Adaptive chirplet transform for sensitive and accurate monitoring of pulsed gas metal arc welding process

Abstract

Condition monitoring of welding processes have received considerable attention in recent years. The method proposed in this paper provides a novel and a better method for analysis of the weld joint strength, i.e., the adaptive chirplet transform. The presence of the nonlinearities in the various sensor outputs of the monitoring systems of the welding procedure demands a more precise signal processing method for a more accurate analysis of the weld joint strength. The adaptive chirplet method has been used here which produced much better results than the other statistical signal processing methods like the wavelet transform technique due to a better time-frequency resolution of the same. In nonlinear feature extraction, wavelet transform technique was first used to detect the weld joint strength using current as a sensor output during the welding. Then the similar procedure was followed using the adaptive chirplet analysis technique which not only showed better differencing capacity between various signals but also provided better time-frequency resolution for the experimental cases where the wavelet method could not predict the weld joint strength correctly. A thorough laboratory study shows that the diagnostic method proposed in this paper is much more accurate, has high sensitivity with respect to faults, and also has better diagnostic resolution.

Monitoring of friction stirred spot weld quality for dissimilar Al6061 to polycarbonate using tool assisted thrust-torque signatures

“…35 The welding induced process errors and surface defects can also be monitored by statistical analyses of these process signals in time domain extended to higher dimension chirplet transform. 42,43 The image analysing techniques have also been applied to scrutinize the weld surface image to discriminate the defected Al 2024 alloy weld. 44 In contrary, numerous analytical, numerical and empirical modelling of FSW process using the interaction effect of input parameters on process outcomes have been developed for the prediction of weld quality characteristics.…”

Stability evaluation of short-circuiting gas metal arc welding based on ensemble empirical mode decomposition

“…Pal et al [25] used neuro wavelet transform to analyze GMAW signals and decompose feature components so as to effectively predict the strength of weld joint. Chatterjee et al [26] introduced the adaptive Chirplet transform to analyze GMAW signals and predict the strength of weld joint. Hilbert-Huang transform (HHT), a new adaptive time-frequency analysis method, can effectively extract the instantaneous frequency of signals due to its high time-frequency resolution and timefrequency clustering.…”

Topographic inspection as a method of weld joint diagnostic

“…Unfortunately, problems arising due to poor joint quality are generally caused by the manufacturers who limit the application of welding. The quality of a welded joint primarily depends on the weld bead shape and weld microstructure influenced by the process parameters [1]. There are also other reasons for the formation of flaws.…”