signal, distortion signal, ripple signal, and noise signal are separated and extracted, and the composite multiscale fuzzy entropy (CMFE) is calculated for the component set of the electrical signal. The experimental results show that the current waveform obtained by the double-pulse MIG welding current signal is consistent with the corresponding weld forming diagram. Simultaneously, the composite multiscale fuzzy entropy is calculated for the arc characteristic parameters. The rationality of matching process parameters and arc stability of aluminum alloy's double-pulse MIG welding were evaluated.
References
Basheer UM, Noor AFM, Zuhailawati H, et al., 2013, Advances in Friction Welding Process: A Review. Science and Technology of Welding and Joining, 15(7): 534–558.
Kimapong K, Watanabe T, 2005, Effect of Welding Process Parameters on Mechanical Property of FSW Lap Joint between Aluminum Alloy and Steel. Materials Transactions, 46(10): 2211–2217.
Karthikeyan R, Balasubramanian V, 2012, Optimisation and Sensitivity Analysis of Friction Stir Spot-Welding Process Parameters for Joining AA 6061 Aluminum Alloy. International Journal of Manufacturing Research, 7(3): 257–272.
Li Y, Zhao Y, Zhou X, et al., 2021, Effect of Droplet Transition on the Dynamic Behavior of the Keyhole During 6061 Aluminum Alloy Laser-MIG Hybrid Welding. The International Journal of Advanced Manufacturing Technology, 119: 897–909. https://doi.org/10.1007/s00170-021-08270-1
Wang JB, Nishimura H, Katayama S, et al., 2008, Study of Laser-MIG Hybrid Welding of Aluminum Alloy. Preprints of the National Meeting of JWS, 2008: 105. https://doi.org/10.14920/jwstaikai.2008s.0.5.0
Wang Y, Wei B, Guo Y, et al., 2017, Microstructure and Mechanical Properties of the Joint of 6061 Aluminum Alloy by Plasma-MIG Hybrid Welding. China Welding (English Edition), 26(002): 58–64.
Czerwinski RN, Jones DL, 1995, Adaptive Cone-Kernel Time-Frequency Analysis. Signal Processing IEEE Transactions on Signal Processing, 43(7): 1715–1719.
Hou TY, Shi Z, 2011, Adaptive Data Analysis via Sparse Time-Frequency Representation. Advances in Adaptive Data Analysis, 3(1–2): 1–28. http://dx.doi.org/10.1142/S1793536911000647
Wang XK, Gao JH, He YY, 2010, Time-Frequency Analysis Based on Time-Frequency-Adaptive Optimal-Kernel. Systems Engineering and Electronics, 32(1): 22–26.
Enghardt L, Moreau A, Tapken U, et al., 2013, Radial Mode Decomposition in the Outlet of a LP Turbine–Estimation of the Relative Importance of Broadband Noise. Journal of Health Communication, 19(4): 392–412.
Alatas B, 2011, ACROA: Artificial Chemical Reaction Optimization Algorithm for global Optimization. Expert Systems with Applications, 38(10): 13170–13180.
Jin Z, Xiao Y, He D, et al., 2023, Fault Diagnosis of Bearing Based on Refined Piecewise Composite Multivariate Multiscale Fuzzy Entropy. Digital Signal Processing, 2023: 133.
Zheng J, Pan H, Cheng J, 2017, Rolling Bearing Fault Detection and Diagnosis Based on Composite Multiscale Fuzzy Entropy and Ensemble Support Vector Machines. Mechanical Systems and Signal Processing, 85: 746–759.